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The request for comments on the draft report has ended.

We were overwhelmed and delighted with the number of comments on the draft.  On behalf of the whole IEAG, I’d like to thank everyone who responded, under great pressure of time, and to acknowledge the exceptionally high quality, thoughtfulness, constructiveness and usefulness of the comments we received.  We are doing our best to incorporate your wise and well-informed thoughts, and hope that we can produce a report that is useful to us all  in our quest for a data revolution for sustainable development.

The report will be launched on the 6th November.  For information on launch events please contact

Best wishes,

Claire Melamed
Head of Secretariat
On behalf of IEAG

Read the draft report below

A data revolution for sustainable development 

What is the data revolution?

Data are the lifeblood of decision-making, and the raw material for accountability. Without data, we cannot know how many people are born and die; how many men and women still live in poverty; how many children need educating, and how many teachers to train or schools to build; the prevalence and incidence of diseases; if water is polluted or if the fish stocks in the ocean are dangerously low; how many adolescent girls are getting pregnant and what policies are effective in helping them; what companies are trading and whether demand for their product is expanding.

To know what we need to know involves a deliberate and systematic effort of finding out. It means seeking out high quality information that can be compared over time, between and within countries, and continuing to do so, year after year. It means careful planning, spending money on technical expertise, robust systems, and ever changing technologies. It means building public trust in the data, and expanding people’s ability to use it.

Since 2000, the effort involved in monitoring the Millennium Development Goals (MDGs) has spurred increased investment in just these things, to improve data for monitoring and accountablity. As a result, we know much more now about the state of the world and, particularly, the poorest people in it. But despite this significant progress, huge data and knowledge gaps remain about some of the biggest challenges we face, and these gaps limits governments’ ability to act and to communicate honestly with the public. Months into the Ebola outbreak, for example, it was still hard to know how many people had died, or where.

And now the stakes are rising. In 2015, the world will embark on an even more ambitious initiative, a new development agenda underpinned by the Sustainable Development Goals (SDGs). Achieving these goals will require integrated action on social, environmental and economic challenges, with a focus on being inclusive and thus ensuring that no one is left behind. This in turn will require another significant increase in the information that is available to governments, civil society, companies and international organisations to plan, monitor and be held accountable for their actions.

Fortunately, this challenge has come together with a huge opportunity. The volume of data in the world is increasing exponentially: one estimate has it that 90% of the data in the world has been created in the last two years[i]. As the graph below demonstrates, the volume of both existing sources of data (represented in the graph by the number of household surveys conducted) and new sources (represented by mobile subscriptions per 100 people) have been rising, as has the openness of data (illustrated by the number of surveys placed on line). Thanks to new technologies, the volume, level of detail, and speed of data available on societies, the economy and the environment is without precedent. Governments, companies, researchers and citizens groups are in a ferment of experimentation, innovation and adaptation to the new world of data. This is the data revolution.

growth in data

1.2    What is this report about and who is it for?

Revolutions do not begin with reports, and the data revolution is no different. This report is not about how to create a data revolution – it is already happening – but how to mobilise it for sustainable development. It is an urgent call for action now to support the aspiration for sustainable development and avert major social and environmental disasters, to stop and reverse growing information inequalities, and to ensure that the promise of the data revolution is realised for all.

In this first section we first describe what the data revolution is, and the opportunities and pitfalls it presents. The second section highlights the current state of data, and the kind of world we could see if the promise of the revolution is realised. Finally, the third section provides a “vision” of a possible world of data in 2030, and some recommendations for how to achieve it.

This report has been prepared in response to a request by the Secretary-General of the United Nations. We hope it will also be helpful to Member States, the UN System as a whole, and to the large constituencies that support the three pillars of the UN: peace, human rights and development.

We believe that governments, and governments acting together through the UN, have a crucial role to play. This report offers a way forward, framing the scope of the actions to be undertaken, showing how resources, actors, forms of collaboration and institutions can evolve, best be managed and deployed to make the data revolution a force for progress and for enhancing possibilities. It is about seizing the opportunity of the post-2015 development agenda and using the data revolution not only to monitor progress towards SDGs , but also to accelerate their achievement.

A revolution for what?

This is the revolution

As part of a project to engage young people in disaster risk reduction, teenagers in Rio de Janeiro have used cameras attached to kites to gather aerial images, helping to identify the presence or absence of drainage systems, the availability of sanitation facilities, and impediments to evacuation. In Rio, this has already led to the removal of piled-up garbage and the repair of a bridge. UNICEF[ii]

The data revolution is a revolution of possibilities – of new technologies, data production and dissemination systems and new resources opening up to produce more and better data, as well as expanding what can be done with data. First and foremost, if offers the possibility of increased knowledge through data integration – the deliberate putting together of traditional and new data in ways which illuminate as yet unquantified aspects of human and planetary behaviour and allow for a more timely, nuanced, responsive and effective type of decision making.

This involves new sources of data – satellite imagery, social media or anonymous mobile phone records, or data created and willingly shared by citizens to monitor and reflect their own circumstances and priorities. It involves the quantification of what was previously considered qualitative data – for instance, defining proxies for the measurement of happiness or the fulfilment of human rights. Bringing together established and new sources in the service of sustainable development can shed new light on old problems, reveal new possibilities for action, identify what remains to be done and provide the real time monitoring that allow policies to be adapted for maximum effect. To fulfil this promise, it must be done in a way that adheres to the highest standards of honesty, respect of privacy, rigour and impartiality that have been developed over decades and centuries of academic research, statistical practice and political negotiation.

It is also a revolution of expectations – of people demanding that these changes and innovations be used to enhance their control over their own lives and the decisions that affect them. Data is the bedrock of accountability. More information opens up the possibility for an honest, informed dialogue between service providers and beneficiaries, between tax payers and governments who spend tax revenues, between companies and employees and between the private sector, governments and civil society. Data is the basis for social compacts and ultimately this contributes to improving the responsiveness, efficiency and effectiveness of institutions, and, eventually, the overall welfare of citizens.

But the data revolution comes with a range of new risks, posing questions and difficult challenges concerning the rights to access and use data. Fundamental issues of human rights: privacy, respect for minorities or data sovereignty requires us to balance the rights of individuals with the benefits of the collective. As more is known about people and the environment, so there is a correspondingly greater risk that the information could be used to harm, rather than to help. They could be harmed deliberately, if the huge amount that can be known about people’s movements, their likes and dislikes, their social interactions and relationships is used with malicious intent, such as discriminating in access to services. Or they could be harmed inadvertently, if information that has not been checked for quality or standardised in accepted ways is used for policy or decision making and turns out to be wrong.

Without deliberate actions, the opportunities will be slower in coming and more unequally distributed when they arrive, and the risks will be greater.

The data revolution has huge potential to serve the sustainable development agenda: to empower citizens and to provide governments, companies and civil society with the information they need to achieve the SDGs. But at the moment it is only potential. Without fast and decisive action to shape this new world of data for the public good, not only will that potential not be realised, but inequalities will widen and citizens could see their rights eroded.

Change is happening. The more slowly governments, companies, non-governmental organisations and individuals respond to the revolution out there, the harder and more expensive it will be to catch up. Those with access to data and information will have more power in the new world than those who do not. The public sector is falling far behind the private sector in how they generate and use data to make decisions and monitor their impact. A lack of oversight and regulation in many places has created an information free-for-all, where information is available to track and monitor individual actions, but citizens have little democratic control over how it is used, stored and shared, and where the creators of data are under no obligation to share it, or to present it in ways that serve the public good. At the same time, privacy regulations elsewhere are growing ever more restrictive in response to concerns over misuse of data without creating space for innovation in reusing data in ways that promote accountability and serve the public good. Value creation and appropriation emanating from information is not always managed for the good of the public and people and governments in richer countries have more possibilities than those in poorer countries to get access to, knowledge of, and capacity to use and benefit from the new information opportunities that the data revolution has created.

Above all, this should be a revolution for equity in access and use data.

Major gaps are already opening up between the information haves and have-nots. Without action, a whole new inequality frontier will open up, splitting the world between those who know, and those who do not.

Many people are excluded from the new world of information by language, poverty, lack of education, remoteness or prejudice and discrimination. While the use of new technologies has exploded everywhere in the last ten years, the costs are still prohibitive for many. In Nicaragua, Bolivia and Honduras, for example, the cost of a mobile broadband subscription exceeds 10% of average monthly GDP per capita, compared to France and Korea where it is less than 0.1%[iii]. The information society should not force a choice between food and knowledge.

In several countries, the public sector is not keeping up with companies which are increasingly able to collect, analyse and respond to real-time information as quickly as it is generated. Although it is getting faster, in many countries the reporting of GDP figures is still months behind real activity, while companies are able to monitor their output, employment, and sales in real time.

Richer countries are benefitting more from the new possibilities than poorer countries that lack the resources for investment, training and experimentation. According to McKinsey, African countries spend about 1.1% of GDP on investment in and use of internet services, less than a third of what, on average, is spent by richer countries – meaning that the gap in internet availability and use is growing every year, as some regions accelerate ahead[iv]. The graph below shows how advanced economies are ahead of the rest of the world on every indicator of access to, use of, and impact of the use of digital technologies.

If our vision is of a world where information reduces rather than increases inequalities, we are still a long way from realising that ambition.


Inequalities in Access to and Use of ICT Services

If there are gaps in the mobilisation of new opportunities for the public interest, it is up to governments to fill those gaps, working together in the multilateral system, at regional and global levels. It is governments ideally working in collaboration with forward looking and socially responsible private institutions, who can set legal frameworks to guarantee data privacy and security of data for individuals, and ensure its quality and independence. It is governments who can balance public and private interests and create systems that foster incentives without creating unacceptable inequalities, adopt frameworks for safe and responsible use and manage the international system that can transfer finance and technical expertise to bring the least informed people and institutions up to the level of the most informed. And it is governments who are elected to respond to citizens on their choices and priorities.

New institutions, new actors, new ideas and new partnerships are needed, and all have something to offer the revolution. But national statistical offices, the traditional guardians of public data for the public good, will remain central to government efforts to harness the data revolution for sustainable development. To fill this role, however, they will need to change, and more quickly than in the past, and continue to adapt, abandoning expensive and cumbersome production processes, incorporating new data sources, and focusing on providing data that is human and machine-readable, compatible with geospatial information systems and available quickly enough to ensure that the data cycle matches the decision cycle. In many cases, technical and financial investments will be needed to enable those changes to happen.

This is the revolution

The GEO Global Agricultural Monitoring initiative uses weather and satellite data, data from regional bodies and from national governments to provide monthly reports on the growing conditions for four major crops (maize, rice, soybeans and wheat) that between them account for 70% of the calories consumed by humans worldwide. Within only a few years, daily satellite imagery will be available[v].

New data, health services and malaria

Malaria is one of the biggest killers in several developing countries and imposes a huge strain on health systems. Using new data sources to inform planning and policy can improve services and reduce deaths.

The Mtrac programme in Uganda, supported by UNICEF and the WHO, uses SMS surveys completed by health workers to alert public health officials to outbreaks of malaria, and lets them know how much medicine is on hand at health facilities, so they can anticipate and resolve any shortages.

Before Mtrac, the Ministry of Health had very little health facility-level data, either paper or electronic. By March 2014, that’s to this programme, 1,203 district health officials, 18,690 health facility workers, and 7,381 village health team workers were using the system. Now the Ugandan government is collecting data from thousands of health facilities, capturing and analysing results within 48 hours at a total cost of less than US$150 per poll. The Anonymous Hotline receives an average of 362 actionable reports per month, and approximately 70% of these reports are successfully followed up at the district level within 2 weeks. The number of facilities that were out of stock of Artemisinin-based Combination Therapies (ACTs) to treat malaria at any given time had fallen from 80% to 15%.

In the longer term, new sources of data might also have a role in tracking and predicting epidemics of malaria or other diseases. Combining strongly anonymized data from the Orange mobile telephone network on movement patterns and their geographic relationships within population with official information from the WHO on the spread of malaria, the University of Minnesota School of Public Health produced epidemiological models that are more detailed than any currently in use. This knowledge could be used to create services to notify doctors, field hospitals and the general public ahead of epidemics, using mobile networks or local radio. Similar work has been done on the spread of AIDS, cholera and meningitis, and could be used for rapid response and planning for new epidemics.

Source for graphics: de Cordes, Nicolas, The use of big data for development goals, DEF Yearbook 2014 – “Social Networks and Social Machines, Surveillance and Empowerment, to appear in November 2014 (


The Data Revolution for Sustainable Development

In September 2015, the UN Member States will commit to an ambitious new set of global goals for a new era of sustainable development. Achieving this will require an unprecedented joint effort on the part of governments, civil society, the private sector and millions of individual choices and actions. To be realised, the SDGs will require a monitoring and accountability framework and a plan for implementation. A commitment to realise the opportunities of the data revolution should be firmly embedded into the action plan for the SDGs, to support those countries most in need of resources, and to set the world on track for an unprecedented push towards a new world of data for change.

Bringing together the demand for new data to inform the SDGs with the growing expectations about monitoring and accountability, and the potential provided by new technologies and ways of doing things in a responsible and privacy respecting approach, could provide the world with the step change that mobilises the data revolution on the right course and transforms societies for the better. First and foremost, the data revolution can and must be leveraged to provide more and better data, information and knowledge to more people, more often. Too many governments still lack information about crucial aspects of sustainable development. Too many people are still uncounted. Too much data is out of date, unreliable or simply not available. Too many people are not able to access and use the data they need to make informed decisions and hold others to account.

There is much to be done, and this is the moment to do it.

The state of data

Although there have been steady and dramatic improvements in recent decades, there is still work to do to create a clearer and more up to date picture of the world, to use in planning, monitoring and evaluation of the policies and programmes that will together achieve the SDGs, and in holding to account those in positions of power over resources and other decisions that affect people’s lives.

There are two main problems to address:

  • Not enough good quality data. In a world increasingly awash with information, it is shocking how little we know about some people and some parts of our environment.

Despite huge improvements over the last decade, the experience from the current MDG monitoring process highlights the challenges ahead. The world has made huge strides in tracking specific aspects of human development such as poverty, nutrition, child an maternal health and access to water and sanitation. However, too many countries still have poor data, data arrives too late and too many issues are still barely covered by existing data. Data on employment, for example, is notoriously unreliable. A great deal of data is difficult to access to common citizens or is not available until several years have passed since the time of collection period.

Screen Shot 2014-10-24 at 9.33.42 AM

The figure above presents a summary snapshot of current data availability in the MDG database (as of October 2014), covering 55 core indicators for 157 developing countries or areas. There, a country is counted as having data for an indicator if it has at least one observation over the reference period, and availability is broken down by whether the data comes from country or international data sources, and whether it is estimated, adjusted or modelled. [1]. Overall, the picture is improving though still poor. There is no three year period when the availability of data is more than 70% of what is required. The drop in data availability after 2010 demonstrates the extent of the time lags that persist between collection and release of data. Of course, there is considerable variation in data availability between indicators, where, for example, data on malaria indicators is very scarce but there is relatively good country level data available for most countries and years for the ratio of girls to boys enrolled in primary, secondary and tertiary education.

While the first graph shows that MDG data availability is still low for some individual indicators and/or countries, the graph below highlights how, when looked at from a country level, there has been a tremendous improvement in the ability of national statistical systems to provide data directly over the past ten years (see Figure YY). This has been one of the greatest achievements of MDG monitoring, and is testament to the tremendous efforts of many national and international organisations.

Beyond the MDG indicators

Beyond the MDG indicators, other huge and disturbing gaps exist. Entire groups of people and key issues remain invisible. Indigenous populations, for instance, are consistently left out of most data sets. It is still impossible to know for sure how many disabled children are in school. Globally, the birth of nearly 230 million children under age five has never been recorded. In 2012 alone, 57 million infants – four out of every ten babies delivered worldwide that year – were not registered with civil authorities[vi].

Policy makers at national levels and below often have very little disaggregated data that allows them to compare progress one district with another and make planning decisions. In water supply, for example, many household surveys produce a single national estimate of access to clean and safe water in rural areas, but don’t show how it varies between districts.

Discrimination against women and undervaluing of their activities and priorities in every sphere has been replicated in the statistical record. Many of the issues of most concern to women are poorly served by existing data:

  • only just over half of all countries report data on intimate partner violence, and where it is reported quality is not consistent and data are not comparable;
  • data on informal employment particularly in agriculture are not accurately measured, despite women’s concentration in this sector. And much more data are needed on the economic roles of women as caregivers to children, the elderly and the disabled in the household and in labour force, and young women’s transition from school to work;
  • some estimates show that 80% of displaced persons and refugees are women and children, but we lack accurate data, so cannot know for certain and cannot design assistance programmes for refugees accordingly[vii].

Better data sources, and better usage and linking of existing sources such as administrative and survey data, can help to overcome these inequalities in who is counted and what is known about them, but decades of experience in every sector has taught us that the systematic inequalities that hide groups from view will not be overcome without deliberate action to measure, monitor and report on the progressive elimination of inequalities. This should be a core purpose of the data revolution.

Finally, it is quite clear that the monitoring of the SDGs will require substantial additional investment order to consolidate gains made during the MDG era and to develop reliable, high quality data on a range of new subject matters, such as climate risk mitigation or inequality, and with an unprecedented level of detail.

This is the revolution

Pro-active disclosure of information was designed into India’s NREGA program, which guarantees 100 days employment per year to people in rural communities. Through regular social audits, people can verify the official records and identify discrepancies[viii]. In some cases, corruption has been identified and stopped; in many more, it has been prevented from occurring in the first place.

  • Data that is not used or not usable. To be useful, data must be of high quality and must be made accessible to those who want or need to use it. Comparability and standardisation are crucial, as they allow data from different sources or time periods to be combined, and the more data can be combined, the more useful it is. Combining data allows for changes of scale – aggregating data from different countries to produce regional or global figures. It allows for comparison over time, if data on the same thing collected at different moments can be brought together to reveal trends. Too much data is still produced using different standards – household surveys that ask slightly different questions or geo-spatial data that uses different geographical definitions. And too little data is available at a level of disaggregation that is appropriate to policy makers trying to make decisions about national level allocation or monitoring equitable outcomes. This prevents researchers, policy makers, companies or NGOs from realising the full value of the data produced.

It’s not only about standards. Access is often restricted behind technical and/or legal barriers that prevent or limit effective use of data. Data buried in pdf documents, for example, is much harder for potential users to work with, though it represents an improvement on data that is only accessible to a small pool of well-connected statisticians and policy makers; administrative data that are not transferred to statistical offices; data generated by the private sector or by academic researchers that are never released or data released too late to be useful; data that cannot be translated into action because of lack of operational tools to leverage it. This is a huge loss in terms of the benefits that could be gained from more open data and from linking data across different sectors.

Household Surveys and the Data Revolution

Household survey data can be of enormous value in identifying patterns of progress among different groups and using this to inform policy. For example, the Indian government’s Total Sanitation Campaign, launched in 1999, has a budget of $3.9billion to improve access to sanitation in the country.  However, data from household surveys showed that between 1995 and 2008, the the outcomes were far from satisfactory.  In this period, the percentage of households from the poorest 20% of Indian society practicing open defecation fell from 99% to 95%, while among the second-richest quintile it fell from 56% to 20%.  Analysis of household data by UNICEF and others has helped to inform the government’s efforts to improve the targeting of subsidies, in the hope of reaching a larger number of the poorest people.

As a source of data to enhance our understanding of human development and to guide policy, household surveys, especially if augmented by data from less traditional sources, can play a big role in monitoring SDGs and contribute to and benefit from innovations in methods and technologies used to collect, manage, and disseminate micro data.

Public access is becoming the norm for internationally sponsored household surveys, which makes hundreds of micro data sets freely accessible. The International Household Survey Network (IHSN) and the Accelerated Data Programme (ADP) have made great contributions in this regard. The IHSN’s Central Survey Catalogue currently holds 4,224 surveys. The ADP has helped to make the data more open, with 45% of the documented surveys accessible along with 62 national catalogues now available online[ix].

The data we want for sustainable development

Too much that could be known remains unknown. Data could be used better to improve lives and increase the power and control that citizens have over their destinies. A cultural shift is required, towards a world where data is no longer the preserve of statisticians or computer programmers, but where everyone values, feels entitled to, and is confident navigating the growing ocean of data that surrounds us every day, and uses it in ways we cannot now imagine. Data is a new natural resource, an endless source of fuel for innovation that will power sustainable development, of which we must learn to become effective and responsible stewards.

The world we need, if the data we have is to be used to the fullest to achieve sustainable development, is a world of data that is transformed in the following ways:

  • Data for everyone. The rules, systems and investments that underpin how official data is collected and managed need to be focused on the needs of people, while protecting their rights. These data should reflect what is important to people and the constraints and opportunities that affect their lives. The new platforms, processes and standards that disseminate data must start with what people need, not what institutions want to deliver. Rules and standards should be aimed at reducing information inequalities and providing the maximum high quality information for the widest possible number of people. The priority should always be to use data to improve outcomes, experiences and possibilities for people in the short or long term. And data that is for people needs to be collected, stored and used in ways that protect their interests: with respect for privacy, but also with a presumption of openness, so that people themselves have access to the information and are able to make choices accordingly.
  • Data for now. If data is to be useful and support good decision making, it has to be ready at the time when decisions are being made or where the opportunity for influencing the outcomes is there. Real time implies having feedback data before a decision becomes irreversible. New technologies and innovations provide the opportunity for the public sector, citizens groups, individuals and companies to have access to data that is aligned with their own decision making cycles and information needs – available when and how they want it – and strengthen policy planning, crisis early warning, programme operations, service delivery, impact evaluation, and disaster response. Many companies and governments are already adapting to faster and more diverse data. Around the world, satellites are used rather than survey data to monitor crop conditions, many countries use web-scraping to collect information on prices; in other cases, rather than produce their own estimates of house prices, statistical offices draw on existing private sector indices quickly available, having checked them for quality.
  • Data for the future. Data is a key resource not just for decision making now but for future modelling and problem solving. We cannot precisely predict future needs, or know how current data could be reused in the service of complex and interconnected problems as yet unknown or unsolved. Data at different timescales will be most useful for solving future problems if they are part of a flexible and connected system, not tied to one project or research question. Data that can be reused at different scales, and combined with other data, can better reflect the complex and dynamic interactions between people and the planet. We need to begin investing in data today as a shared resource that will enable the innovations required to meet the challenges of tomorrow.

This is the revolution

RapidFTR (Rapid Family Tracing and Reunification) is an open source mobile application used to collect vital information about children who have been separated from their families in disaster situations, and share it securely on a central database for family members looking for a missing child. RapidFTR uses the same type of security as mobile banking to ensure that family-tracing information, especially photos, is accessible only by authorized users to protect these vulnerable children. In Nyakabande transit centre in Uganda, and Rwamwanja refugee settlement camp in South Sudan, RapidFTR reduced the time required for information to become available from more than six weeks to a matter of hours, speeding up the process of family reunification[x]. UNICEF

The value of better and more open data

As well as being important in its own right for accountability purposes, through its impact on policy and behaviour better and more open data can save money and create economic, social and environmental value.  Although research in this area is still limited, modelling exercises and evidence from actual examples illustrates the scale of the potential impact of better and more open data on the economy.

  • A report produced by accountancy firm Deloitte for the UK’s Department for Business, Innovation and Skills estimates the economic value of the information held by the public sector in the UK and released for use and re-use to be around £5 billion per year. This includes £400 million per year as the value of lives saved from reduced death rates among cardiac patients, and time savings worth between £15-58 million from the use of real-time transport data and consequent adjustments in behaviour[xi].
  • A report from McKinsey Global Institute puts the global value of better and more open data at $3 trillion per year[xii].
  • The U-report social monitoring platform established by UNICEF in Uganda has more than 260,000 young people reporting on issues that affect their communities. Early reporting of an infectious disease in banana production contributed to halting the spread of the disease, which could have cost the country $360 million per year if left unchecked[xiii].
  • Using mobile phone records to track the link between employee interactions and productivity, a small change in the schedule of coffee breaks at a Bank of America call centre, so that employees took their breaks together to encourage more interactions was found to increase productivity by $15 million a year[xiv].

What might this mean for the different institutions involved in this area? By 2020, we hope to be witnessing the emergence of a vibrant “global data ecosystem” in which:

  • Governments and other public institutions empower statistical offices, protecting their independence, to take on the needed changes to respond to the data revolution and put in place regulatory frameworks that ensure robust data privacy and data protection, promote sharing of data by public sector, private sector, citizens groups and individuals, and build capacity for continuous data innovation in which promising new sources of data, analytical approaches and technologies tools are rapidly identified, evaluated, and mainstreamed into programme planning, operations, and evaluation.
  • Governments, international and regional institutions and donors invest in data, to provide resources to countries and regions where statistical or technical capacity is weak, to develop infrastructures and implement standards to continuously improve and maintain data quality and usability, and to keep data open and usable by all. They also finance analytical research in forward-looking and experimental subjects.
  • International and regional organisations provide platforms for inter-governmental dialogue and multi-stakeholder partnerships at global and regional levels to set common standards for data production, anonymisation, sharing and use to ensure that new information flows are safely transformed into global public goods, and maintain a system of quality control and audit to ensure that quality, independence, openness and privacy are respected in all systems and by all data producers and users. They also support countries in their capacity building efforts.
  • Statistical systems are empowered, resourced and independent to quickly adapt to the new world of data to produce, process, disseminate and use high quality, open, disaggregated and geo-coded data. They might change drastically, becoming less about producing data and more about managing and curating information created outside of their organisation. They operate at a level that allows for exploitation of the possibilities of ever changing technologies and new relationships.
  • All public, private and civil society data producers share data according to globally, nationally or regionally brokered agreements and norms, and publish data, geospatial information and statistics in open formats and with open terms of use, following global common principles and technical standards, to maintain quality and openness and protect privacy. Governments and parliaments make law and legal information available to all on a timely basis, freely, and in open standards for re-use.
  • Governments, civil society, academia and the philanthropic sector work together to strengthen the data and statistical literacy (“numeracy”) of citizens, the media, and other ‘infomediaries’, ensuring that all people have capacity to evaluate the quality of data and use them for their own decisions, as well as to fully participate in initiatives to foster citizenship in the information age.
  • The private sector report on their activities using common global standards for integrating data on their economic, environmental and social activities and impacts. Some companies also cooperate with the public sector, according to agreed and sustainable business models, to the production of statistical data for SDGs monitoring and other public purposes.
  • Civil society organisations hold governments and companies accountable using evidence on the impact of their actions, provide feedback to data producers, develop data literacy and help communities and individuals to generate and use data, to ensure accountability and make better decisions for themselves,
  • The media fairly report on the statistical and scientific evidence available on relevant dimensions of sustainable development and foster an evidence-based public discourse using advanced visualisation technologies to better communicate key data to people,
  • Academics and scientists carry out analyses based on data coming from disparate sources providing long-term perspectives, knowledge, and data resources to guide sustainable development at global, regional, national, and local scales; make scientific data as open as possible for public and private use in sustainable development; provide feedback and independent advice and expertise to support accountability and more effective decision making, and provide leadership in education, outreach, and capacity building efforts.

Progress toward Universal Civil Registration and Vital Statistics

Millions of people in low- and middle-income countries are denied basic services and protection of their rights because of deficient CRVS and national identification systems. Lacking records of their birth and civil status, they are excluded from health coverage, schooling, social protection programs, and humanitarian response in emergencies and conflicts.

A well-functioning CRVS system is also vital for policy making and for monitoring, generating statistics for policy formulation, planning and implementation, and monitoring of population dynamics and health indicators on a continuous basis at the national and local level. These data help to identify inequalities in access to services and differences in outcomes. They also improve the quality of other statistics, such as household surveys, that depend on accurate demographic benchmarks. One proven solution is through issuance of Digital Identity, which gives government and business the ability to deliver citizen services electronically, boosting efficiency and driving innovation and serving people often in isolated areas.

Despite progress in recent years, many countries still lack the capacity, infrastructure, and resources to implement well-functioning CRVS systems. Today only one country in Africa has a complete CRVS system[xv].

Civil registration and national identification requires collaboration among several government ministries and technical know-how and innovative use of technologies for registering, archiving, and data management. Most of all it requires leadership, political support, and resources. In poor countries, building or extending CRVS systems will also requires the international community and donors to work together on a global program of support.

Some good news: International partners and countries have recently agreed on a CRVS Scaling Up Investment Plan[xvi]. The plan covers activities over a 10-year period from 2015 to 2024, with the goal of universal civil registration of births, deaths, marriages, and other vital events, including cause of death, and access to legal proof of registration for all individuals by 2030. Africa and Asia have already established regional programs to motivate political support, systematic national planning, and provision of technical assistance. And key donors[xvii] announced recently the establishment of a trust fund to support developing countries’ plans to establish CRVS systems with the aim of accelerating progress toward the health-related Sustainable Development Goals.

Mobilising the data revolution for sustainable development: a call to action

A revolution is an idea – an inspiring vision of a world of fast-flowing and free-floating information, deployed for the public good, and of citizens and governments buzzing with the excitement and possibilities it creates. But it is also a practical proposition. Getting from here to there involves deliberate actions and choices.

Mobilising the data revolution for sustainable development and ending information inequalities is a long and complex endeavour. The main objective is to enable data to play its full role in the realisation of sustainable development by closing key gaps: between developed and developing countries, between information-rich and information-poor people, and between the private and public sectors.

Decisive action now, taking advantage of the political opportunities this and next year, can set the scene and have a positive impact for years to come. We urge the UN Member States and system organisations to dramatically speed up their work in this field, using the post-2015 development agenda as a strong and universal policy driver to achieve significant results by 2020.

Our recommendations suggest a comprehensive programme of action in four areas, illustrated below: principles and standards, technology, innovation and analysis, capacity and resources, and leadership and governance. At the heart of the recommendations in every area are people and the planet – our revolution is with them and for them.

Flower - with planet people

Principles and standards

One of the key roles of the UN and other international or regional organisations is setting principles and standards to guide collective actions within a global community and according to common norms. We believe that mobilising the data revolution for achieving sustainable development urgently requires such a standard setting, building on existing initiatives in various domains.

We recommend …

that the UN develop a comprehensive strategy and a roadmap towards a new ‘Global Consensus on Data’, setting principles and agreeing standards to build trust and enable cooperation, including:

  • Agree on and promote adoption of specific principles related to the data revolution (drawing from and building upon those described in table 1, to be further developed by the appropriate UN body and agreed by their Member States);
  • Accelerate the development and adoption of legal, technical,geospatial and statistical standards, in a range of areas including but not limited to:
    • Standards to facilitate openness and information exchange, including: statistical data and metadata exchange, technical standards for fostering the interoperability of information systems, standards for geospatial information and “geographic semantic” management and exchange; standards for open data and digital rights management and licensing
    • Standards to protect human rights including: standards for anonymizing data that is personally identifiable, standards and enforcement mechanisms for data security, integrity, documentation, preservation, and access

Basic Principles for Data Revolution for Sustainable Development

Bearing in mind that:

  • The data revolution is critically important for sustainable development in at least four ways: it allows systematic tracking of progress on the globally agreed goals; it allows development organizations to adopt more effective and efficient ways of working; it democratizes and broadens the involvement of institutions and individuals who can access and use data and provide feedback; and it opens up unprecedented new opportunities to understand the factors that drive human behavior and design informed responses;
  • The data revolution consists of the entire data ecosystem – national statistical agencies, mapping authorities, government administrative data collection systems, academia, independent think tanks, researchers, civil society, private sector, media and individuals. The role and freedom to act of each of these actors should be recognized and protected. A healthy data ecosystem is typically characterized by strong complementarities and robust engagement and free debate among these actors;
  • While the data revolution is already happening, it would be incorrect to assume its effects will be inevitably positive. There are many areas where the data revolution intersects with human rights. Left alone, it is likely to reinforce existing inequities and patterns of marginalization;

For it to be useful, the data revolution will need to be harnessed for sustained and inclusive development through proactive measures and guided by the following key principles:

1. Data quality and integrity

Poor quality data can mislead. The entire process of data design, collection, analysis and dissemination needs to be demonstrably of high quality and integrity. Clear standards need to be developed to safeguard quality, drawing on the U.N. Fundamental Principles of Official Statistics and the work of independent third parties. A robust framework for quality assurance is required, particularly for official data. This includes internal systems as well as periodic audits by professional and independent third parties. Existing tools for improving the quality of statistical data should be used and strengthened, and data should be classified using commonly agreed criteria and quality benchmarks.

2. Data disaggregation

To the extent possible and with due safeguards for individual privacy and data quality, data should be disaggregated across many dimensions, such as geography, wealth, sex and age. Disaggregated data can provide a better comparative picture of what works, and help inform and promote evidence based policy making.

3. Data timeliness

Data delayed is data denied. Standards should be tightened and technology leveraged to reduce the time between data collection design and publication. The value of data produced can be enhanced by ensuring there is a steady flow of high quality and timely data from national, international, private big data sources, and digital data generated by people. The data cycle must match the decision cycle.

4. Data transparency

Many publicly-funded datasets, as well as data on public spending, are not available to other public ministries or to the general public. All data on public matters and/or funded by public funds (with narrow exemptions for genuine security or privacy concerns) should be made public and be should be “open by default”. The underlying data design and sampling, methods, tools and datasets should be explained and published alongside findings to enable greater scrutiny, understanding and independent analysis.

5. Data openness

Open data is also about the way in which data is made public. It needs to be both technically open (i.e. available in a machine-readable standard format so that it can be retrieved and meaningfully processed by a computer application) and legally open (i.e. explicitly licensed in a way that permits commercial and non-commercial use and re-use without restrictions). Data should be made public in ways that encourage greater use. In particular, published data should be complete, machine-readable, freely available for re-use without restrictions, and transparent about underlying assumptions.

6. Data usability and curation

Too often data is presented in ways that cannot be understood by most people. Data architecture should therefore place great emphasis on user centred design and user friendly interfaces. Communities of “information intermediaries” should be fostered to develop new tools that can translate raw data into something meaningful to a broader constituency of non-technical potential users and enable citizens and other data users to provide feedback.

7. Data protection and privacy

As more data becomes available in disaggregated forms and data-silos become more integrated, privacy issues are increasingly a concern about what data is collected and how it is used. Further risk arises where collectors of big data do not have sufficient protection from the demands of such information from State bodies or ill meaning hackers. Clear international norms and robust national policy and legal frameworks need to be developed that regulate Opt-in and Opt-out, data mining, use, re-use for other purpose, transfer and dissemination. They should enable citizens to better understand and control their own data, and protect data producers from demands from governments and attacks by hackers, while still allowing for rich innovation in reuse of data for the public good. Within privacy constraints, people’s rights to freedom of expression using data should be protected – people who correctly provide, collect, curate and analyse data need freedom to operate and protection from recrimination.

8. Data governance and independence

Many national statistical offices lack sufficient capacity and funding, and remain vulnerable to political and interest group influence (including by donors). Data quality should be protected and improved by strengthening NSOs, and ensuring they are functionally autonomous, independent of sector ministries and political influence. This can include independent monitoring of the same public services, for example, or monitoring of related indicators such as public satisfaction with services.

9. Data resources and capacity

There is a global responsibility to ensure that all countries have an effective national statistical system, capable of producing high quality statistics in line with global standards and expectations.  This requires investments in human capital, new technology, infrastructure, geospatial data and management systems in both governmental and independent systems, as well as information intermediaries. At the same time, national capacity for data science must be developed to leverage opportunities in big data. Increased domestic resources and international support for developing countries are needed to have the data revolution contribute to sustainable development. Applications of big data for the public good must be developed and scaled up transparently, demonstrating full compliance with applicable laws.

10. Data rights

Human rights cut across many issues related to the data revolution. These rights include but are not limited to the right to be counted/right to an identity, the right to privacy/ ownership, the right to due process (for example in how data is used to make decisions about people), freedom of expression, the right to participation, the right to non-discrimination and equality, and principles of consent. Any legal or regulatory mechanisms, or networks or partnerships, set up to mobilise the data revolution for sustainable development should have the protection of human rights as a core part of their activities.

Technology, innovation and analysis

Technology has been and will continue to be the fundamental driver of the data revolution. To harness the benefits of new technology, large and continuing investments in innovation are required at all levels, but especially in those institutions which are currently lagging behind. In addition, but beyond the scope of this report, and urgent effort needs to be made to increase access to information technologies by, among other things, increasing access to broadband, increasing literacy, including adult literacy, and increasing the use of ICT in schools worldwide.

We recommend

… that the UN foster the establishment of a “Network of Data Innovation Networks (NINE)” for sustainable development bringing together a range of partners to generate knowledge and solve common problems. The networks should involve existing institutions active in this field and new experts, academic and research institutions, as well the private sector and official bodies. Some specific areas of activity could be:

  • Contribute to the adoption of best practices for improving the monitoring of SDGs, through systematic research and mapping of how emerging data sources can be used for measuring and fostering sustainable development. This could particularly focus on linking innovators with national statistical offices to improve their effectiveness and the practical uptake of innovations. Information sharing could be facilitated through the production of an annual round up of “Technologies and Methodologies for Data Innovation”.
  • Identify areas where the development of common infrastructures to exploit the data revolution for sustainable development could solve capacity problems, produce efficiencies and encourage collaborations. One such suggestion would be a “world statistics cloud”, to store data and metadata produced by different institutions but according to common standards, rules and specifications.
  • Identify critical research gaps, such as an investigation of the relationships between data, incentives and behaviour to enable the creation of data dissemination policies that will support rational responses by citizens.
  • Engage research centres and innovators in the development of publicly available data analytics tools to better evaluate long-term trends affecting sustainable development, identify the most effective policies for achieving it, to make better decisions at all level and support improved organizational planning, operations and evaluations.
  • Create incentives, for example through prizes or data challenges, to engage social entrepreneurs, private sector, academia, media, civil society and other institutions in this global effort.

Capacity and Resources

Strengthening national capacities from data production to use will be the essential test of any data revolution, in particular in developing countries where the basic infrastructure is often lacking – preventing them from scaling-up activities and innovating to fill data gaps, improving quality, or investing in open data systems. The requirements involved in monitoring progress towards the agreed goals and targets will not be met in many countries without significant new investment. A cultural change is needed, to explicitly recognize data as a development issue just like infrastructure, health, or other key components of modern economies and societies

We recommend …

A proposal should be developed for a new funding stream and innovative financing mechanisms to support the data revolution for sustainable development, for discussion at the “Financing for Development Summit”, which will take place in Addis Ababa in July 2015. The proposal should be built on the five following pillars:

  • An analysis of the scale of investments needed for the establishment of a modern system to monitor progress towards SDGs, especially in developing countries. This analysis, building on various attempts currently ongoing, should highlight the costs as well as opportunities for efficiency gains associated with different production systems.
  • A proposal on how to manage and monitor new funding for the data revolution for sustainable development. Taking stock of existing sources and forms of funding, this should look at how funding from a range of sources could be managed and disbursed to incentivise innovation, collaboration and whole systems approaches, while also encouraging creativity and experimentation and accepting that not all initiatives will succeed. It also should take into account the limited financial and technical absorption capacity of low income countries which in the past has hampered the up-take of new funds.
  • A proposal on how to leverage the resources and creativity of the private sector. This could include the exploration of areas where a private-public partnership could be set-up to develop economically sustainable “data markets” while ensuring quality public service. Incentives for the private sector to invest with the time horizon and returns they operate with should be studied.
  • A proposal to improve existing arrangements for fostering the necessary capacity development and technology transfer. This should inclued upgrading the ‘National Strategies for the Development of Statistics’ (NSDS) to do better at coordinated and long-term planning, and identify sound investments; engaging non-official data producers in a cooperative effort to speed up the production, dissemination and use of data, and training a new generation of leaders (especially in national statistical systems) for the new world of data.
  • A proposal for a special investment to increase global data literacy. To close the gap between people able to benefit from data and those who cannot, in 2015 the UN should work with other organisations to develop a massive education program and promote new learning approaches to improve people’s, infomediaries’ and public servants’ data literacy. Recommendations could also be developed for running secondary and tertiary education programmes to train statisticians and data scientists around the world, especially in developing countries where the need is greatest.

Governance and leadership

Strong leadership by the UN is vital to make the data revolution serve sustainable development. Such leadership should be made extremely concrete through various actions and activities, and the continuous engagement of all relevant partners, maintaining a very open and transparent approach with governments, the private sector, NGOs, the media, and academic researchers. The primary aim would be to add value to existing institutional setups, accelerating the delivery of their outputs and building new partnerships. Short- and medium-term results should be clearly spelled out, to avoid raising excessive expectations or losing momentum and credibility, and periodic reviews should be undertaken to ensure that global cooperation in this area is on the right track.

To promote some ‘early harvests’ on SDGs monitoring, encourage innovation and experimentation, and ensure that the possibilities of the data revolution are being fully utilised for sustainable development,

We recommend …

  • The establishment of a “Global partnership for sustainable development data” (GPSDD)tomobiliseandcoordinateasmanyinitiativesandinstitutionsaspossibletoachieve the vision sketched above.TheGPSDD could promote several initiatives, such as:
    • The establishment of a biannual “World Forum on Sustainable Development Data”, and associated regional events. These would maintain momentum on data improvements, foster regular engagement between private and public actors, showcase ongoing activities and initiatives and provide practical spaces for innovation, knowledge sharing, advocacy and technology transfer.
    • Establish a “global users forum for data for SDGs”, to ensure feedback loops between data producers and users to improve the usefulness of data produced. It would also help the international community to set priorities and assess results achieved
    • Work in partnership with international and regional organisations, and other actors, to enhance coordination of work in this area, to share knowledge on SDG monitoring, encourage good practice such as open data and harmonisation, and to streamline capacity building initiatives and reduce duplicated effort, mobilizing new resources.
    • Broker some key global public-private partnerships for data sharing. Drawing on existing efforts already underway, these would provide models for best practice, useful for national and regional bodies trying to negotiate similar arrangements, would identify incentives and constraints specific to various industries, would allow for economies of scale, and would demonstrate the value and the possibility of sharing data and collaborating between public and private sectors.
  • Some ‘quick wins on SDG data’ todemonstratethe feasibility of different approaches, experiment and innovate with partnerships and methods as a first step to setting up longer term initiatives. These could include:
    • a “SDGs data lab” to support the development of a first wave of SDGs indicators as soon as possible after they are agreed at global, regional or national level. Taking into account the important experience of the interagency expert group on MDGs and the whole MDG monitoring architecture, the lab should mobilise key public, private and civil society data providers, academics and stakeholders to identify available and missing data and indicators, as well as opportunities for benefitting from new methods, analytical tools and technologies to improve the coverage, timeliness and availability of indicators in each of the main SDG categories.
    • Develop a SDGs analysis and visualisation platform to launch in September 2015, using the most advanced tools and features for exploring and analysing data. The development of the website would also represent a laboratory for fostering private-public partnerships for data dissemination and visualisation. It would be continuously updated during the lifetime of the SDGs, remaining a showcase for new ideas and innovations and a source of high quality and up to date information on progress.
    • To launch the website, establish an initiative to draw on the best, most innovative and most diverse sources of data to build a dashboard on ”the state of the world”. This would harness the richness of traditional and new data, maintain the excitement and openness of the whole SDGs process, engage think-thanks, academics and NGOs as well as the whole UN family in analysing, producing verifying and auditing data, provide a place for experimentation with methods for integrating different data source, including perceptions data and citizen generated data, and eventually produce a ‘people’s baseline’ for new goals.

Taken together, we believe that these recommendations could move the world onto a path of information equality, where all citizens, organisations and governments have the right information, at the right time, to build accountability, make good decisions, and ultimately improve people’s lives.


[1] The coding of the nature of the data in the MDG database ( is as follows:

  • Country data: Produced and disseminated by the country (including data adjusted by the country to meet international standards).
  • Country data adjusted: Produced and provided by the country, but adjusted by the international agency for international comparability to comply with internationally agreed standards, definitions and classifications.
  • Estimated: Estimated are based on national data, such as surveys or administrative records, or other sources but on the same variable being estimated, produced by the international agency when country data for some year(s) is not available, when multiple sources exist, or when there are data quality issues. Estimates.
  • Modelled: Modelled by the agency on the basis of other covariates when there is a complete lack of data on the variable being estimated.
  • Global monitoring data: Produced on a regular basis by the designated agency for global monitoring, based on country data. There is no corresponding figure at the country level.



[iv] : McKinsey Global Institute (November 2013). “Lions go digital: The Internet’s transformative potential in Africa.” Available from:


[vi] United Nations Children’s Fund, Every Child’s Birth Right: Inequities and trends in birth registration, UNICEF, New York, 2013.

[vii] “Mapping Gender Data Gaps”






[xiv] Pentland, A., The Data Driven Society, Scientific American, October 2013, pp. 78-83


[xvi] Global Civil Registration and Vital Statistics Scaling Up Investment Plan 2015-2024 by the World Bank and WHO

[xvii] The World Bank Group and the governments of Canada, Norway, and the United States

The request for comments on the draft report has ended.


  1. Ms Director General of UNESCO,Excellency’s, Ladies and Gentlemen’s ,i’m following with grave concern the sustainable development agenda of the United Nations General Assembly and as many peoples around the world want and requires a sustainable development by 2015 agenda, we know that the time is not in our side,the UNESCO is in extremely tight deadline and also the UN, the Data revolution it is of highly importance to create a strong and secure safety base to protect Data’s which are top secret,i hope this message to be a start point as organizations to work together UNESCO and UN for a sustainable development. Thank you!

  2. Dear Honorable Leaders, Innovators, Scientists and Dignitaries,

    I’m happy to see my contributions – lines 809 and 816 – have been considered and seen as “quick wins.” The language is precise and well defined, and I see no reason to edit what you have written there. I’ll read the rest of the report more thoroughly and respond back before Monday if I see anything that should be changed. If there is anything further I can do to assist with this portion of the project then please let me know. I’m happy to serve in any capacity where I can be useful. Cheers!

  3. As a past curator of digital data for the US National Library of Medicine, my experience speaks of 2 key points in delivering data to the public from a government agency. 1) Quality Assurance 2) Mandatory Adherence to Standards.

    1. In my opinion, establishing a “global users forum for data for SDGs” is a great starting point, but in order to actually impact data quality and availability, a quality assurance agency is absolutely critical. Asking the public to provide recommendations via a forum is also a great first step, but experience proves that an authoritative governing body is essential to unify and enforce content quality. This is especially apparent in data whose content cannot be programmatically checked.

    2. Again, an oversight body is needed to be sure that adherence to standards are met by data submitters. Output quality is seldom a priority if there is no validation and no real-world consequence for low quality. At NIH, grants are contingent on submission of the finished research to a public repository. Working with world governments to establish acceptance systems for data ingest, that enforce robust standards, and rejecting low quality data is essential because we are committed to the ideas put forth in this paper.

    Thank you,
    Abraham Becker

  4. The National Alliance of Women’s Organisations (UK) would like to see the data disaggregation paragraph mention ‘marital status’. This is important for women’s participation in sustainable development. In particular widows suffer from stigma, harmful traditional practices and important constraints on their freedom to gain skills, secure and enjoy employment. They are of all ages – some very young especially in conflict zones. But they also offer solutions and need to be included in decisions that affect them. A category of marital status will identify problems (including child marriage) and offer solutions.

  5. I fully support the recommendations on human rights and data disaggregation. Because disaggregated data is so critical to informed policy making, I do think it should be stronger. There are some dimensions along which data should always be disaggregated, and others where data should be disaggregated in a way that captures meaningful information for the programs/policies under consideration. As such, I would suggest rephrasing as follows:

    “To the extent possible and with due safeguards for individual privacy and data quality, data should be disaggregated across many dimensions. At a minimum, data should always be disaggregated on the basis of sex, age, geography and wealth. Disaggregated data should be collected on other dimensions based on their relevance to the program, policy or other matter under consideration, for example, ethnicity, migrant status, HIV status, sexual orientation and gender identity, with due protections for privacy and human rights. Disaggregated data can provide a better comparative picture of what works, help inform and promote evidence based policy making, and ensure resources are distributed more effectively.”Reference

  6. Figure between lines 563 and 564 has to capture the consequence of the interaction it depicts and that consequence is purpose of the interaction so add in the centre “Possibilities” to People and Planet

  7. Thank you for sharing the report and inviting comments. More than 70 CSOs from more than 50 countries have endorsed transparency and monitoring of public spending to achieve development goals. Given our shared concerns, we propose the following:

    1. Transparency on publicly-funded data sets and data on public spending

    Line 629, suggested insert:

    This includes key budget reports, such as budget plans, execution and audit reports, that all governments are expected to publish as per international good practices.

    2. Revolution for equity in access and use of data – Example Box

    Line 184, suggested insert:

    In Mexico, a budget research and advocacy group called Fundar developed an online database on government farm subsidies. One of the key problems brought to light by the database was the way in which billions of dollars of the funds were distributed. Though many farm subsidy programs claim to target the neediest farmers, the database revealed that a small group of wealthy farmers had captured the vast majority of subsidy funds over time (the top 10 percent of recipients had received over 50 percent of the funds). The government responded to the publicity generated by Fundar by reviewing and changing the distribution of the subsidies.

    Source: Fundar, International Budget Partnership

    3. Recommendations

    After Line 815, suggested insert:

    Governments should publish data on the public resources they collect and invest in pursuing each of the goals, as well as the results they achieve. A regularly updated UN registry could work as a global repository of such data, and provide easy access to disaggregated information that would allow all stakeholders to monitor what governments are doing and hold them accountable for their performance.

  8. Dear Authors – thanks for the substantial piece of work; also showing how “big data” is relevant for policy making. Although data quality is an issue to care about, quality of data input improves if people notice that data are used. Thus, essential to improving data quality is to have data users and to communicate about results obtained and difficulties tackled. Honour the efforts of “first users” to give incentive to improve quality of data entry. Regards, Martin (HoU, Horizon 2020 Data and Information Service, European Commission)

  9. Hello, following (‘..the right to due process’) how about replacing
    ‘..(for example in how data is used to make decisions about people)’
    ‘..(for example when data is used as evidence in proceedings, or in administrative decisions)’,
    Just pulls back more to the centre of gravity for when due process considerations may be primary. Due process considerations vary in the application, quite widely, in different states bound under ICCPR. Due process is largely delineated by domestic common law or legal tradition. Due process rights are expressed in absolute terms through justice access rights. Many data disputes will be civil. Those involving interactions with ICCPR may be significant or grave in their effect. So, all up my feeling leans towards a slight re-emphasis on the example chosen at this para.


    ABOUT LINE 30: The volume of data in the world is increasing exponentially: one estimate has it that 90% of the data in the world has been in the last two years[i].
    But, although you later warn about the abuse of data by those in power, this misses the CRUCIAL point that data HAS to be collected for a purpose; and that is frequently absent from omnibus household surveys: see attached article written 27 years ago (Carr-Hill, 1987), from which the conclusions still apply when considering large scale policies:
    1. Data and evidence cannot always be used to decide between two competing theories, or between competing policies;
    2. Expectations of finders and proposers are very different, and are liable to lead to misunderstanding;
    3. The request for further data is often a means of delaying positive social action; and finally
    4. The possibilities of gigantism.

    ABOUT LINE 150: Many people are excluded from the new world of information by language, poverty, lack of education, remoteness or prejudice and discrimination
    Many of the poorest are excluded from the standardized household surveys because, with rare exceptions, typically omit BY DESIGN: those not in households because they are homeless; those who are in institutions, including refugee camps; and . mobile, nomadic or pastoralist populations. In addition, in PRACTICE, because they are difficult to reach, household surveys will typically under-represent: those in fragile, disjointed or multiple occupancy households (because of the difficulty of identifying them); those in urban slums (because of the difficulty of identifying and interviewing); residents of certain areas of a country deemed to pose a security risk; and other adults or children in households who are at risk of being under-represented because of illegality (e.g. underpaid household servants in rich households) or stigma (children with disabilities) (see Carr-Hill, 2013).

    And it is not just my hobbyhorse; Buettner and Garland (2008) speaking at a Conference on coordinating statistical systems say, on behalf of the UN Statistical Division, that ‘children are systematically undercounted’; and the article by Setel et al . (2007) includes in the ‘counting everyone’ in the title, followed by his blog “In poor areas, up to 90 percent of deaths may happen at home,” leaving no opportunity for a physician to observe the death and write a certificate, said Setel. Nor do developing countries have the systems to capture that information after the fact, he added. – See more at:

    Buettner, T., & Garland, P. (2008). Preparing population estimates for all countries of the world: Experiences and challenges. Committee for the Coordination of Statistical Activities, Rome.
    Carr-Hill, R.A. When Is A Data Set Complete: A Squirrel with a Vacuum Cleaner, Social Science and Medicine, Vol 25, No. 6 , p. 753-
    Carr-Hill, R.A. (2013) Missing Millions and Measuring development Progress, World Development, 46, 30-44
    Setel, P., Macfarlane, S., Szreter, S., Mikkelsen, L., Prabhat, J., Stout, S., et al. (2007). A scandal of invisibility: Making everyone count by counting everyone. The Lancet, 370(9598), 1569–1577.

    There are two main problems to address:
    • Not enough good quality data. In a world increasingly awash with information, it is shocking how little we know about some people and some parts of our environment. What is MUCH MORE shocking is that we do not know ANYTHING about a significant minority of the poorest – those who are NOT in the sampling frames of household surveys (see estimates in Carr-Hill, 2013 in response to Section on ‘A Data Revolution’
    • Data that is not used or not usable: To be useful, data must be of high quality and must be made accessible to those who want or need to use it. That is the SUPPLY SIDE perspective; from the DEMAND side, if the data cannot be used to comment, ‘evaluate’, examine provision then it is useless; and so ‘accessibility’ MUST be assessed from the community perspective, i.e. is it IN FACT used by the community. Or, as a World Bank expert put it several years ago, if data is not use within 5 km of where it is collected it decays (i.e. no one bothers about collecting data correctly).
    A cultural shift is required, towards a world where data is no longer the preserve of statisticians or computer programmers, but where everyone values, feels entitled to, and is confident navigating the growing ocean of data that surrounds us every day, and uses it in ways we ca not now imagine. It might be worth you looking at Radical Statistics (, a journal that has been advocating precisely that for over 40 years!

    As well as being important in its own right for accountability purposes, through its impact on policy and behaviour better and more open data can save money and create economic, social and environmental value. Although research in this area is still limited, modelling exercises and evidence from actual examples illustrates the scale of the potential impact of better and more open data on the economy. All your examples are from the commercial sector; why not use those examples where communities – in both poor and rich countries – have demanded information about their schools, which have led to changes in school management?
    Millions of people in low- and middle-income countries are denied basic services and protection of their rights because of deficient CRVS and national identification systems. Lacking records of their birth and civil status, they are excluded from health coverage, schooling, social protection programs, and humanitarian response in emergencies and conflicts. It is much more than that; they will automatically be excluded from the sampling frame of any survey based on administrative data; and they might well exclude themselves in practice from any survey because they are afraid of the consequences of not being officially registered.

  12. IN THE SECTION ON Mobilising the data revolution for sustainable development: a call to action, I HAVE NO SPECIFIC COMMENTS TO WHAT YOU HAVE WRITTEN BUT

    LINE 32: The volume of data in the world is increasing exponentially: one estimate has it that 90% of the data in the world has been in the last two years[i].
    But, although you later warn about the abuse of data by those in power, this misses the CRUCIAL point that data HAS to be collected for a purpose; and that is frequently absent from omnibus household surveys: see attached article written 27 years ago, from which the conclusions still apply
    1. Data and evidence cannot always be used to decide between two competing theories, or between
    competing policies;
    2. Expectations of finders and proposers are very different, and are liable to lead to misunderstanding;
    3. The request for further data is often a means of delaying positive social action; and finally
    4. The possibilities of gigantism
    LINE 137: Many people are excluded from the new world of information by language, poverty, lack of education, remoteness or prejudice and discrimination
    Many of the poorest are excluded from the standardized household surveys because, with rare exceptions, typically omit BY DESIGN: those not in households because they are homeless; those who are in institutions, including refugee camps; and . mobile, nomadic or pastoralist populations. In addition, in PRACTICE, because they are difficult to reach, household surveys will typically under-represent: those in fragile, disjointed or multiple occupancy households (because of the difficulty of identifying them); those in urban slums (because of the difficulty of identifying and interviewing); residents of certain areas of a country deemed to pose a security risk; and other adults or children in households who are at risk of being under-represented because of illegality (e.g. underpaid household servants in rich households) or stigma (children with disabilities)

    Carr-Hill, R.A. When Is A Data Set Complete: A Squirrel with a Vacuum Cleaner, Social Science and Medicine, Vol 25, No. 6 , p. 753-
    Carr-Hill, R.A. (2013) Missing Millions and Measuring development Progress, World Development, 46, 30-44

    There are two main problems to address:
    LINE 250 Not enough good quality data. In a world increasingly awash with information, it is shocking how little we know about some people and some parts of our environment. What is MUCH MORE shocking is that we do not know ANYTHING about a significant minority of the poorest – those who are NOT in the sampling frames of household surveys (see estimates in Carr-Hill, 2013 in response to Section on ‘A Data Revolution’. PLEASE MAKE THIS EXPLICIT
    • LINE 328 Data that is not used or not usable: To be useful, data must be of high quality and must be made accessible to those who want or need to use it. That is the SUPPLY SIDE perspective; from the DEMAND side, if the data cannot be used to comment, ‘evaluate’, examine provision then it is useless; and so ‘accessibility’ MUST be assessed from the community perspective, i.e. is it IN FACT used by the community. Or, as a World Bank expert put it several years ago, if data is not use within 5 km of where it is collected it decays (i.e. no one bothers about collecting data correctly).
    LINE 379 A cultural shift is required, towards a world where data is no longer the preserve of statisticians or computer programmers, but where everyone values, feels entitled to, and is confident navigating the growing ocean of data that surrounds us every day, and uses it in ways we ca not now imagine. It might be worth you looking at Radical Statistics (, a journal that has been advocating precisely that for over 40 years!
    LINE 432/3 As well as being important in its own right for accountability purposes, through its impact on policy and behaviour better and more open data can save money and create economic, social and environmental value. Although research in this area is still limited, modelling exercises and evidence from actual examples illustrates the scale of the potential impact of better and more open data on the economy. All your examples are from the commercial sector; why not use those examples where communities – in both poor and rich countries – have demanded information about their schools, which have led to changes in school management?
    Millions of people in low- and middle-income countries are denied basic services and protection of their rights because of deficient CRVS and national identification systems. Lacking records of their birth and civil status, they are excluded from health coverage, schooling, social protection programs, and humanitarian response in emergencies and conflicts. It is much more than that; they will automatically be excluded from the sampling frame of any survey based on administrative data; and they might well exclude themselves in practice from any survey because they are afraid of the consequences of not being officially registered.

    IN THE SECTION ON Mobilising the data revolution for sustainable development: a call to action, I HAVE NO SPECIFIC COMMENTS TO WHAT YOU HAVE WRITTEN BUT

  14. I would like to see the threats of cyberinsecurity mentioned clearly and how data resilience will evolve to protect the objective data revolution.

  15. Pingback: Friday Roundup: Ebola, Malaria and Cellphones, Jean Tirole, Report on the Data Revolution, and Deworming - Sig Nordal, Jr

  16. Thank you for sharing the report and inviting comments
    I fully support the recommendations on human rights and data disaggregation. Because disaggregated data is so critical to informed policy making
    Governments should publish data on the public resources they collect and invest in pursuing each of the goals, as well as the results they achieve.
    Although data quality is an issue to care about, quality of data input improves if people notice that data are used.

  17. Dear all,
    thank you for sharing the draft report and asking for comments.
    After line 387 – Data for everyone:
    If we want to make data available for everyone, beside literacy or data protection concerns we must focus on the “how” we make data available. Too often data is available but too complicated for everyone to read and understand. Data must be presented in a visual and simple to understand way. Only then data will be used by everyone.
    Thank you,
    Christian Stampfer

  18. Thank you IEAG Members and IEAG Secretariat for the excellent work done in putting this Great Report together and in record time. My initial assessment is that the Report has effectively captured the essence of serious public and private contributions to the Data Revolution Global Consultation.

    However, the Recommendations still largely address, unintentionally in my view, Data Revolution What and Why Questions, hence the point made by Christian on the need to focus on “How” we make Data available and this underline need for IEAG Members and IEAG secretariat to effectively and decisively address Data Revolution How questions in the Final Report. There is at least one suggestion on way forward in this regard.

    It is pertinent to note that without meaningfully involving all whose ideas have enriched the Report, including IEAG Members and IEAG Secretariat that have put the Report together, in its Implementation and the Monitoring and Evaluation of this Implementation, the probability is high that answers to Data Revolution How questions cannot be designed and delivered in ways that help achieve increasing convergence between Data Revolution Vision Intention and Reality. Allowed to occur this will be Great Error on the part of IEAG Members and IEAG Secretariat.

    It is our hope that this error is not committed in putting the Final Report together because such error would result in this Global Consultation being Motion without Movement, whose consequence could ultimately be catastrophe for Citizens in both Developed and Developing Countries in our World today, particularly the over 2 Billion Poor including Women and Children in both Developing and Developed Countries.

    I have suggestions for enriching the Report. It appears to be too lengthy to Post here when completed and so will be sent through Private contribution but extracts will be made public before the Monday 27 October deadline.

    Once again, Congratulations to IEAG Members and IEAG Secretariat. It is our hope that World Leaders will give the Implementation as well as the Monitoring and Evaluation of the Implementation of the Report Recommendations every required Practical help and support.

    God Bless our World.

  19. The authors of the report are to be congratulated for turning around the report in such a short time frame, and providing strong and important messages regarding the need for a data revolution, in particular one that is able to track progress of some of the currently invisible groups, including children with disabilities, or refugees and IDPs for example. Recommendations at the end of the report are very valuable.

    A general comment is that further work in relation to the data revolution needs to take more account of specifics related to the goals. Statements like a ‘Of course, there is considerable variation in data availability between indicators, where, for example, data on malaria indicators is very scarce but there is relatively good country level data available for most countries and years for the ratio of girls to boys enrolled in primary, secondary and tertiary education’ [p9]
    potentially gives the false impression that data needed for the education targets are readily available so does not need to be given as much attention. Gender-disaggregated data on access to different education levels are available (although less so in the way required for tertiary education), but limited data in a comparable way for learning – which is likely to be more of a focus post-2015. Unfortunately such statements regarding education are common in general reports of this kind, which do not do justice the wealth of information that is available that could provide a better understanding of the strengths and constraints of tracking progress towards goals and so the related demands for data (I imagine health and nutrition specialists, for example, could make similar comments). Such statements as that on page 9 of the report are not based on a thorough review of the data needs in different areas is potentially dangerous.

    A related point is that this report – or a follow up one – would benefit from engaging with those who have been using data to track progress for specific goals, and so learning lessons from this for post-2015 (the UN MDG reporting process provides a small snapshot so should not be the only, or main, basis, for the current state of affairs). To take the example that I am familiar with, the Education for All Global Monitoring Report is widely recognized as an example of good practice, as is the World Inequalities Database on Education (WIDE: that provides disaggregated data using household surveys and learning assessments to the extent possible. Lessons from this could be derived in relation to how data can be used, and what the limitations are for existing data not just for education but also for other sectors (and is indeed already happening through work within ODI and with Save the Children, for example).

    Related to the point on page 14, line 475, a lesson from the EFA Global Monitoring Report process has been the importance of monitoring that is independent of data collection (so the UNESCO Institute for Statistics collects and collates administrative data, while the GMR analyses this and other sources of data including from household surveys). The role of independent monitoring isn’t very clear from the box which, while making some good observations, would also benefit from providing a framework of how these different actors connect – and so a flow of responsibilities (from collection, to monitoring, to dissemination, as well as including training), rather than listing their potential contributions.

    From my experience with the EFA Global Monitoring Report, I would agree with the data revolution report that household surveys possibly have the greatest potential for tracking progress post-2015, although more work will be needed to ensure a wider number of countries have comparable household data on key variables, that they are undertaken at regular intervals, and have a sufficient sample size to allow for disaggregated data analysis (including for overlapping inequalities) for sub-groups of the population, eg selected age groups. [I don’t understand though, what is meant by the sentence: ‘especially if augmented by data from less traditional sources’ [p12, lines 362-363].

    Examining this and other such examples of the use of data of relevance to particular targets in more detail (rather than used as case studies in boxes in a non-systematic way) would help to give a more substantive understanding of where we stand and the challenges ahead.

    A proposal is that, for future work on the data revolution, sub-groups are set up that look at each of the goals to provide inputs in relation to these more specifically to support and feed into the more general oversight that the data revolution group can do. The education sector has already started to do work on ways to measure the targets associated with the OWG, identifying potential challenges, data needs etc. This work will only ultimately be effective if it is taken up by those in the UN system who have responsibility for the post-2015 goals overall – for example, the need for better data for education via household surveys would need to be addressed through a wider system of data collection that isn’t education specific.

    And those with responsibility in the UN system for the data revolution can indeed benefit from work that is going on in education – and probably other areas – while also helping to inform our work by engaging more directly. Only then will it be clear that recommendations from reports such as this will have real practical implications for the fulfillment of identified goals.

    From an education perspective, such engagement is vital if we are not to suffer from the same problems that existed with the parallel Education for All and MDG processes (the former are much more aligned to the current set of OWG targets, given the latter have recognized the limitations of the MDGs – and part of the explanation of the problems with the MDGs was that they were too distant from those who understood the issues in particular areas).

    I very much look forward to the final report, and continued engagement and discussion on these important issues. Many thanks again to the expert group for the report.

  20. This paragraph sets the tone for much of the paper as a whole. In my understanding it represents a fundamental shift in the way in which the data revolution has been defined. It equates the data revolution with a generic global technological revolution , and consequently focuses much of the paper on the role of technology in statistics. Interesting as this topic may be it is a major diversion from real-world development issues.Reference

    • Just tools & technology & more data is a too shallow basis to talk about radical change ou ability to achieve new results with it (ttd) is as important – entrenched practices must be replaced by new ones that fully exploit the new means.

    • Just tools & technology & more data is a too shallow basis to talk about radical change; our ability to achieve new results with it (ttd) is as important – entrenched practices must be replaced by new ones that fully exploit the new means.
      The Rio example is nice, but it also is very basic – the observation is evident & so is the required actionReference

  21. The rapidly growing range of technology and its expanded availability demonstrates increased opportunities to engage more people in tackling global challenges. For instance, in less than five days, the MapGive campaign, run by the US State Department, mobilized more than 300 UN Online Volunteers to supply humanitarian responders with the most recent map information for Ebola-affected areas in Sierra Leone and Liberia.Reference

  22. Who are “we” in this paragraph? As you’ll see in my further comments, I’m passionately interested in a data revolution that makes a shift from a top-down to a bottom-up paradigm, so that communities and CBOs can set priorities and track their own progress.Reference

    • I strongly agree on the need for data to also empower at the local and sub-national level to ensure communities and CSOs can play a stronger role in holding decision-makers to account. Data required for decision-making is often different to data required for monitoring progress and analysis to demonstrate which interventions are working and why. The is very little emphasis in this document on data on budgets, government spending, private sector spending (including CSR and foundations) down to the local level. Without the transparency of this data it will be difficult for national and sub-national decison-makers to make informed decisions on spending often very limited resources locally.

  23. This para continues to reflect an unconscious bias towards national governments and doesn’t reflect more participatory local governance – community-level engagement and decision-making.Reference

  24. Here, for example, the report could have language consistent with the draft SDGs simply by referring not just to “governments” but to “government at every level”Reference

  25. In the next three paragraphs, it could be introduced that – in the information age where education and participation in governance are centered in the internet – affordable access to the internet becomes a human right.Reference

  26. I’m glad that districts are mentioned here, but the tone implies that someone at higher levels is making district-based decisions – not true empowerment of democratic decentralization. And if I were writing it, I would have said “allows individual communities to compare their progress – on a timely basis – with district and national goals and progress.”Reference

  27. To be useful for community decision-making, household survey data would need to have a sufficient sample of each community, and would need to be annual. This is not likely to become affordable. A more valuable approach would be to maximize the integration of data collection in normal service delivery, and aggregate upwards.Reference

  28. These three distinctions are very good, but they leave out a fourth critical factor: “Data by every distinction.” – Data must be meaningfully disaggregated by gender, age, minority group and geography down to the sub-district level.Reference

  29. For countries like Ethiopia where over 80% of the population lives in scattered rural villages and on average more than 3 hours away from major roads, the data can only be rough estimate. Collecting accurate data will be too expensive and may not be cost feasible, compared to what they will gain out of it at the end.
    Starting at the dawn of light all members of family are out of their thatched roof home each member working to make ends meet. Child goes out to watch animals, daughter and mother go out to fetch water and fire wood, the man goes farming. It is hard to count these types of families.
    For SDG for these types of people, the action plan is better aimed at enabling people to come together into multi-residential communities or cities. They can cost effectively get clean water, electricity, diversified economic activity instead of subsistence agriculture. They can raise their productivity and be in a position to pay back for the investments that allowed them to move and work. They will improve on their own and achieve most MGD goals if generous investments are made engaging them.


  30. While I would endorse empowering statistical offices, in a bottom-up data revolution data generation is increasingly integrated into all social and economic activities, and this new integration must go far beyond statistical offices – throughout every organization – as soon as possible.Reference

    • Again- Agreed. Its the concerned departments that are likely to be sites of data collection and first line analysis.

  31. In Bullet 8 – Civil Society organizations are not merely watchdogs – they are actors in their own rights – and represent important generators and analyzers of data, and are increasingly being demanded to meet very high levels of accountability.Reference

    • To add to J. Coonrod point, CSOs not only must meet the same high levels of accountability as other data generators, they also face the same challenges to capacity and culture as other contributors to the data revolution. So a vision for 2020 must include organizations (who are often also the most effective “infomediaries”) who not only observe standard formats (Bullet 5) and contribute to others’ data literacy (Bullet 6), but who also have incentives to maintain their own capacity and culture of good data practices and their own data literacy (in addition to the CSO role supplying and empowering others).

  32. I suggest you put a target date on this. Anything beyond 2017 would be pretty late in the game – by the end of 2016 would sound right.Reference

  33. … based policy making at every level, down to the community level.

    …must match the decision cycle, which in most cases means it must be refreshed annually.Reference

  34. Governments at all levels should encourage the private sector to identify data streams it possesses which can serve the public good. This is particularly important for privately held public concessions – such as privatized public services, particularly those in a regulated environment.Reference

  35. UN agencies, in their advisory roles to many major ministries, have a key role in this data revolution, and assessment of progress towards standards should be an annual review subject by UN country teams.Reference

  36. While global-level initiatives are useful, every country needs to mobilize its own organizations and create its own strategies. The IEG should recommend that UN teams encourage such “Data Revolution Summits” to be held in each country before the end of 2016.Reference

  37. I fully agree with the collection of current and accurate data is critical for making analysis and projections. I used extensive data in many mega projects I worked on in Canada. For least developed nations, the world knows basic data – productivity, tools and ways on how they make their living is stagnant. People in subsistence agriculture still make less than one dollar a day, the pastoralists (that are up to 15% of population in in some countries) get less than a litre milk per day from each animal – compare with 20-50 litres per cow in developed economies. This can be seen in the UNHDR 2014 report. The Millennium Villages Project showed intervention works, but it requires major investment and full stakeholder engagement to make it sustainable.
    The data that is changing is climate change due to deforestation and accelerating erosion, worsening living conditions in scattered rural communities because of declining agricultural yield due to decreased precipitation and loss in land fertility. New SDG can change this for good. Steer LDC population to live in planned urban centres and increase productivity in all economic sectors. If each person makes ten dollars a day working in newly created demand and supply centres in cities, it means tenfold increase in GDP, they can pay back all start-up investment with high rate of return. Modern ecological agriculture can change the food scarcity with multi-fold increase in agricultural output with fewer people. This will reduce population growth, improve social conditions, makes less chaotic living, improves environment and nurtures enduring world peace.
    See the 4EPR approach
    Educationally, Economically, Environmentally, Engineered Productivity Revitalization at

  38. Dear Authors,
    Thanks for sharing your extensive work.
    I fully agree with the collection of current and accurate data is critical for making analysis and projections. I used extensive data in many mega projects I worked on in Canada. For least developed nations, the world knows basic data – productivity, tools and ways on how they make their living is stagnant. People in subsistence agriculture still make less than one dollar a day, the pastoralists (that are up to 15% of population in in some countries) get less than a litre milk per day from each animal – compare with 20-50 litres per cow in developed economies. This can be seen in the UNHDR 2014 report. The Millennium Villages Project showed intervention works, but it requires major investment and full stakeholder engagement to make it sustainable.
    The data that is changing is climate change due to deforestation and accelerating erosion, worsening living conditions in scattered rural communities because of declining agricultural yield due to decreased precipitation and loss in land fertility. New SDG can change this for good. Steer LDC population to live in planned urban centres and increase productivity in all economic sectors. If each person makes ten dollars a day working in newly created demand and supply centres in cities, it means tenfold increase in GDP, they can pay back all start-up investment with high rate of return. Modern ecological agriculture can change the food scarcity with multi-fold increase in agricultural output with fewer people. This will reduce population growth, improve social conditions, makes less chaotic living, improves environment and nurtures enduring world peace.

  39. Some place in the document, may be here, it should be mentioned that data quality requires independence from political influence. We have seen too many cases (for example in the former German Ddr) in which data were not reliable because they have been manipulated by governments, pretending to impose methodologies and controlling the disclosure. The result has been economic disaster.Reference

  40. <>

    would consider rephrasing as

  41. As new data is generated a vast and increasing proportion of it is passively collected either through direct observation (i.e. via sensors, adtech) or inferred via algorithms. The growing distance between the awareness of individuals and the amounts of “massive and passive” data being generated and mediated by third parties fuels anxiety among individuals and communities.Reference

  42. <>


  43. perhaps a bit more detail on the nature of those inequalities. Seems like a fine place to sprinkle a bit of Pikety’s work on wealth concentration. Or maybe some of Jared Lanier’s work on the disproportionate value flows within the current digital ecosystem. To me this is arguably one of the core tensions to be resolved for a peaceful data revolution….the redistribution of value to the individual data producers.Reference

  44. I agree. I also strongly disagree (almost from an information theory perspective) that data is the bedrock of accountability. There’s lies, damned lies and big data. Due process, auditing and enforcement over the impact measurements (which require robust and accurate data sources) are essential for trusted and accountable systems… not the data in and of itself.Reference

  45. Information is power. The more things change….the more they stay the same.

    I think this paragraph reflects some of the institutional biases of the experts group. It’s a bit “pro-Westphalian”Reference

  46. Think it might be informative to include a comment or two on the sclerotic delays in getting other mobile network operators to provide access to their data in the fight against ebola. Orange could be the exception that proves the rule…the process for making this data available was not done quickly nor in a crisis environment. The lack of a measurement for sitting on our hands and not sharing access to data sets is costing human lives. See the link from the Economist magazine for more details (

  47. One might add the extreme lack of rich, contextually informed understanding (i.e. qualitative data( of the lives of the most vulnerable is a significant gap. Actions without understanding could create second order problems which only make things work. Need to prioritize the risks of blindly operating in areas of “known unknowns”.Reference

  48. oops…meant to say “make things worse not work”.Reference

  49. May want to tweak that one a bit… People may want to have better and control on the impacts and decisions of data-fueled policiies but maybe not exclusively the raw data. There are capacity, usability and utility issues in just getting raw and unfettered access to all one’s data. Understanding and control over data is necessary but not sufficient. The tools, due process and meaningful engagment over the intended impact of how it is used is also vitally important. I don’t have the time to manage and control all the carbon atoms in my body…it’s how they combine and interact that matters.Reference

  50. Think one needs to reinforce the need to maintain the integrity of the context for how data is used here. The data for now section could easily be interpreted for more centralized governmental surveillance systems. Where does development end and societal surveillance begin. Granted thats a larger meta theme but who/what/how real time data is used for the common good needs to be balanced against how likely and severe the harms could be if it is abused. Not only do we need better real time management of crisis situations but also the assurances that the outcomes are fair and principle-based. It’s just what new amazing impacts could be delivered but how and why and to whom they were delivered.Reference

  51. Agree with the question & would suggest “all members of the global partnership” which would include…Reference

  52. Would add ” can be compared among peers” to emphasize the global partnership as the ” user group”Reference

  53. While the lack of real time data in the public sector is an issue is a problem, not sure its fair to juxtapose national GDP with individual company output, sales and staffing. There is an altogether different level of complexity/scale involved.Reference

  54. Perhaps also flag the role of concerned line departments (eg health, education ministries at all levels- not just national) in the same paragraph. National statistical offices rely on data gathered elsewhere and these structures need to change too.Reference

  55. You may want to flag that the MDG indicators are to be replaced by the SDGs and this would create additional data gaps. Thus, while the data on enrollment figures in education are relatively better than malaria ones (while still being hugely deficient as has been pointed out in one of the comments made yesterday)- the SDGs introduce altogether new indicators for which data simply doesn’t exist at present.Reference

  56. Adding: social actors also include divers range of “legal persons” for which privacy must be differently conceived than for human beingsReference

  57. Thank you for a useful report about the importance of collecting and providing data.

    I think one aspect you could pay more attention is an open framework for proposing and criticising data analysis. Once the data is collected modellers will make future projections based on this data. What is needed is a forum for developing these methods. Specifically, I would suggest on at line 248 where you identify two problems, I would identify a third.

    We need an open framework for analysis and prediction: Currently the available data used primarily for visualisation by organisations and not actively utilised to predict future changes, as is needed for development goals. Where data is used for deeper understanding it is primarily analysed in academic papers with long publication times, and low impact. We need to create a vibrant community who analyse, discuss and utilise data. This forum should involve not only researchers, but citizens in both developed and developing countries. We should aim to open up a discussion which involves both prediction and assessment of these predictions.

    A major point here is that if we ope up data, then the analysis and interpretation of data should not be left primarily to experts.

    Example: There already exist online communities such as dataisbeautiful on reddit (with over a million followers), who try to present and utilise data. Such a community can act to both make predictions and assess the validity of predictions. A similar open forum for the SDG could allow a dynamic and verifiable progress for predicting how we are progressing.

  58. The dictionary was the egalitarian “tool” in the former century. We might think Google search has a similar impact on www, but that’s only in as far as search is concerned. When it comes to placing new content search has no answer (beyond SEO). That is why Actor Atlas & its tags have been created, to fill a “remaining”unequal access gap.Reference

  59. All indicators should be disaggregated by disability, race and
    ethnicity, rural-urban location, areas affected by conflict and
    humanitarian crisis and socio-economic status (among others),
    and by gender in each of these categories. Progress should be
    measured not only against aggregate indictors but also in terms
    of a narrowing in gaps between most and least advantaged

  60. Add at the end of the paragraph, “particularly the poorest and most marginalised people who are most often excluded from decision-making.”Reference

  61. Reword from line 26 to “Achieving these goals will require integrated action on social, environmental and economic challenges, with a focus on inclusive, participatory development that leaves no one behind.”Reference

  62. Reword beginning of para from l.45 “Revolutions begin with people, and the data revolution is no different. This report is not about how to create a data revolution, which is already happening, but how to mobilise it for sustainable development that serves both people and planet.” I strongly highlight that no revolution is history has ever succeeded with out people – and this one is no different.Reference

  63. Agreeing with Ame E’s point above, a key shift available to us now is who has the ability to engage and have ownership in the creation, analysis and dissemination of data – this needs to be led by people, rather than technocrats, if it is going to succeed.Reference

  64. I fundamentally disagree with the statement that the data revolution is about quantification of what was previously considered qualitative data. This shows a lack of understanding of the role and value of qual data as complementary but different to quant approaches. Suggested rewording: “It involves the inclusion of both quantitative and qualitative data to give a holistic, nuanced understanding of our global progress.”Reference

    • I agree with Neva. Data revolution should help us deal with the limitations of quantitive data and and tap from the vast potentials presented by both qualitative data as well as participatory techniques

  65. Agree with comments above – would insert at end of first sentence, l94, “through participatory methods that strengthen inclusive, democratic governance.”Reference

  66. Change line 95 to read ‘More information opens up the possibility for an honest, informed dialogue between duty bearers and rights-holders, between service providers and beneficiaries…”Reference

  67. Agree with comment above and would also advise that GDP should not be assumed as a proxy for development; other indicators that better reflect development outcomes for people should be used, e.g. maternal mortality, child and adult literacy, or child stunting.Reference

  68. Suggest rewording from L.235 “First and foremost, the data revolution can and must be leveraged to enable people experiencing poverty and marginalisation to decide on, collect, analyse and disseminate the data that is relevant to their own lives, to hold governments to account and to ensure the best possible service delivery.” People should not be seen as passive recipients in the data rev – this report should understand them as active agents of change.Reference

  69. Agree with comment above and would add that this is why new approaches that involve people in the data process from start to finish are needed. Suggest inclusion of language from line 284: ‘Entire groups of people and key issues remain invisible, which is why the dynamic of data gathering, monitoring and accountability needs to shift so that the poorest and most marginalised groups are no longer excluded or ignored.”Reference

  70. It’s not just about discrimination against women, but also gender inequality more generally. In this context it is important to mention intra-household inequalities, for which there is a massive dearth of data (intra-household spending and distribution of resources, for example), but which severely impact the empowerment and inequality of millions of women but are not revealed by household surveys. It is good to see a mention of care work.Reference

  71. Again, the language here does not understand the need to put people at the heart of development – not by having better data about them – but by empowering their ownership of data. Suggest changing line 311 to ‘but decades of experience in every sector ahs taught us that the systematic inequalities that hide groups from view will not be overcome without deliberate action to ensure that people experiencing poverty and marginalisation have the power to measure, monitor and report on the progressive elimination of inequalities. A core purpose of the data revolution should be to put people at the centre.”Reference

  72. Insert for line 340: “It’s not only about standards. People living in poverty are given no voice on what data would be useful or how it can be useful to them; no role in decision-making, gathering, analysis or dissemination.”Reference

    • Agree with the suggested insertion.

  73. Total Sanitation was widely recognised as a failure until the introduction of Community Led Total Sanitation. See “Community Led Total Sanitation (CLTS) is an innovative methodology for mobilising communities to completely eliminate open defecation (OD). Communities are facilitated to conduct their own appraisal and analysis of open defecation (OD) and take their own action to become ODF (open defecation free).

    At the heart of CLTS lies the recognition that merely providing toilets does not guarantee their use, nor result in improved sanitation and hygiene. Earlier approaches to sanitation prescribed high initial standards and offered subsidies as an incentive. But this often led to uneven adoption, problems with long-term sustainability and only partial use. It also created a culture of dependence on subsidies. Open defecation and the cycle of fecal–oral contamination continued to spread disease.

    In contrast, CLTS focuses on the behavioural change needed to ensure real and sustainable improvements – investing in community mobilisation instead of hardware, and shifting the focus from toilet construction for individual households to the creation of open defecation-free villages. By raising awareness that as long as even a minority continues to defecate in the open everyone is at risk of disease, CLTS triggers the community’s desire for collective change, propels people into action and encourages innovation, mutual support and appropriate local solutions, thus leading to greater ownership and sustainability.”

    The basic point is that top-down approaches – as with the data revolution – are ineffective unless it is engaging and empowering people to make change through participatory approaches.Reference

  74. data from.. local, regional, national, civil societyReference

  75. I would add that policy- and decision-makers have a responsibility to ensure that people who have the least power – the poorest and most marginalised – are at the centre of the post-2015 development agenda; the data revolution offers an opportunity to make that ambition a reality.Reference

  76. And these community approaches – were they women-led/women consulted? Women have particular sanitary needs and open anything leads to risk of rape and all forms of sexual interference an assault.
    If women were not consulted and their view and opinions valued, then whatever ‘the community’ said/wished might not have included their solutions. Including provision for separate buys and gils school toilets.
    And, as long as girls fetch water they are at risk of sexual attacks and of not getting to school.

  77. Under ‘data is for everyone’, suggest language: The rules, systems and investments that underpin how official data is collected and managed should give power to people to identify and shape their needs, while protecting their rights.’Reference

  78. Data accessible to public in ways that encourage greater use or data usability. In developing countries and until technological/internet barrier are overcome, this includes failing to ensure data is accessible in non digital formats at the community level.Reference

  79. It is interesting that while concerns are raised on this thread on need to include answers to Data Revolution How questions in the Final Report, no contributor has made suggestions in this regard. This underlines the challenge facing IEAG Members and IEAG Secretariat.

    Further to last post our suggested answers to Data Revolution How questions have been sent by private contribution. The suggested amendments to the Report focus on:-
    1. Clarification in body of Report to improve understanding of Data Revolution Vision Intention.
    2. Inclusion of 5th Recommendation Area to answer Data Revolution How questions.
    3. Conclusion to better highlight Bright Prospects of Success should the full implementation of Report recommendations and the effective monitoring and evaluation of this implementation receive optimum support from all concerned Leaders – Local, National, Sub-regional, Regional and Global.

    The overarching suggestion is for a GPSDD Pilot Program that start immediately the Report is released on 6 November 2014. Developed Countries, Developing Countries, International Institutions and their Partners need help to implement Report Recommendations with effective monitoring and evaluation of this implementation. This is what Internal Consultants and External Consultants through the GPSDD Pilot Program will provide.

    If this crucial support is not provided, the probability of this Data Revolution Initiative succeeding where past initiatives had failed is low. Allowed to occur, this will be very sad.

    Once more, we commend IEAG Members and IEAG Secretariat for great work done producing the report and calling for comments of draft report. We hope that the enriched Final Report is approved by all relevant authorities; that implementation start on 6 November 2014 and that Data Revolution Vision Ambitions are achieved to the Glory of God and benefit of Humanity particularly the over 2 Billion World Poor.

    God Bless UN.

    God Bless our World.

  80. To support John’s first point, I would add in another bullet point at the top: People experiencing poverty and marginalisation are empowered to decide, gather, analyse and disseminate the data that is most relevant to their lives, and this is taken into account in decision-making at local, national, regional and global levels and as a means of accountability for the SDGs.Reference

  81. Bullet 7 – Private sector should report on their economic, environmental and human rights activities and impacts.
    Bullet 8 – I would suggest that citizens should also be recognised as actors, discrete from CSOs.Reference

  82. Let’s not forget the people who are struggling to meet their basic human rights for food, water and shelter on a daily basis…Reference

  83. Forgive the typos all over my last comment – ‘boys’ not ‘buys’ for just one example!

  84. On statistical systems: Capacity should be built to collect different types of data, including both quantitative and qualitative data – e.g. time-use surveys, which are very important for analyzing gender inequalities.Reference

  85. Just as crucial are gaps between the rich and poor (economic inequality), and those people who benefit from public policies and services and those who are not reached/left behind. Data has a crucial role to play in identifying these inequalities. For example, one concrete first step would be to suggest that household surveys capture the distribution of effective income and capital, especially of high-net wealth families, which would help to combat the problem of under-reporting by the “invisible rich”.Reference

  86. On standards to facilitate: The examples cited are quite limited. Openness and information exchange in e.g. budgets, illicit financial flows are crucial and should also be part of the ‘data revolution’Reference

  87. Ref. bullet one: The list of reasons why the data revolution is important for sustainable development is overly focused on the technocratic aspects and misses the points of empowerment, participation, accountability and identifying and tackling inequalities, which are the main end goals of the data revolution from a human rights / social justice perspective.Reference

  88. This para refers exclusivelly to access to personal data while the primary purpose of conducting a survey is to produce statistical results for informing decision making; one should not ignore that access to micro-data is limited by national legislation for legitimate reasons (see fundamental principles for official statistics and the principles governing international statistical activities). There is a rule of law issue if micro-data are disseminate while not allowed.
    The dissemination of survey results (the first purpose for spending public money) is overall more important to the community at large than the access to micro-data by a few not always linked to national decision making processes. SGD monitoring indicators will be survey results having state recognition, not personal data sets.
    I suggest that this last point is introduced.Reference

  89. One point that is overlooked here is the questions of what kind of data, and broadening the types of data that are collected and used for sustainable development (event-based data, survey-based data, etc). More qualitative data is needed, data on those issues beyond the low-hanging fruit/ what has been measured before (measure what we treasure, not vice versa). As recommended in the original CESR submission, it is important to look not just at outcomes but also process/input indicators, policy efforts and resources allocated. Example: The maternal mortality ratio (an outcome indicator for MDG target 5.A) relies on data that are notoriously difficult to collect and interpret. In contrast, emergency obstetric care (a “process” or input indicator) is a valid and reliable indicator, necessary and policy-relevant in all contexts, and can be monitored at district as well as national levels.Reference

  90. Must also include disability, otherwise many of the hard won gains of the disability community in the post-2015 debate will have been lost.Reference

  91. As well as strengthening the capacity of NSOs, their transparency and accountability should be strengthened, as well as their links and communication channels with the publicReference

  92. This process should include adequate representation from specific sectoral groups. While there are common problems across sectors, the needs, experiences and prevailing challenges are also issue specific.Reference

  93. Especially given the report’s emphasis on access to data, the right to participation should also be included as of fundamental relevance to the data revolution. It would also be useful to acknowledge that the data revolution could usefully serve to allow better collection of data and information on human rights enjoyment & violations, and enable better monitoring of human rights enjoyment (and therefore accountability when human rights obligations are not met).
    The report as a whole seems to have an implicit vision of the relationship between right and the data revolution as one in which the latter could negatively threaten the former (privacy, etc.). But the relationship is much broader and more complex, and encompasses both challenges and opportunities. The mechanisms etc. set up to mobilize the data revolution should therefore have the ‘protection, respect and fulfillment’ of human rights as a core part of their activitiesReference

  94. This and the other proposals in the draft for new networks, forums and platforms must explicitly include the ‘experiential expertise’ of people living in poverty and other disadvantaged people, otherwise they will end up just as another top-down, technocratic exercise. This leads to a further point that the whole conceptualization of citizens/the public in this report could be much clearer that they are individual rights-holders not just subjects and consumers of data.Reference

  95. This section is missing a few crucial elements. 1) there is a need to strengthen civil society’s capacity, resources and space to produce, use and disseminate data (for instance in order to hold governments accountable); 2) following from this, a need to strengthen the capacity of accountability mechanisms (e.g. courts, human rights bodies) at local, national and international levels to interpret and use data; 3) mention of global data literacy should specifically reference need to reach out to those who are poorest and most disadvantaged, otherwise the inequalities keep increasing.Reference

    • Yes, but the goals, targets and the nature of debate has moved on overall and especially on some of the specific goals.

      There also needs to be a stronger interface between the technical aspect of indicator development and the political process of target setting. The indicators need to speak to the goals and targets and not be driven by what is easily measurable. This will be a challenge that the new lab would have to deal with.Reference

  96. Before going into big cross-sector issues, need to put in place sub-groups looking at the concerns of specific sectors (candidate SDGs). These should include concerned UN bodies tasked with the goal (if any), government departments tasked with implementation, academia and civil society. In the latter, participation of civil society networks and alliances would be ideal in order to ensure that CSO contributions are representative of the larger CSO voice.Reference

  97. data on sustainability doesn’t have the profile here throughout, given that these are the “S” DGs. Maybe a specific para or two on, e.g. climate change detection and attribution, determining planetary boundaries – data in the natural sciences?Reference

  98. From Naiara Costa: I wonder if the “data revolution” only refers to the “volume, level of detail and speed of data available”. I think it will only be true “revolution” if it serves the people, especially the most marginalized.Reference

  99. This is the only mention of qualitative data in the whole report. This is very problematic. Quantification of previously qualitative data is not the change sought and can indeed be very reductive. Many stakeholders agree that the data revolution should spur investment in and focus on more and better qualitative data collection. Many of the social and environmental problems we face require quantitative and qualitative data for proper analysis and redress.Reference

  100. Agreed. This is an important point. I’ve drafted one suggested input to highlight that a data revolution can impact qualitative data too (below), as not sure if a general critique of the analysis of data is too late at this stage, but suspect there are other places more integration of qualitiative data could be drawn out:

    Suggest adding at line 87: “Digital systems can also better store and index qualitative data, making it possible for decision makers to deepen their analysis of quantiative data with access to qualitative insights.”Reference

  101. A draft input below I’ve been working on from the Open Data Research Network ( below tries to address this point by noting the importance of records for accountability, with data a tool to access them:

    …it would be more accurate to state that “records are the bedrock of accountability, and data makes is possible to analyse and understand them”.

    Theories of change around the use of open data in securing accountability often rely on using data to discover failures and corruption – but access to the underlying records, with their specific legal status, is important in actually converting those discoveries into the excercise of accountability. A reference to records and record keeping practices as underlying much public data would strengthen the report.Reference

  102. On the subject of legal frameworks, there is no mention of right to information laws here, which are a crucial vehicle for accessibility of data. From the Center for Economic & Social Rights original submission: “Effectively implementing right to information laws that guarantee prompt and effective access to high-quality information on public policies, including on budget, financial and tax policies is an important component of the paradigm shift necessary.”

  103. Civil registration on one hand and Vital statistics on the other are two processes of totally different nature. Only 5 lines are devoted to the former which is by far the most inportant for the population, highly permanent resources demanding and a pre-condition for the latter (producing statistics) which is technically rather simple. The legal environement is a very complex issue very often overlooked. For many yeras households surveys and population censuses have provided the main indicators and will continue to do.
    I believe the actual text could be slighly more balanced between the two issues.Reference

  104. Official statistics is righly included; however, it deserves a specific treatement because it is a system set by states to respond to official needs aiming at serving impartialy the whole of the national community, governed by a specific reguslation, and Fundamental principles have already been endorsed by the UN member countries.
    In most countries the statistical system includes a NSO but also other agencies, a fact overlooked in § 8. Data governance and independence.Reference

  105. Dear DataRevolutionist,

    Congratulation for the quality and “right-to-the-pointness” aspect of this report. Thanks also for sharing and allowing for comments.

    You will find first general comments, then more specific ones:

    ¤¤¤General comments¤¤¤
    As general comment, it would be even stronger if it was backed more on existing literature surveys, one finds very few references in the text. Some figures have no sources. Although the authors did underline that the data revolution is something wider than being able to monitor the SDGs, let’s reaffirm it louder and recall that a good balance have to be find between following up with the SDGs and reinforce the reliability and availability of basic economic indicators. It is important not to create/ increase an eviction effect within NSOs Orienting too much the data revolution towards SDGs monitoring won’t create sustainable incentives – even if the SGDs definition process was way more inclusive/participatory.

    Reference and bibliography might need to be reinforced. DEF Yearbooks are not available for free. And the Mc Kinsey report link is broken.

    ¤¤¤Specific comments¤¤¤
    l.8 suggestion : after “we cannot know how many poeple are born and die;” suggest adding “The ones that are not counted are not accounted for, their lives do not matter” -> reference to the blog post “Rights based data revolution”

    l.18 “the effort involved in monitoring the MDGs has spurred increased investment in just these things, to improve data for monitoring and accountablity” => meanwhile the accuracy of national account have not progressed => Observers underlined the existence of an eviction effect within NSOs dedicating less to national accounts because of donor command and MDGs

    l.120 “Those with access to data and information will have more power” => and foremost those who will produce the data… => then regulation step in to avoid to much market and information power

    l.121 … but yet the public sector, is the only one that can create enough incentive – or compel – people to deliver information, in a more more structured way and really usefull data for people, not only machine generated or data exhaust. Not forget that the state by defnition is the only, ie. at the country level to be able to provide a N=all dataset about citizens.
    “The public sector is falling far behind the private sector in how they generate and use data” … Might be more cautious with this statement ? => private sector is collecting – and destroying a lot of data – but most of them needs refinement, is there so much company really taking advantages of the data they store ?

    l.146 “in many countries the reporting of GDP figures is still months behind real activity, While companies are able to monitor their output, employment, and sales in real time »
    Overstated? => in ALL countries national account are month behind real activity. The actual figure of GDP is know the year after (and not right after the end of fiscal year), the figures given before are simply estimates. Regarding the private sector, not necessarily true for most of the companies, billing delay is a widespread issue.

    l.150 “African countries Spend about 1.1% Of GDP On investment in and use of internet services,” => Should be more cautious, this figure is a guestimates (+ Reference needed dead link in biblio). In most of African countries ICT are not taken into account in the national accounts as for more than half of the African countries, methodologies and base year last from before 2000 which means ICT sector is barely counted in their GDP (see Devarajan, Jerven, etc.).

    l.151 “less than a third of what,on average, is spent by richer countries – meaning that the gap in internet availability and use is growing every year, as some regions accelerate ahead” => Not so clear ? Internet availability is somehow bounded at 100% of the population, so the gap of availability can hardly keep on deepening and accelerate.

    l.590 more general comments => no mechanism or incentives are yet proposed to make the private sector sharing their data. Private sector is on its way to become to bigger data producer. Storing the data has a cost, “owning” the data is an important potential source of profit, by creating asymmetry of information towards competitors and getting to know better consumers, or more basically just by selling it. Data ownership is a real question, are consumer owner of their data? Incentives are to be found for the Private sector to develop data-philanthropy.

    l.613, “data should be classified using commonly agreed criteria and quality benchmarks” this statement might not be clear/bold enough. Quality of data should be acknowledge. Within International database users know very little of data reliability. A rating system should indeed be implemented.

    l.660 the role of press/media and data journalism should be underlined as necessary for ensuring “data accountability” See open-source data journalism launched by the Guardian oct. the 24th

    l.688 “Network of Data Innovation Networks” UN skeptics might be less prone to criticism towards something called “Hub of Data Innovation Network” maybe?

    Congrats to the DataRevolution Team.


  106. The UN should consider which sectors are likely to improve in the next 3-4 decades, and the role of evidence/monitoring with these. Nearly all development indicators have improved, but health and education have improved the fastest, say since 1990. In this context, special emphasis is needed to ensure the reliable monitoring of both sectors. For health, the focus of this commentary, the simplest and most important data to collect is on causes of death. Currently only 3% of the world’s children who die have a medically certified cause of death. As most deaths in LMICs occur in rural areas and without medical attention, alternative systems are needed. These are possible- such as the Indian Registrar General’s sample registration system (see These mortality statistics can be linked, increasingly to other key determinants including environment, poverty, indoor/household conditions and use of health services or education. Together, these 21st century tools applied to simple ideas of counting deaths and describing causes could revolutionize big data for global health. Moreover, as global health is likely to be relatively well funded sector, it makes sense for big data efforts to ensure relevancy to this sector. Prabhat Jha, Director, CGHR, U of Toronto.

  107. Ms Melamed; IEAG Secretariat Head: thank you and the IEAG members for putting together such a comprehensive report on data revolution for the future. After the upcoming 2015 global agreement promoting the SDGs, perhaps more attention can be given by IEAG to develop appropriate strategies about how can developing-countries better promote and use data consistent with the aim of the data revolution, including respective governments interactions with civil societies and local intellects.

    Nedal Alomari

  108. The report has so many sections and repetitions. It should be more concise and have an index.

  109. It is not always clear in the sections that follow, particularly in the four key areas under the “Call to Action” whether what is outlined below are actions to support a broad data revolution or actions to harness the data revolution to meet the monitoring and accountability needs of the post-2015 agenda or both. This should be made clearer, particularly given the implications for the recommendations / suggested architecture proposed. There are risks associated with linking broad efforts / global structures on the data revolution too closely with the SDG monitoring and accountability framework (rather than a broader agenda), esp. given that “data revolution” needs and priorities (including national level data needs within and beyond the SDG framework) will vary considerably across countries.Reference

  110. The program of action has a lot to say about international coordination but does not say a lot about coordination at the national level. It would be good to unpack how the global architecture / actions listed below can actually play out at the country level – where a lot of the real action will take place – and in a way that supports country ownership, including ensuring that efforts are geared towards meeting national ‘data revolution’ priorities.Reference

  111. I am wondering if there is room in this report to refer to the risks associated with the data revolution going forward- not as they have been discussed above (personal information, etc.) but more in terms of potential risks activities/actions on the data revolution might bring. For example, over emphasis on technology and ‘sexy’ initiatives rather than much needed capacity, risks to country ownership on measurement/data collection priorities as we seek to measure the SDG framework, insufficient attention to ‘more difficult to measure’ areas, insufficient coordination at national level, etc. It would be good to recognize these risks from the get go as the international agenda / architecture for the data revolution is being established.Reference

  112. The World Resources Institute is grateful to the members of the IEAG for producing a solid draft report under extreme time constraints. We also appreciate the committee’s efforts to squeeze in a short period for public comment despite their near impossible time constraints. We feel the draft is an excellent foundation to build upon. We hope that the committee will be given sufficient time to complete the report, allowing them to draw on their own vast expertise, as well as the many constructive comments they have received during the short consultation window.

    A vision of the data revolution in 2030
    The data revolution really is a game-changer for every part of society. We propose capturing a sense of the promise it offers by opening the report with a one-page ‘vision’ that articulates how the world will look differently in 2030 for different stakeholders, if decisive action is taken now to harness its potential for the SDG process. We are happy to draft at short notice language for the committee to consider, if decides to include a vision. The “from-to” slide below can also help inform the vision.

    Line 6: What is the data revolution?
    Only the second half of the fifth paragraph actually addresses this question. We recommend adding text that highlights the following interconnected technological trends that underpin the data revolution (to the extent feasible, these trends should then be reflected in each section of the report):

    • Remote sensors, including satellites, airborne systems, and ground-based monitors are providing vast amounts of data about the Earth’s environment, resources, and people at increasingly higher spatial resolution and increasingly closer to “real time.”

    • Crowd sourcing using simple apps, phone cameras, and web interfaces is empowering millions of ordinary people to participate in environmental monitoring by sharing GPS-tagged data, maps, and photographs.

    • Cloud computing is enabling the collection, storage, and processing of immense volumes of data at a speed and affordability unimaginable just a few years ago.

    • Smartphones with expanding functionality are becoming ubiquitous in most places around the planet, allowing people to access, share, and contribute information wherever they are.

    • Social media is creating a flat, networked world in which information spreads quickly around the globe, communities with a common interest self-organize, and people mobilize.

    The slide below envisions what will change as a result of the data revolution.

    Lines 13 and 32
    Also need sharper focus, and broader consensus, on what we need to know (for example, definition and multiple dimensions of ‘poverty’ and implications for data needs; ‘inequality;’ dimensions of environmental sustainability in relation to economic and social development…).

    Related to point above, the exponentially increasing volume of data is both an opportunity and a challenge – making it all the more important to have greater clarity on what we are trying to understand and measure.

    Line 133 -136
    We suggest starting with a positive sentence that acknowledges the potential of the data revolution to create more equity. Instead of a few people knowing everything, open data platforms can help ensure that knowledge is shared among the masses, creating a world of informed and empowered citizens, capable of holding decision makers accountable for their actions. We agree that the points currently raised in this section are important and reflect current reality.

    Lines 158 to 175
    On role of governments, this has significant policy and institutional implications with respect to data/information gathering, access and use. This also applies to the multilateral system. [Note: This is addressed somewhat in Lines 455-508.]

    Line 243 The state of data.
    We suggest adding two more bullets to this section that a) acknowledge the critical role of open data in the data revolution (this would then set up your principle on data openness) and b) addresses the demand side of the equation (which would set up your principle on data usability and curation).

    a) Data that is not open. A critical first step to opening up data is to ensure that citizens have legal rights to access information, participate in decisions that affect them, and have access to justice when these rights are denied (the three Access rights – see for more information). Even in countries where citizens have been granted access rights, the poor may face specific barriers that prevent them from accessing data. These barriers include illiteracy, poor access to communication channels, and high costs of access (see for example: WRI 2010: A Seat at the Table: Foti and DeSilva). And even when data is made public it is often not in a useable form.

    Open data generates more value for more people, than closed data. Notwithstanding legitimate concerns around privacy, the default state for official data should always be open. The key to making open data accessible is through APIs that allows different applications to share data. People interact with apps, not with data. The US government, for example, now offers federal agencies an API management service called API.Data.Gov. The first principle of a National Data Policy is “openness,” and a Digital Services Playbook instructs US government agencies to “default to open.”

    b) Data that is supply driven with no demand-side strategy. Data needs to be generated with users in mind. Too often data providers underinvest in identifying and engaging those in a position to use data to drive action. Agencies with a mandate to collect public information are not always well-suited to ensuring their information is used by stakeholders. Civil society and the private sector can play a critical role in translating data into a form that is more readily useable. Access rights combined with requirements for open data and open API’s provide the foundation for unleashing the creativity of business and civil society to mobilize data in ways that drive action. Data creates information. Information drives transparency. Transparency drives accountability. And greater public and private sector accountability can drive more sustainable development Public and private actors are more likely to make environmentally and socially responsible choices when they know they are being watched.

    There is often more focus on making data available then turning data into useable form that can be used to make change happen. Let me provide you with an example from WRI. Aqueduct, WRI’s global water risk mapping platform ( translates publicly available water data from around the globe into high-resolution, interactive, map-based, water-risk information. Its multiple risk indicators cover 15,000 catchments globally, and comprise 50 years or more of historical data. This open map-based data platform facilitates overlays of other data such as drivers of water demand, flood risk, climate change information, agriculture data, etc. These data overlays break down traditional agency data silos, shining the spotlight on emerging risks at the intersection of individual agency’s boundaries e.g., water-energy; food-forest; agriculture-climate; forest-water, agriculture-water, city-water, etc.

    In addition to translating data into more usable forms, civil society can help build networks of users around open data platforms. Open data platforms combined with purpose-built partnerships, and user-specific APIs represent the next frontier of the data revolution. Taking two examples from our own experience, we would like to highlight Aqueduct and Global Forest Watch ( as prototypes of such open data platforms. Aqueduct has developed partnerships with major institutional investors and Fortune 500 companies who use its water risk data in investment analysis. Bloomberg, a leading investment information company, now includes Aqueduct maps on its subscription terminals, used by 300,000 globally. China’s Ministry of Water Resources and National Energy Administration is working with WRI to develop a detailed China Water Risk Atlas that will be open to all users and help to develop policies that guide China toward a water-secure future through sustainable use and development.

    Global Forest Watch (GFW) unites satellite technology, social media, open data, and crowdsourcing to guarantee access to timely and reliable information about forests. The World Resources Institute and partners, including Google and UNEP, have designed innovative algorithms to process billions of pixels of satellite imagery to identify deforestation in near-real time, making it available to users on an easy to understand platform. GFW data is being integrated into operational decisions of governmental, advocacy, research, and businesses. The simple, web based maps are the result of a massive data mining effort. And the initiative is growing. GFW will soon feature expanded crowdsourcing of forest clearing activity, a dynamic mobile application, and many other features to remain at the very forefront of technology in service of global forests and the climate. Such effective partnerships, which provide data on-line and for free, can be established in a wide variety of issues/sectors.

    Line 399: Data for Now
    We suggest adding an example box of the data revolution to illustrate the importance of timely data in supporting decision making. An example is provided below.

    Box: Forest fires in Indonesia
    Indonesian authorities estimated that 50,000 people in Sumatra suffered from respiratory illness as a result of forest fires in March 2014. Several major cities were effectively closed for weeks. The environmental impacts were equally severe, with valuable forest and peat land burned, contributing significantly to Indonesia’s greenhouse gas emissions. The immediate availability of free forest fire data on WRI’s Global Forest Watch site (GFW) enabled companies—Asia Pulp and Paper Group (APP) and Asia Pacific Resources Limited (APRIL), Indonesia’s two largest pulp and paper producers—to evaluate daily where their limited resources are best deployed to respond to fires on lands they are responsible for. The governments of Singapore and Indonesia also used GFW Fire’s ultra‐high resolution imagery, available through a partnership with Digital Globe, to crack down on illegal burning by companies. And GFW Fires, combined with the Indonesian Government’s Karhutla Forest Fires Monitoring System, enabled firefighters to reduce response time from 36 hours to 4 hours.

    Line 350
    Enhancing the value and use of household surveys is really critical. Lines 362-365 could be expanded somewhat by making reference to tapping mobile phone technology and social media/crowdsourcing.

    Line 565 Principles and standards
    We recommend explicitly calling out the importance of open data and open API standards and setting the default presumption around data as open (this is in fact what the World Bank did with its highly successful open data effort).

    Line 573
    We recommend adding a mention of the importance of building on existing international and national efforts to facilitate coordination and information exchange, like GEOSS and others. In this and other cases in the draft, there are existing efforts, which should be leveraged.

    Line 605-606 Introduction of principles for harnessing data revolution.
    It is great to see the articulation of these principles. Our only concern is that ten principles start to feel too long. We suggest a more effective presentation would be to combine the following two linked principles 4 and 5 (Data transparency and data openness) into one principle on “access to data rights.” The narrative should explicitly call out the importance of freedom of information and public right to know laws that are proactively implemented. We know from our Environmental Democracy Index, which measures the quality of law and practice related to access rights in 70 countries (using indicators developed under the framework of the internationally recognized UNEP Bali Guidelines on Principle 10) that access to information is still a huge challenge. [note: We can provide more information on this EDI initiative if needed. ]

    You could also combine principles 1 and 3 (data quality and timeliness). Timeliness is a key part of quality.

    One possible additional principle – Data for more integrated decision-making (as illustrated by the earlier Aqueduct example).

    Line 716
    It is not clear how this relates to already present efforts of the UN on capacity building for national statistics.

    Line 706
    We feel it is worth pointing out that innovation of data and data tools can stem from bottom up (e.g. national statistics) and top down (e.g. GFW) approaches. Both efforts need to be supported. However, the implications are very different. A top-down data product (like GFW), for example, can supply global consistent, relevant information that is immediately accessible to all countries.

    Line 797
    We suggest being more concrete on what these short wins will lead to in the end. On the longer term, we should aim for a global SDG data platform that promotes openness, and standardization and invests in a demand-side strategy that connects data with users. This effort should also build and integrate relevant efforts of other UN work.

    Line 809 SDG analysis and visualization platform and ‘dashboard’ on the state of the world
    We are not sure what this recommendation entails and how this differs from the SDG monitoring. How does this relate to the Rio+20 decision to have a Global Sustainability Report every 2 years? Would this be an online complement to the GSR – providing an online platform with more regular updates?

    Line 822 Need for continuation of public opinion measurements/perception data
    We are glad to see a mention of perception data here. We think more could be said about the value of such data. There is a huge opportunity for citizens (rather than just private sector) to share their views and perspectives of what is important to them. We now have the experience of MyWorld as a public opinion baseline, which has reached 5 million people. It would be great to see investment in democratic public engagements like this and to find ways that channel the engagement towards the SDGs, once they are approved. This could be through a public-private partnership undertaking that includes principals of national statistics offices, businesses (mobile phone companies, Gallup etc.), and civil society. With the prevalence of social media, smart phones, internet connectivity, affordability of cloud computing, and the ability to crowd source data, giving people a voice can now be done quickly and at relatively low cost. Adapting to such approaches will no doubt require a change in culture in institutions, but the resulting prize may be huge – more transparent, accountable, and citizen orientated governance of public resources.

    Final note and offer of assistance
    WRI stands ready to assist IEAG and the intergovernmental UN Statistical Committee in developing a set of indicators for the SDG’s that take advantage of the data revolution, including the role of civil society in building open data platforms and networks of partners to translate, combine, analyze and complement official data with data from other sources, including the crowd.

    Together with its partners, WRI is expanding its initial open data platforms (water and forests) into a World Resources Watch (WRW) that will include interlinked open data platforms for food, climate, energy, and cities. These platforms are directly relevant to the proposed dashboard on the “state of the world.” WRW will unite technology, transparency, and human networks to drive more sustainable management of the planet’s resources. WRW will leverage advanced monitoring systems, data (historic, near-real-time, and projections), complementary imagery and maps, cloud computing, mobile technology, and a networked world to create radical transparency on what is happening to the world’s resources―both faraway and right at home. Data will be quality controlled and methodologies peer-reviewed and made publicly available. We believe such transparency will empower civil society, NGOs, the media, and progressive public and private sector leaders to take action to conserve and sustainably manage resources at a pace that matches the modern world.

    We would welcome an opportunity to share with IEAG our experience building civil society led open data platforms for collaboration and action. We are also willing to convene our partners and others to provide input to IEAG on the key design parameters of SDG indicators.

    Thank you very much for the opportunity to comment.

  113. It is welcome that the IEAG recognises these risks – but they could be made a little more clear here and lead to stronger qualifications throughout the text. This is especially important given that the IEAG has chosen to highlight NSOs as the guardians of data. Independent, transparent and impartial NSOs can play such a role, but these characteristics are often absent. When NSOs are used for political ends and lose their independence their proposed role at the centre of the data revolution becomes a huge risk for people.Reference

  114. The following response is from the International Movement ATD Fourth World. It is a follow-up to previous inputs sent with CAFOD as well as Beyond 2015 and Participate:

    As previously explained, ATD Fourth World views citizen participation as a critical component of the data revolution. We have carried out participatory research with people living in poverty in efforts to evaluate and monitor the implementation of the MDGs, some of the outcomes of this work can be found here:
    To this end, the report makes some important references such as:

    Lines 81, 85-87: Suggesting that previously unquantifiable factors like human rights could be quantified through the data revolution. CAVEAT: this seems to imply that qualitative data cannot be used in sustainable development monitoring, which we hope is not the intended message of the EAG

    Line 490: Emphasizing citizen participation

    Line 498: Recognizing the role of civil society in helping citizens generate data

    Line 673: Emphasizing the right to be counted, and the right to participation

    Line 822: Mentioning citizen-generated data

    In our experience, the general lack of interest in citizen-generated data has made it difficult to a) garner resources and capacities for this kind of work and b) build a policy environment that consults this kind of innovative data during the policy-making process. And, we believe that the EAG can help visualize a data revolution that responds to these shortcomings.

    However we are disappointed that in this draft, the useful references mentioned above appear more as cursory mentions rather than a conceptualization of the citizen as a critical producer of data. Almost every time that data literacy, innovation, transparency, and openness are discussed, the underlying assumption is that citizens will be utilizing this data and thus their access to the data has to be ensured carefully. Although this is certainly true, the assumption fails to recognize citizen agency and power as data producers, and the work of civil society (particularly NGOs) in building capacity and collecting this data as partners in sustainable development monitoring and evaluation.

    The language suggestions below are attempts to thrust citizen-generated data into the foundation of the data revolution:

    Line 490: ‘infomediaries’, ensuring that all people have capacity to [add: produce] and evaluate the quality of data and use…

    Line 593: …individuals who can [add: produce,] access and use data…

    Line 675: participation [add: in the production and analysis of data], the right to non-discrimination and equality.

    Line 630: …analysis. [add: To this end, citizens and civil society should be encouraged to collect data on public spending, particularly regarding local and national level implementation of policies related to sustainable development.]

    Line 706: enable the creation of data [add: collection and] dissemination policies that [add: encourage participatory monitoring and accountability while supporting] [delete: will support] rational responses by citizens.

    Line 709: analytics tools [add: and methods] to better evaluate [add: citizen perspectives and other] long-term trends affecting sustainable development…

    Line 753: people able to benefit from [add: and produce] data and those who cannot…

    We hope that these recommendations can help the EAG strengthen the data revolution’s capacity to harness citizen-led data collection for sustainable development monitoring. By including this kind of data, as well as qualitative data describing citizens’ perspectives, the data revolution can begin to tap into the wealth of knowledge held by people living in poverty and other citizens who are most directly impacted by sustainable development policies.

    For further questions contact: Fabio Palacio at

  115. While you perhaps allude to it in the first line, I think a clear sentence or two on the political barriers to freeing data would be helpful here. Many governments won’t allow NSOs to collect or publish data on potentially sensitive issues. It is often politics rather than technical barriers which have prevented on measurement of issues which have now been agreed in the OWG for inclusion in the SDGs – such as levels of violence, participation or access to justice. The IAEG needs to take this opportunity to challenge governments to free this information on the state of societies into society’s hands.Reference

  116. I’d expand your list of examples of dimensions to include ethnicity, religion. Identity-based inequalities such as these need to be addressed in the SDGS.

    While there are clear challenges with disaggregation – and there are limits on how far we can go – I would add an extra line (and possibly an example) which stresses that significant disaggregation is far more possible and achievable than is perceived. This is an area of data where we need show real ambition.Reference

  117. Agree with Hoffman’s observation & suggestion.
    And would describe it as an institutional failure to cope with buoyance in the IT marketReference

  118. Dear Ms. Melamed,

    thank you for this draft report and for giving me the opportunity to comment on this report as an individual.

    General comments
    As I wrote in my initial submission I feel that it is important to highlight that data does not equal information and does not equal meaningful decision support and that capacity building is vital. While this is reflected in the later sections of the report (lines 238-240, 379-382, 467,487-491, 639-643, 656-660,662-669, 717-723, 752-756) it is missing in other sections, especially in Chapter 1.

    In my opinion, the data revolution should, as you invoke in your first chapter “leave noone behind” by making sure that information derived from data is available to a non-technical audience. The data revolution should not only be directed at technical experts, but should also aspire to achieve that people with only basic ICT literacy can harness the benefits of the data revolution. Positive examples exist, e.g. .
    To “leave noone behind” you might want to look at the responses you received, see from which group of stakeholders or nations you did not get any feedback and think about how you can make sure that in 2030 a follow-up survey will engage these stakeholders, too.

    For a UN document I feel that this report could benefit greatly from clearer language: For example: poorer and richer countries are very fuzzy terms.

    Section 3 Table 1 is in my opinion one of the most important parts of the text. As boxes might receive less attention from readers who quickly skim through a report, I feel it should be given more prominence in the text. In general the section on recommendations should ideally be more prominent as well as more concise.

    The report could benefit from a separate section with best practice examples.

    Text specific comments:
    Line 16-17: It is not only about “expanding” people’s ability to use data : in some countries and stakeholder groups it’s about building basic ICT literacy and skills to use the wealth of data available.

    Line 39-41: This phenomena is not uniformly distributed over the globe and across different groups of stakeholders. I feel this should be made clear at this point in the text.

    Line 134-136: This is being discussed in the UN. It might be helpful to look at ongoing conversations on this topic and use similar terminology, e.g. the “digital divide” and ICT literacy to connect to those ongoing conversations. (see e.g.: )

    Line 157: For the figure by the World Economic Forum: If using categories of countries that are not usually used in the UN, it would be important to provide a definition. Also they should be consistent throughout the report

    Line 222-223: Maybe add a sentence that data should be considered as cross-cutting element throughout the SDGs and the Post-2015 development agenda.

    Line 328-348: While this paragraph touches on the importance of making data useable, it does not expand on this enough, in my opinion. Data needs to be easily accessible and useable through free open source applications with high user-friendliness and a graphical user interface.

    Line 379-382: I fully agree. I feel you should streamline this thought more throughout the report.

    Line 639-643: This paragraph gives the impression that only information intermediaries should be able to translate raw data into something that is useful for non-technical users (inconsistent with Line 379-382). Who are those information intermediaries? In my opinion this needs to explicitly include relevant national ministries and governments, as later reflected in line 656-660 and 662-669:

    Line 688: When introducing a new term, such as data innovation, it should be explained.

    Line 715: Add a paragraph: NINE could also monitor how well these innovations are translated into actual decision-making support for sustainable development, i.e. user-friendly information and tools for non-technical experts.

    Daniel Kachelriess

  119. There’s a missed opportunity here to assert that the data revolution ultimately can (and should) contribute to strengthening individual rights and fostering democratic institutions.Reference

  120. The overemphasis on government-multilateral collaboration at the expense of mobilizing domestic constituencies among civil society and the private sector is unnecessarily limiting and unsustainable. Yes, governments bear a responsibility, but the means by which gaps are filled are likely to be in partnership with other sectors.

    Suggest this revised language: “If there are gaps in the mobilization of new opportunities for the public interest, governments have a responsibility to work with international and domestic actors to ensure those gaps are filled, at all levels.”Reference

  121. The issue of data quality here is insufficiently fleshed out to be useful in unpacking the problem and proposing a sufficient solution. The post-2015 data test presented a useful starting framework for Data Quality that would be a useful departure point to include here. The main components of data quality were articulated as: relevance, accuracy and reliability, timeliness and punctuality, accessibility and clarity, coherence and comparability.

    Suggest revising language: “However, too many countries still have data of insufficient quality to be useful in making decisions, holding governments to account or fostering innovation. Good quality data is relevant, accurate, timely, accessible and comparable. Unfortunately, far too few countries have data that doesn’t do much for them. “Reference

    • It might be worth including a definition for what is meant by “good quality data” either here or earlier in the report when first mentioned.

  122. Dear Ms. Melamed, and IEAG Secretariat,

    I am submitting these comments in my personal capacity.

    First of all thank you very much for the work you have done so far in drafting this report, and taking public comments on board.

    While this draft contains all main relevant elements and important recommendations, there are some points that should be made clearer / reflected more prominently in general:
    – The report could benefit from a clearer structure and more concise language. The section on recommendations should ideally be more prominent and the current status and capacity needs should be introduced upfront.

    – Section 3 – Table 1 “Basic Principles for Data Revolution for Sustainable Development” – This should not be a simple table, but a section on its own, as it bears important insights that bridge the gap between the current status and recommendations. These should be supported by best practice examples.

    – The need for capacity building and increasing ICT literacy (716-758) should already be presented in the beginning as a pre-requisite for realizing the benefits of a data revolution for sustainable development

    – The draft would benefit from greater conceptual clarity and more consistent use of terminology; e.g. there should be a clear definition what is meant by “richer/poorer countries” (lines 130-132, 147) or “advanced countries” (figure above line 157 from World Economic Forum). It would be advisable to stick to UN-agreed language and definitions.

    – Data does not equal information and does not equal decision support: while in part reflected in later sections of the report (lines 238-240, 467, 488, 639-643, 752-756), it is completely missing from section 1 and lines 328-348.

    – “Leave no one behind” (line 28): this sentiment is essential and should be streamlined throughout the report. Information should be understandable and available to a non-technical audience.

    – The report reads and sounds very much written from more developed country perspective; it would be recommended to revisit the comments from the public consultation to see whether there are more comments from LDCs, LLDCs, SIDS and other that could be taken on board. While there is a lot of data available, it is not equally distributed across the globe, but mainly in developed countries

    Specific comments:

    Line 7: Data should be the lifeblood of decision-making, but are not yet. This is a more aspirational objective than a given.

    Line 16: Here you could weave in the need to build capacity to create decision-making support prior to talking about expanding people’s ability to use data.

    Lines 32-41: Yes, but please emphasize that this data is not equally distributed across the globe – there is still a global digital divide present. Especially lines 39-41 differ very much from country to country.

    Lines 134-136: Digital divide – this and similar topics are currently being discussed in the UN, might be helpful to look at ongoing conversations (e.g. Information and communications technologies for development, agenda item 16 of 69th GA session)

    Lines 158-167: (The need for) Support of international community should come out much stronger here. In many parts of the world, governments alone won’t be able to fill those gaps, or balance public and private interests etc. Civil Society and parliaments are also essential in supporting governments to fulfil these tasks.

    Lines 175: very vague, please elaborate further

    Lines 222-223: A sentence could be added that data should be integrated in a cross-cutting manner throughout the SDGs.

    Lines 238-240: Important point.

    Lines 328-348: This box should expand more on the importance of making data usable. Data does not only need to be openly accessible, but be able to be accessed via user-friendly open source applications with a graphical interface (e.g. gapminder).

    Lines 379-381: Yes, but how could this cultural shift take place? As mentioned above, data needs to become accessible in a way that it is nice-to-use for everyone with basic ICT literacy skills, without additional efforts.

    Lines 487-491: Essential paragraph.

    Lines 639-643: Very important, but needs definition who these information intermediaries are – governments should be included here. Tools that need to be developed will require graphical user interface (again, gapminder is a good example)

    Lines 655-669: Given that this is emphasized in operative part of the report, it should also be adequately reflected in pre-ambulatory section.

    Lines 671-677: What about people who are stateless?

    Line 688: Please define what you mean by “data innovation”.

    Lines 712-714: Add sentence that the NINE should also ensure or monitor that and how well data innovations are translated into actual decision-making for sustainable development, i.e. user-friendly information and tools

    Lines 726-727: Third International Financing for Development Conference

    Lines 745-750: NSDS – please clarify term and definition (could cause confusion with National Sustainable Development Strategies)

    Line 762: please define “extremely concrete”

    Line 773: “global partnership” – given the existing global partnership for development , maybe a different name would be advisable

    Lines 790-795: maybe partnership of UN with google to set up world statistics cloud (mentioned in 702-703)

  123. Glad to see the emphasis on disaggregated data and particularly on subnational data. This is critical in our view. You could provide further rationale.

    Suggest the following added language: “Maximizing the use of resources to foster progress towards the sustainable development goals is limited if we can’t account for subnational variation and target assistance to areas of greatest need and opportunity. We need more granular, subnational information on both outcomes of, and funding for, development.”Reference

  124. Re: lines 328-339: There is an assumption that theoretical access is the same as awareness, confidence and desire to use data. That isn’t necessarily the case and if we want this to change, you need to be more explicit.

    Suggest the following revised language: “Data must be of high quality if people are to effectively put this to use to further their goals. Beyond quality, data must be accessible – publicly available, relevant, easily visualized and analyzed – to those who want or need to use it. Finally, even high quality, accessible data won’t have impact if efforts aren’t made to ensure that prospective users are aware it exists and have the will and capacity to put it to use.”Reference

  125. Re: lines 340-341: Technical and legal barriers don’t cover the full scope of barriers to use. Political and socio-cultural barriers are also cause for concern.

    Suggest the following revised language: “Access is often restricted behind technical, legal, political and/or socio-cultural barriers that prevent or limit effective use of data. Political barriers are present if government officials are concerned they lack space to accurately report on progress and failure or if citizens and civil society are concerned about retribution for drawing attention to waste and fraud. Socio-cultural barriers may exist that curb the ability of disadvantaged groups to access and use data or if there is limited data literacy and value for evidence-based policy making.”Reference

  126. Re: lines 399-410: The emphasis on real-time data for now is good, but it assumes that the same type of data is equally valuable for all purposes. This is where looking at a variety of use cases is critical. In the world of aid transparency, for example, researchers interested in evaluating the impacts of aid on poverty alleviation or disease eradication are interested in time series statistical data that has been quality assured to a high degree of validity. Whereas, citizens interested in capturing a snapshot of what is going on with a particular project or area may emphasize real-time data and not be as concerned about validity.

    Suggest the following revised language: “Data for multiple uses. If data is to be useful and support good decision making, it has to be well suited to the different ways that people want to use it. There is a need for both quality assured, statistical data that researchers and policymakers can use to assess the impact of the development efforts over time, as well as real-time qualitative and quantitative data that citizens and policymakers can use to support decision making now.”Reference

  127. Re: lines 457-463: The government responsibility to create and use good data goes far beyond statistical offices — actors from executive-level offices to small local governments are responsible for creating, disseminating and using good data.

    Suggested the following added language: “Governments must set expectations that all of their actors, from central to local levels, will generate high-quality data, disseminate it appropriately and use it to inform planning and policy.”Reference

  128. Re: lines 487-491: There is an assumption here that capacity is the only issue in play here. Awareness-raising and creating perceived value for open data is also key.

    Suggest the following revised language: “Governments, civil society, academia and the philanthropic sector work together to raise awareness and the perceived value of publicly available development data, strengthen the data and statistical literacy (“numeracy) of citizens, the media, and other infomediaries, ensuring that all people have capacity to evaluate the quality of data and use them for their own decisions, as well as to fully participate in initiatives to foster citizenship in the information age.”Reference

  129. Re: lines 571-572: There seems to be an assumption here that there are no previous global agreements around data standards, but there has been some headway in global standard setting in official development assistance such as the OECD’s Creditor Reporting System, the International Aid Transparency Initiative, among others. Not taking these initiatives into account is a missed opportunity to build upon them and raises the possibility of duplicated efforts.

    Suggested revised language as follows: “…that the UN develop a comprehensive strategy and roadmap towards a new Global Consensus on Data, that builds upon existing efforts in other domains such as the International Aid Transparency Initiative, the Open Contracting Data Standard, EITI and the OECD’s Creditor Reporting System, setting principles and agreeing to standards to build trust and enable cooperation…”Reference

  130. Re: lines 687-714: Documenting and disseminating best practices is good, but what are the incentives for uptake? Could this network of networks have some sort of index or ranking of countries/donors’ progress on data quality, quantity and timeliness as means to create incentives to invest in this? In essence, this is one of the reasons that the International Aid Transparency Initiative has gained so much traction. Another factor to consider is what is the appropriate level at which to have these networks? International networks would facilitate best practice sharing and lessons learned across multiple contexts, but for the data revolution to truly take root, there most likely needs to be thriving communities of practice at a national and local level.

    Suggested added language: “Comparatively evaluate and report on the progress of countries and donors on data quality, quantity and timeliness as part of an annual index or report card to incentivize improvement. Create monetary annual awards (e.g., the Data Revolution Achievement Prize) for the highest performers and most improved which can be invested into taking their data quality efforts to the next level. Foster the development of communities of practice at the national and international levels to share lessons learned and best practices around catalyzing the data revolution.”Reference

  131. Re: line 708: We should not overlook the role of governments in creating public-good tools that help people access and understand data.

    Suggested revised language: “Engage research centres, innovators and governments….”Reference

  132. Re: lines 725-758: The role of south-south cooperation, remittances and foreign direct investment is little explored in your discussions of funding to support the data revolution and SDGs. This is a missed opportunity. Also, there is already a concern as to whether existing resources for development are being used effectively; hence, discussions in the recent post-2015 data test workshop related to absorptive capacity and capital flight. There is an assumption that a massive education program will resolve the data literacy issue, but this only works if this is well-integrated into existing efforts that puts data literacy into context, such as job skills training, capacity building for civil society and the media, civic education, etc.

    Suggested revised language: “A proposal should be developed to mobilize new funding to support the data revolution for sustainable development and ensure that existing funding is being used most effectively.”

    Would also recommend some additional language: “A proposal to assess the effect and potential of resources beyond official development assistance, such as remittances, foreign direct investment and south-south cooperation, to support or detract from the sustainable development goals and the data revolution.”Reference

  133. Distributed governance is needed to enjoy the leverage and risk reduction that is possible with distributed information systems.Reference

  134. Re: lines 760-780: It is unclear that it is helpful to have data siloed into its own global partnership and global forum, rather than mainstreamed as a cross-cutting theme through as many other development fora as possible. Is global the right level for this? What about national? Ex. of UNDP national level consultation. While comparability and common standards are important, so is local ownership.

    Suggested revised language: “The establishment of a biannual World Forum on Sustainable Development Data and associated regional and country-level events.”Reference

  135. Data rights arejust empty words without corresponding and enforceable data duties being placed on other stakeholders. To provide rights at large scale duties will need to be imposed and enforced on multiple stakeholders. In such a large, complex and dynamic ecosystem, identifying both the means and the legal authority to impose “data duties” needs to be more fully addressed.Reference

  136. Re: lines 780-784: When it comes to fostering conversation between data providers and data users, a global forum isn’t sufficient because the users of global data are likely to be quite different than the users of national/subnational data. There needs to be resources for, and expectation of, country-level discussions/dialogues. Our work also suggests that fostering government “champions” of data uptake — senior government officials that can promote a culture of data use — will be a critical component of any government-wide strategy to improve data use for planning and policy-making. Suggest that you also use this event as an opportunity to convene, highlight and sustain networks of “data champions” from governments around the world.Reference

  137. Re: lines 790-795: There is little emphasis on troubleshooting and navigating data production, quality assurance issues. This has been a real challenge with current efforts around the implementation of the IATI standard and will likely be a similar challenge for the SDGs. This could be a natural area of fit for public-private partnerships with both the private and civil society sectors.

    Suggested revised language: “Broker some key global public-private partnerships with private companies and civil society organizations around data production, data sharing and quality assurance.”Reference

  138. Re: lines 797-808: Where does the SDGs data lab fit institutionally? Is this envisioned as part of the UN system or as an independent body?Reference

  139. To quote Scott David of the University of Washington…”The report perpetuates the common institutionalized habit of mixing the concepts of data and information. This has the result of inadvertently eliminating an entire category of solutions from consideration that are made possible by division of the concepts, and division of their respective social governance and economic market” structures, to allow for separate, but integrated, rules development.

    Elinor Ostrom (Economics Nobel prize winner in 2009), cautioned against resisting “complex” interaction structures to map “complex” social/economic structures. The division of data from information might initially seem to increase complexity, but it also better captures functional and economic reality, and accords with information theory, providing practical and theoretical support for the recognition of the division.Reference

  140. Re: lines 809-815: A visualization platform is good, especially if it could signal the potential for what highly granular development indicator and finance data can reveal. The only concern is that an international platform would serve a different purpose and different users than a national-level platform. For example, AidData has an international platform – – that serves users that want to be able to compare different types of financial flows or compare trends across countries and sectors. Whereas, through our institutional partner, Development Gateway, we are also supporting making national level aid management platforms publicly accessible and more able to be disaggregated at a subnational level. These platforms serve different constituencies, teach us different things about the challenges of making data accessible and useable.

    Suggested revised language: “Develop an SDGs analysis and visualization platform at an international level to launch in September 2015, either as a stand-alone effort or possibly integrated with an existing platform focusing on development finance. Support parallel national-level pilot projects in several countries to incubate learning and experimentation about optimizing data visualization and analysis to support the needs of different users.”Reference

  141. Yes, agree with Shannon’s point on this. In addition to some discussion of risks and unintended consequences, also useful to have a frank discussion of likely costs/benefits we can foresee.Reference

  142. Re: lines 816-823: Great idea, in implementation though, need to avoid duplication of efforts. Perhaps look for natural partnerships with existing efforts both within and outside of the UN system.Reference

    • Re. lines 816-823, while a dashboard can make disparate datasets more comprehensible, accessible and shareable, a standalone dashboard will privilege existing data “wranglers” and journalists over less familiar, lower-resourced users. It would be better to consider the dashboard as part of an “SDGs data lab,” and to include in the vision the advice and human resources necessary to help dashboard users turn the data available within the dashboard into program-specific actionable knowledge through a minimum required amount of support to be made available as part of the core “SDG lab” offer.

  143. Continuing with the quote of Scott David…We are squandering solution space if we assume that data and information are the same thing. We cannot afford to continue to perpetuate this lack of definitional clarity in developing policy; we need to enable as many avenues for policy and technical architectural solutions as possible. It is worthwhile to re-examine the assumptions through the slightly more articulated paradigm that recognizes a difference between data and information, which enables policy/legal distinctions and economic structures to be crafted with greater dimensionality, enabling greater flexibility to address challenges. Data and information are distinctive in information theory, and should be evaluated as distinctive to increase solution “phase space.”
    The distinction is important, because the value of “big data” is clearly not just about mingling data with other data (although it is certainly about that), but it is also about putting data into new contexts and introducing subjective observer elements so that it can become valuableReference

  144. Thanks to he DRG for this report. It is an important step in fleshing out the “data revolution” as a resource for assisting and monitoring progress on the SDGs. Here are three points, partly covered in other comments. Firstly, data of itself is a blooming, buzzing confusion. Educated intelligence is required to shape data into useable and focussed information, through distilling, comparing, drawing trends, insightful graphing, etc. The report seems to me to pay inadequate attention to the importance and difficulty of achieving this essential value-add, beyond asides such as helping “’information intermediaries’ …to develop new tools that can translate raw data into something meaningful’.
    Indeed, even this aside is misleading. The “tools” are already abundant, on PCs and even smartphones. Tools don’t “translate”. It is the capability to apply the tools discerningly – whether in the strategy units of government departments, national statistical offices, civil society organisations, or academic institutions – that is generally in short supply, especially (but not only) in developing countries. And given the mix of statistical and communicative expertise involved, it is a deficit that doesn’t remedy rapidly. If the data-divide is widening, as the report notes, because of continuing under-investment in developing countries compared to developed countries, the width of the information chasm is surely accelerating. This means that indigenous evidence-based policy-making, from above or below, too often remains at the mercy of foreign development experts with implicit knowledge frameworks that, even if sincere, are often exploitative in effect.
    What is to be done? The report offers several tantalising “boxes”. A later draft could well “lift” and link these intimations of process, in comparison to the precepts – thorough but well-worn – about data quality, independence, dissemination, etc. For instance, there are the emerging possibilities and the system requirements of harnessing affordable and appropriate technology to new curricula and more open modes of learning, so that young people and life-long learners alike – especially in disadvantaged contexts – can appropriate, and (as one “box” illustrates) indeed help to produce, not only the data but also the purposeful information about themselves that they can apply to change their own lives.

  145. Per scott David of the Univeristy of Washington: People are data “producers,” not just “consumers” in networked information systems. Much of the most valuable feedstock of networked information systems is provided by individuals.

    The rights of individuals in data and information systems are currently broadly conceived of in their role as consumers (see both compulsory laws and regulations such as FTC, GLB, HIPAA, EU, and various private terms of service and terms of use of large social network and online service providers), but people are also important data producers in information systems, and deserve rules and institutional structures to reflect that role.

    The absence of that institutional construct and governance matching with actual valuable data and information system flows, retards information system reliability and sustainable growth.Reference

  146. We need rules/institutions built with the goal of recognizing people as big data producers in big data systems, not just as consumers. Those institutions are entirely absent worldwide, which is why big data agendas are set by big data consumers (not producers). Individuals lack leverage and are faced with a take-it-or-leave-it value proposition. We take it because of the leverage and risk reduction, but the options are not necessarily reflective of agenda that individuals would proffer if they had control of the first draft of the term sheet. We cannot be confident that development agenda reflect individual needs until individuals are themselves able to express those needs.Reference

  147. From an architecture perspective, I think you need to note that with the SDGS, there is considerable recognition that ensuring national engagement, buy-in and space is critical. Suggested text for a final sentence: “The importance of connecting global efforts with national ambitions and realities should not be forgotten.”Reference

  148. Suggest being clearer about the importance of building capacity at the national level. Suggested edited text: “A proposal to improve existing arrangements for fostering the necessary coordination, capacity development and technology transfer at the national level. This should include upgrading the ‘National Strategies for the Development of Statistics’ (NSDS) to do better at coordinated and long-term planning, and identify sound investments; engaging non-official data producers in a cooperative effort to speed up the production, dissemination and use of data, supporting heightened coordination between different stakeholders at the national level to foster communities of national level “data revolutionaries” and collaboration and training a new generation of leaders (especially in national statistical systems) for the new world of data.Reference

  149. Thank you for this excellent and perceptive draft report, and I appreciate the opportunity to comment.

    Lines 607 to 677

    I feel that this section conflates properties of the data per se, with properties of the data delivery mechanism or dissemination platform.

    To be specific, the following are strictly properties of the data: quality and integrity; disaggregation (aka granularity); timeliness; transparency; legal openness; protection of privacy. These properties of a particular dataset persist no matter what “form” the dataset is published in, or by what mechanism the dataset is actually delivered to the end-user. Accurate data is accurate data, whether it’s delivered by internet or by smoke signal. Meanwhile, inaccurate or obsolete or opaque data cannot be improved merely by making it easily available.

    (Other desirable properties of data qua data that are not mentioned in this section of the draft include overall organization, structure in metadata, internal consistency, verifiability and of course documentation)

    Conversely, the following are strictly properties of the data delivery mechanism: technical openness; usability and curation; governance and independence; resources and capacity; usage rights. These properties adhere to all datasets published using a given platform, and are independent of the quality of any individual dataset.

    (Other desirable properties of a data delivery mechanism that are not mentioned in this section of the draft include acquisition and dissemination efficiency, reliability, scalability, interoperability with other systems, ease of use for both humans and machines, and data findability).

    This distinction between “data” and “platform” is especially relevant given some of the other suggestions in the draft; for instance, the proposal in line 700-704 to develop a common data infrastructure. Defining the properties of such an infrastructure is, in my opinion, crucial. Which brings me to my second comment:

    Lines 700-704 and lines 740-744

    When discussing the development of a common data infrastructure, especially given the subsequent mention of contributions by the private sector, I think it is important not to “reinvent the wheel”. There exist a number of interesting private-sector data initiatives that address the very problems identified in the draft, often on terms that align perfectly with the SDGs.

    For example, the company I work for, Quandl ( has already built a “grand unified platform” for open public data. Quandl currently unifies data from 50+ international organizations, 100+ national statistical agencies, 50+ think tanks and universities, 25+ central banks, and 100s of smaller data publishers, on a single platform. All data is published under a fully open and unrestricted license; there are simple and easy-to-use search, browse and visualization tools for non-technical users, plus a full-featured API for technical users and machine access; integrations with 25 different libraries; a fast and reliable update mechanism; and more. In other words, Quandl already ticks all the boxes for a “world statistics cloud” as hypothesized in line 702. And Quandl’s uptake proves this: over 100,000 data users currently download over 4 million datasets from Quandl every month, encompassing over 100 billion data points. Could this be a template for further progress on the public-private partnership front?

  150. I think this opening statement is weak. Data is important, but first paragraph should speak about the context for this report and its overall goal. Data is important but what is stopping us for making it more actionable.Reference

  151. This needs to be specific. The problem is that we dont have data that correctly represents or speaks about the interconnectivity of social, environmental, and economic factors for development. Clear example: we dont measure efficiency for the use of resources.Reference

  152. New technologies are important. However, we need to be honest about this. Yes there is more data, but the access to it is not equal. The graph with the mobile subscription numbers is misleading. Having mobile subscription does not equal to having access to the internet. If we take some countries in Africa, the cost of data plan is huge. So even if they have access to the internet, they wont spend their limited data in researching national surveys. Would be more useful to look at free access points like public access computers. Secondly, a technology by itself doesnt provide a solution. Some people dont even know how to browse the web. Consequently to truly achieve a data revolution, people (every day citizens) must have the skills to use these technologies and be data literate.Reference

  153. I would suggest emphasizing the benefits of vital statistics from civil registration in terms of localization and disaggregation of the data by adding “These data help to identify inequalities in access to services and differences in outcomes… **because, unlike most other sources of the same vital statistics, the data from civil registration can be localized and disaggregated in a variety of ways, particularly by age and sex, which will be critical for ‘leaving no one behind’.”Reference

  154. I suggest making the link with legal identity:

    Millions of people in low- and middle-income countries are **without a legal identity and** denied basic services and protection of their rights because of deficientReference

  155. Agree that this paragraph reads contradictory — change is happening implies that there are some positive things taking place to facilitate the transition to a data revolution.Reference

  156. support suggestions aboveReference

  157. I cant follow this stement. What is the second problem to be addressed. Mentioned in here so we can follow in text below.Reference

  158. Planet People seems like a confusing name here. Why not keep it Data Revolution.Reference

  159. There should be specific — or more specific agreed about disaggregation. What categories does the panel thinks are the most effective ones?Reference

  160. Would mention libraries and government centers are places and institutions that can help in the process of making info available. They are also considered infomediaries.Reference

  161. Suggestion: add a section on challenges for the data revolution. what are the main challenges that need to be addressed? what is needed? what risks need to be addressed?

    Also: ending should be a call for action or ask for the supporters of the data revolution.Reference

  162. I agree on the relevance of governments. I think it would be good to have a stronger emphasis on the international institutions on the governance of data. In many cases, corporations that have very relevant data are bigger and more powerful than countries.
    I think it would be important to emphasize the need for an international institution to really set the standards below of transparency, privacy, etc.Reference

  163. In addition to the comments on political barriers and the need for independence, I think it is important to note on the economic interests involved with data. I think it needs to be clearly stated that there are economic interests in maintaing information unaccesible. This would make the document a bit more realistic on the role of the private sector.Reference

  164. There is a tension between asset based and policy/goal based
    Scoping of the data collection. Data collection should break with the tradition of being the accumulation of the data needs of all past policies & goals. Taking a 360º view of both the economy and government, data collection capability of nat. statistcs should cover all to some extent and with a detail that is relevant for decision making.Reference

  165. Its unclear who the information intermediaries are supposed to be. Shouldnt this be (to the extent possible) the responsibility of the the data providers?

    If this is about utility to the ordinary citizen, then it is essential to ensure data on actionable aspects of inputs and processes (eg. in education, disaggregated budget allocations, documents pertaining to school facilities, school and regional plans etc) is put in the public domain. The ecosystem in form of robust redress mechanisms would also need to be responsive to complaints filed by this data.Reference

  166. While not disagreeing with a role of the NSO, it would have been highly useful if it looked at how the specific sectoral data needs are going to be addressed in the new framework. Thus for education, much of the data flows from schools, to education departments and then enters into the NSOs and the UN data systems. Strengthening NSOs alone will not address the question of data quality for the education goal until it looks at it from its source- the need to strengthen the data systems in education ministries at school, sub-regioal, regional and national levels.Reference

  167. There is also a quality gap to consider and should be added to the data and knowledge gapReference

  168. Here is an inflation of the meaning of data to all forms of record-keeping. This may create a vagueness around the term ‘data’ early on. Data, in this context, refers to a specifically structured form of record-keeping that allows easier querying, analysis, extraction of insight and portability (across different media / file formats). Further, calling data ‘lifeblood’ of decision-making renders decision as inherently apolitical, reference to which is also perhaps avoidable. I also submit that the opening statement should locate the importance of data within the presen context (of MDGs and SDGs) instead of describing its intrinsic value.Reference

    • [Please ignore my previous comment, and consider this one.]

      Here is an inflation of the meaning of data to all forms of record-keeping. This may create a vagueness around the term ‘data’ early on. Data, in this context, refers to a specifically structured form of record-keeping that allows easier querying, analysis, extraction of insight and portability (across different media / file formats). Further, calling data ‘lifeblood’ of decision-making tends to de-politicise the processes of decisin-making, reference to which is also perhaps avoidable. Also, decision-making is critically shaped by which party has access to what kind of data. It is in this way, perhaps, data can be called the ‘lifeblood’ of decision-making.

  169. I guess much more is known than is visible via data accessible to statisticians – it is the bounded visibility – much more is known outside their org. than inside it. For tapping that outside knowledge content has to be dealt with differently – see initiative management at wikiworx.infoReference

  170. While efforts at fostering innovative data flows is appreciated, essential to likewise emphasize the need to streamline and re-organize the existing data flows in line with the new goals, targets and indicators. Fairly intensive (but admittedly not always synergized) data systems already exist in several domains and these need to be aligned with the new specific data needs. Thus, for education- while relatively decent data is available on primary education, there are no datasets that are internationally comparable for a number of candidate targets. Where datasets exist, there are no global consistent definitions evolving which would require a political process of dialogue between nation states. As the introductory sections of the document suggest, existing data mechanisms are also far from perfect and additional support would be required to unleash the data revolution. Consequently, suggest: a) a national strand to this global process, b) a clear mechanism for participation of representative number of sectoral experts (nation states and CSOs) in this proposed network- both globally and nationally.Reference

  171. The key issue here is event reporting.
    Done on paper without indexing/classification it is very hard to draw on it for any decision making.
    Event report in a smart system can be gradually indexed, and even re-indexed as classification schemes get additional dimensions, or a dimension gets new valuesReference

  172. The phrase ‘To know what we need to know’ leads to three difficulties – (1) it can be understood as ‘to know about the things that are important for us to know’ and also as ‘to know what things we should be wanting to know,’ (2) it introduces a ‘we’ that is not defined yet, and (3) it does not mention the objective of the ‘data revolution’ that is being talked about. While the first requires re-phrasing, the second and third are crucial. The full paragraph gives a sense of the ‘we’ being the governments of the world. If that is the case, it needs a clear statement early on. If the ‘data revolution’ is about both top-down and bottom-up approaches, then that needs to be stressed much more. Say, by adding the ideas of ‘enabling data as medium of monitoring and transparency of governance processes, ensuring public institutes both support and respond to data-driven engagements with the public, and expanding people’s ability to produce, co-produce, and act upon data.’Reference

  173. Where are the standard survey questionnaires such as those of CBMS in the Philippines?
    CBMS = Community based monitoring systems

    In the name of national souvereignty or academic freedom, each one is encouraged to reinvent the surveyReference

  174. ‘significant increase in the information’ – Perhaps the word ‘information’ is best avoided (and the word ‘data’ is used) unless of course the sentence specifically mean information as opposed to data.Reference

  175. The data revolution officers a lot of possibilities for the development of new methodologies and statistical techniques. This needs to be mentioned.Reference

  176. In addition to risks of lost privacy and reinforced asymmetries of power, the new data opportunity has “operational” risks for agencies and organizations that experiment with new data practices, including: a) low uptake, when the effort to generate usable data is not matched by efforts to ensure usage by key constituencies; b) poor follow-through, when data tools are “designed for launch” and not for ongoing use (e.g., because of poor tool design or because planning did not include resources to replenish or disseminate data); or c) the “wet match effect,” where early experiments fail for circumstantial reasons, hobbling the chance that future experiments will rally support. The report should widen the risk profile to include innovation risks along with risks of power imbalances.Reference

  177. ‘Thanks to new technologies…’ – The phrasing gives a sense of technology being the driving force in the production of increasing amount of (born-digital) data in contemporary world (as opposed to business interests being the driving force, see: This positioning of technology is controversial and best avoided. Rephrasing it as ‘Enables by new technologies…’ will perhaps neutralise the positioning.

    Further, as the embedded chart shows, a large (if not majority) of this newly available data is privately owned. The implications of this private-ownership of large quantity of data about citizens can be discussed later in this report, but its reality deserves a mention in this paragraph.

    Finally, it is crucial *not* to name the existing ‘ferment of experimentation, innovation and adaptation to the new world of data’ as the ‘data revolution.’ These all are rather attempts at grappling with both the newly available facilities of production, accumulation and usage of data, and a the new economic default of data-driven business, governance and funding models. It is important to acknowledge the existing ‘ferment’ as creating the basis for the call for ‘data revolution,’ but identify ‘data revolution’ is something that is yet to happen.Reference

    • [Please ignore the final paragraph of my comment above and consider the following one]

      When discussing the existing ‘ferment of experimentation, innovation and adaptantion,’ it should also be mentioned that there is an unfolding competition between governments, private entities and citizen groups in producing and getting access to data. While certain parts of this competition (say cyber-surveillance) may not be discussed in this report, its reality is important to note, since it may have negative impacts upon the goal of sustainable development.Reference

  178. While the absence of “operational tools” may be a technological barrier for some actors in making data usable, lines 346-347 should be revised to encompass the equally common scenario where capacity, time and expertise limit the approaches available to institutions in making data usable internally, to peers organizations, other sectors, or to individuals. Suggest a revision to:

    “data that cannot be translated into action because an agency or organization lacks the resources, internal expertise, user-centred practices or operational tools to turn datasets into usable or actionable information.”Reference

  179. Line 639 too narrow. Suggest change to:

    “Data architecture, management and outreach strategy should therefore place great emphasis on most likely user groups, user centred design and …”Reference

  180. Great paragraph. Only suggestion is the possible introduction of the idea of negative impacts of ‘data revolution.’ The paragraph makes it seems like that ‘data revolution’ is all good but it is just not realised for everyone yet. Though it is discussed in the next section, it will be useful to have a phrase connecting ‘data revolution’ to ‘growing information inequalities.’ Also, perhaps consider calling it ‘data inequalities’ and not ‘information inequalities.’Reference

  181. When it comes to engaging with qualitative data, tt is true that ‘data revolution’ so far has primarily been interested in its quantification (through proxy variables, sentiment analysis, etc.). While this gives new instruments for ‘data revolution for sustainable development’ to work with qualitative data, I agree with Neva Frecheville that the phrasing in this paragraph suggests that this is the only potential way of using qualitiative data for sustainable developemnt. I second Frechevill’s suggestion to insert a sentence stating that ‘data revolution’ allows new ways of generating and combining quantitative, as well as qualitative data, to ‘allow for a more timely, nuanced … decision making.’Reference

  182. Instead of a default emphasis on government and government agency-based advances in data fluency, the final report should emphasize the need for increased capacity across sectors, including civil society and academic institutions, for example, as key contributors to the data ecosystem with challenges more similar to government challenges than the current draft allows. Suggest changing final sentence of paragraph 1, section 2 by adding words in all caps here:
    “embedded into the action plan for the SDGs, to support those countries AND SECTORS most in need of resources…”Reference

  183. Data revolution: currently, the data is skewed in favour of the affluent urban settlements (South Africa is a case in point). The point of collection of data should be given priority. Usable and accurate data is often found in the urban settlements, because the collection and reporting units have been properly surveyed, properly arranged and have defining boundaries (individual land parcels defined by cadastre and address, as well as official place name defined by official suburb boundaries). This is not the case in the traditional and informal settlements; there are no addresses to mark the individual dwelling units, and no official boundaries to mark the place name territories, hence data from these settlement types is of poor quality.
    It is therefor recommended that the addresses are made mandatory in all settlements (indeed globally), so that each dwelling may be geo-referenced (GIS) and accurately mapped. Governments should have the political will of thriving towards assignment of addresses in the traditional and informal settlements. This will make the national initiatives of Spatial Data Infrastructure to have complete coverage. The private sector and business should be roped in to fund addresses to homesteads. Experience in South Africa has shown that the communities in these marginalized settlement types are willing to have officially recognized addresses, yet there is lack of political will from the government.
    It is further suggested that governments set up independent address assignment commissions, that would only focus on assignment of addresses; these commissions would be linked to, (but not controlled by) other organs of state, e.g. Post Offices, Land Affairs, Internal/Home Affairs, Statistics and Census Bureaux etc.

  184. Agree with comment above. The lack of infrastructure in poorer countries of the world makes the data revolution seems a distant reality. And its is some of these countries that can benefit the most from the data revolution. The infrastructure refers to the ability to store, access and analyse the data that is available. This issue needs to be addressed.Reference

  185. Here the report draft acknowledges the central role of national and international organizations in improving the ability of national systems to provide development data to the emerging global ecosystem. But unfortunately in too many places the overall report relegates the role of these organizations to monitors of other sectors’ practices, advocates for downstream end-users such as individual citizens, or supplemental contributors to the data ecosystem who need to focus on more standardized creation and publication of data.

    While all these responsibilities are real and important to a successful data revolution, the report should increase its emphasis on the need for well-resourced institutions outside government, who will continue to need to make “tremendous efforts” if the data revolution is to spread and become mainstream. These organizations in civil society and elsewhere face “data revolution challenges” more similar than dissimilar to government’s challenges. Therefore, the need for stronger capacity, more integrated expertise and long-term culture changes at these institutions is be no less than important than the need for stronger data capacities at government agencies or among individual citizens.

    In many if not most cases, such organizations are more influential brokers of development data than individual citizen users. While the promise of the data revolution does and should reach down to the least-savvy, least-wired citizen, the most impactful uses of such data are usually seen at the organizational level. (See Fung, Gilman & Shkabatur, 2013 “Six Models for the Internet + Politics,

  186. Jed makes a valid point and linked to this is the skills needed at all levels. Demonstrated use of available data is lacking at most levels in society and there would need to be a specific drive to enhance this.Reference

  187. In the area of open data and “ICT4D,” networks of networks and networks of innovators are proliferating. These platforms are often doomed to failure due to a lack of sustained participation or the curse of “rebuilding the wheel.” (Groups working to network innovators in this way include the Open Government Partnership, the Open Data Institute and the World Wide Web Foundation, to name only a few.)

    To mitigate the risks of building a network without knowing “if they will come,” the recommendations for innovation and analysis should refocus on research and convenings that make a census of similar initiatives and aggregate findings, resources, gaps and shared agendas, before pitching a new “big tent” when tapping the knowledge of network constituents is ultimately more important than the inauguration of a new network amid a crowded bazaar of like-minded efforts.Reference

    • The second and third sentences in the above comment would ideally be reversed. The point of this caveat is not to assess the impact of any individual network, but that as networks proliferate and duplicate, they approach a point of diminishing returns.Reference

  188. The metaphor of ‘bedrock’ is problematic. The bedrock for accountability is perhaps (social) contract. But keeping that aside, it is important nonetheless to emphasise the importance of data in ensuring accountability. The word to use here is perhaps not accountability but transparency. What availability of (reliable) data does is making processes transparent to those who were not involved in the process itself. But transparency can also be ensured through other means – such as opening up decision making processes to public participation. Hence the point to emphasise here is the key role data can play to ensure transparency of processes at (the global) scale.

    ‘More information opens up the possibility…’ – ‘Information’ can perhaps be substituted by ‘data,’ so as to avoid the quick shift from usage of ‘data in the previous sentence.

    Further, there is a darker side to the ‘revolution in expectations’ point. ‘Data revolution’ tends to normalise the expectation that extraction and mining of personal usage data (of digital devices and networks) is acceptable business model in particular, and form of data gathering in general. ‘Data revolution for sustainable development’ may have to act against the normalisation of this expectation, which is also hinted at later in the report.Reference

  189. This part must link for consistency to line 18-24 where it talks of data and knowledge gaps but not quality as such. Include quality there as per earlier comment.Reference

  190. ‘… challenges concerning the rights to access and use data’ – The challenges also involve conceptualisation and enforcement of the rights to opt out of behaviour quantification and data collection through digital devices and networks.

    ‘… balance the rights of individuals with the benefits of the collective’ – Please also add the economic opportunities of data as the third factor to be balanced against ‘rights of individuals’ and ‘benefits of the collective.’

    ‘… the information could be used’ – Change to ‘the data could be used.’Reference

  191. Good points above. The data revolution will not necessarily address the missing people and issues as described above. Continues work on new methods (including the use of technology) will have to happen to ensure totally inclusive SDG data.Reference

  192. It is indeed commendable that the National Statistical Offices are being re-imagined in this report as a fundamental actor in realising ‘data revolution for sustainable developemnt’. Addition of two concerns here, however, can be crucial.

    Firstly, along with desired qualities of the data created by NSOs mentioned here, please also suggest ‘openness’ of data as a fundamental pre-condition for collection and usage of data for sustainable development. ‘Openness,’ in this context is needed not only ensure the wide availability and usability of the data concerned across various types of actors, but also to make the data collection processes themselves more transparenct, and thus accountable.

    Secondly, harnessing possibilities opened up by ‘data revolution,’ as discussed above, requires various government agencies to team up. Data privacy and rights guidelines will perhaps be prepared by Ministries of Information Technology, Communication and Law, while implementation of government-wide re-engineering of data collection, processing, archival and analysis processes will perhaps be governed by Ministries of Personnel, Governance Innovation, and Home Affairs. NSOs are often responsible for undertaking only the major data gathering exercises of the government (such as census and national sample surveys). ‘Data revolution for sustainable development’ will require more granular and broad-based changes in how governance of and through data happens across the state agencies.Reference

  193. ‘…how little we know about’ – The use of ‘we’ here is misleading. Perhaps it can be changed to ‘how little is publicly known about.’Reference

  194. “standardised in accepted ways” seems deliberately vague, and isn’t unpacked here: a comment about bias could be added here, such as ‘recognising biases, and trying wherever possible to mitigate this bias.”Reference

  195. To fill this role, National statistics offices, or rather those working in them, will also need to drastically increase their data literacy to ensure that they understand what they are collecting, and they are using it in the right way.Reference

  196. This is a comment regarding the following paragraph about ‘Data that is not used or not usable.’ It would be useful to identify in this paragraph that a great volume of data is also not used or usable because either it is privately owned, or because it is not available in open formats and licenses. The former issue highlights the predominant privately-owned nature of data coming out the of the ‘data revolution.’ The latter foregrounds that though government, private, academic and civic entities do produce significant amount of data, it is often not used not only because of lacking quality of data, but its simple unavailability as data open for re-usage and re-sharing.Reference

  197. Line 487: governments themselves need to increase their own data and statistical literacy, as well as the other parties mentioned – and not just focused within national statistical offices, as mentioned above.Reference

  198. This will be a great opportunity to mention the need to embrace ‘open data’ as a necessary condition of data coming out of the ‘data revolution for sustainable development.’ The argument is already present in text, but the term ‘open data’ is missing.Reference

  199. Agree with the point made by John it links to the development of new methodologies of collecting data and moving away from traditional collection methods. The emphasis should be on small area statistics and traditional survey methods are too costly. The application of different statistical techniques and geographical methodologies will also play a more important role and attention needs to be given to the development of these methods as an alternative/addition.Reference

  200. The ‘proven solution’ mentioned here is not without its problems; biometric ID systems have been known to actually further exclude marginalised communities and harm people’s privacy rights, and as a result has reduced access to vital services. It’s by no means a silver bullet to solve the problems here. Therefore, I’d suggest removing the sentence starting “One proven solution…”Reference

  201. A caveat regarding the estimates of economic value of open data can perhaps be mentioned here. Much of such estimates take it for granted that gathering of personal data of iusers of digital devices and networks as an unproblematic starting point for value creation (in terms of monetisation of personal data). It is impotant for ‘data revolution for sustainable development’ to address this issue critically, and not avoid its discussion against the justification of economic value creation.Reference

  202. Suggested insert:

    Clear standards, taking into account international initiatives which have already been established, such as the International Aid Transparency Initiative, and the Open Contracting standard…Reference

  203. As commenters above have noted, it is important not to forget qualitative data as an important part of the data revolution. The same digital technologies that enable quantification of qualitative data, can also maintain the connections between quantitative and qualitative data, and enable greater access to qualitative insights from citizens. The digital traces in photos, videos, audio and written text left by citizens, and the ability to better host two-way communication with citizens through digital tools to source new qualitative insights (as demonstrated by some of the data-driven platforms used to access qualitative insights in the SDG consultation process) are equally important to better policy making in future.

    Suggest adding at line 87: “Digital systems can also better store and index qualitative data, making it possible for decision makers to deepen their analysis of quantitative data with access to qualitative insights.”Reference

  204. It is important to consider the distribution of economic benefits from open data. The McKinsey study cited at line 444 indicates that $2tn of the benefit will accrue to the USA and Europe (925m people), with the remainder shared between the rest of the world (6.21bn people). It is also notable that the report cites a potential

    Line 437: Suggest adding -”impact of better and more open data on the economy *and society*.”

    The example at line 446 – 449 is not strongly open data related. U-report data is not widely shared as open data, and particularly not without anonymisation. The example at line 450 (mobile phone records & productivity) is not related to open data at all. Open data is data that is published for anyone to re-use. Detailed mobile phone records are very rarely shared as open data.

    A possible examples to include to show the social value of data:

    “Transparent Chennai have worked to combine city-held data on the location of existing and planned public toilets, with data sourced from participatory workshops with citizens, in order to ensure sanitation is located at places of the greatest need.


    In general, there is a small but growing qualitative case study evidence base on the use of social impact of open data (e.g. and in the prototype literature database accessible at, but methodologically this is unlikely to be expressed in the quantified terms of the economic estimates put forward by large consultancies.Reference

  205. Line 809, on building a visualisation platform: there are lots of excellent open source analysis and visualisation tools already developed. There is an opportunity here to build upon and use these, improving them not only for the needs here but also for the public good, and demonstrate a clear commitment to the ‘open source’ movement within the UN itself. This commitment could also be demonstrated by releasing any new software developed by the UN as open source.Reference

  206. The quick wins presented from Line 797 are welcome, however, it would be good to see an explicit commitment that these will be based on an ‘open by default’ approach. These also provide an opportunity to model best practice in engaging with re-users and making data accessible.

    Suggest updating line 810 to “…using the most advanced tools and features for exploring, analysing, *and re-using data, and demonstrating best practice in engagement(i) with data users through the provision of guidance and education resources for data re-use.*”

    (i: See the five stars of Open Data Engagement for one possible model )Reference

  207. ‘information ineuqalities’ can perhaps be substituted by ‘data inequalities (and hence all the various forms of inequality of power and priviledges it creates and is created by).’Reference

  208. We welcome the recognition of the importance of users to shape data availability and data standards at Line 781.

    There would be value in encouraging adoption of this ‘user forum’ model at a local level.

    Consider adding at line 784: We encourage the replication and experimentation with ‘user forums’ at the country and agency level to foster feedback loops around specific local datasets also.Reference

  209. The report references government and pivate sector collaboration at a number of points (Ln 161; Ln 740; Ln 812) but does not appear to reference at any point vibrant peer-production and volunteer communities who have an important role to play in the data revolution.

    Volunteer communities such as Open Street Map, and the Humanitarian Open Street Map teams harness the effort of thousands of contributors to build vital, high quality open data sources. Hundreds of thousands of communities have created Wikipedia, and are now building the WikiData platform to host millions of well managed, multilingual and copyright free data points.

    Suggest adding an Example Box around Ln 176 to highlight volunteer led data generation efforts. For example:

    “For over 10 years volunteer mappers have been developing Open Street Map, a detailed digital map of the entire world, openly licensed for anyone to re-use and draw upon. This dynamic map not only supports many small businesses, generating value-added products on it’s open data, but has also become a vital resource on crisis situations. Using donated satelite imagery, volunteer contributed GPS traces, and a globally coordinated team of volunteers, the Humanitarian Open Street Map Team (HOT) work to generate updated and accurate maps of disaster zones to support agencies working on the ground.

    This is just one example of a growing range of peer-produced data resources that mobilise average citizens to contribute key data infrastructures for sustainable development.”Reference

  210. Suggest revising at line 160 to include: “It is governments, ideally working in collaboration with *civil society, academia and* forward looking and socially responsible private institutions…”Reference

  211. Great section overall. Congratulations!

    Just to add one thing – along with principles and standards, it is important for UN to push governments to enact policies enforcing such principles and standards. Without policies backing them, such measures are often difficult to enforce, and especially for citizens to monitor and audit.Reference

  212. The report references government and private sector collaboration at a number of points (Ln 161; Ln 740; Ln 812) but does not appear to reference at any point vibrant peer-production and volunteer communities who have an important role to play in the data revolution.

    Suggest adding at Ln 812: “…fostering private-public partnerships, *and fostering community-led peer-production efforts*, for data dissemination and visualisation.”Reference

  213. Line 665 discusses capacity building for ‘data science…to leverage opportunities in big data.’. This is a very narrow specification of the capacity building needed: as the skills requirement is much broader than just ‘big data science’, including also the socio-technical skills to make effective use of data, and to facilitate wider access to open data commons.

    Consider adding:

    “leverage opportunities in big data*, to to work with open data, and to mobilise data as a resource in addressing social issues.”Reference

  214. Agree with William. The average citizen do not necessarily poses the skills or the time. A key responsibility then as part of the wider data revolution is the visualisation of data, the demonstrated use of the data as well as providing basic knowledge to the average citizen. However the question is who will do this? Is it the responsibility of government or someone else?Reference

  215. Clare M. and your team, well done on the production of such a comprehensive report in such a short time. Thanks for making it participatory.

    Great thinking has gone into this report and i find it informative and interesting to read.

    To strengthen it further, I propose a tweak so that the letter and spirit of the text is clear and categorical: data revolution for fighting poverty and delivering sustainability. Politics of how to manage the current data boom is a very important public good, but it is not the central thrust for this report- this is data revolution to fight poverty, discrimination, deliver human rights, promote peace and security, deliver environmental sustainability and tackle inequalities.

    I disagree that the data evolution is already taking place. What we have listed here does not qualify to me to be data revolution but maybe data advancement or at best evolution. It is runaway data development. Otherwise, it would be data revolution if in the spirit of the High Level Panel report it helps us to deliver development that ‘leaves no one behind.’ The report under discussion is correct, the current state of data is data that perpetuates inequalities, leaves populations uncounted and hides huge disparities. By both design and default, the data sector is advancing to maintain, perpetuate and legitimise the status quo. it will be data revolution if it challenges this current trend.

    The report needs to take cognisance of the fact that what gets measured gets done. Hence, data revolution needs to capture data of what actually needs to be done. It should drive actions where they are needed. IT should be empowering the marginalised and those that have suffered discrimination.

    The purpose of data is not only for reporting and making management effective and i agree that these are very important uses of data. Data revolution should drive us a step further and enable people to act, change the power dynamics and promote a bottom up approach alongside the top down and horizontal approaches. It will be revolutionary if it enables us to identify and overcome the current abuses of the current models of data and associated modes of collection and interpretation. It is not unusual for a politician in Kenya to respond to a set of data that says ‘this is green’ and tell the people, ‘no it is not green it is yellow but they(data people) want to fool you into believing it is green.’ What about politicians who tell their electorate that they should falsify population figures or poverty levels they give to national survey enumerators so that their regions can attract higher national budgetary allocations? what are the data abuses coming from the corporate sector to gain market advantage or evade taxation?

    Data revolution need to challenge the questions of who counts and whose reality counts and for whom is counting being done? I am dismayed that this report turns its back to participatory techniques and tools.

    Amongst the principles we may need to add one that provides for contextual development of data. We do not want data that is developed in Africa to be patterned with the data developing in Europe. Though comparison over time and across countries is important, one size will not fit all and we need data that will trigger a different development from the western civilisation. The report should be careful in glorifying data that is produced by the business. This data is collected to drive consumption and sometimes erode the very values that a society needs to survive. We must go slow in telling governments to learn from the corporate. We need a normative driven data revolution in the public sector.

    I am impressed that the report recognises that data drives inequalities and marginalisation. The report also needs to provide a list of values that should drive the revolution- people want to challenge the current state of data ownership, type and mode of mining it. People want to region control over their lives and want to bring in a era of user friendly data that helps them to realise their aspirations for participation, good governance, equity, human rights, sustainability and justice.

    Curent data production, use and interpretation is controlled by segments of society and they use it to skew decision making, indoctrinate masses and radicalise the population. This is where the revolution is needed to a data that is based on peoples’ lived realities and experiences that can not be twisted for ulterior gains.

    while i support the creation of the institutions that are recommended by the report for driving data revolution, the team needs to think carefully whether this will continue to perpetuate data as a specialised field that is a reserve for certain technical people or an approach that integrates data in all spheres of implementation of the SDGs making everyone a producer, user and developer of data. As things stand today, data sector is intimidating and marginalising not only to ordinary people but also to many development practitioners. Yet, my mother and I live in data, we know who goes without food in our neighbourhood and which child is out of school and for what reason and where resources are being used wastefully yet our data never counts. Figures that fly around make no sense to us and our neighbours and never affect the way we vote or act. We are experiencing and using one type of data in our lives while national and global decisions are based on a different type of data. To revolutionarise data is to recreate this connection between what I go through on a day to day basis and what technocrats use in planning for me.

    One final general comment: Beyond 2015, GCAP and others have provided a lot of comments. It is not enough for the panel to listen, the panel needs to be seen to have listened.

    Do not get me wrong. I provide these suggestions for the purpose of making the report stronger. The report is strong. It is informative and it must not be trashed- a good thing must bring forth a better thing.

    Kudos to the team.

  216. We would recommend adding a line here , possibly after sentence that ends SDGs, that …A data revolution is not just about statistics, but promotes dialogue between difference actors at all levels.Reference

  217. add ‘age’ here as a source of exclusionReference

  218. In the section, include a separate point on citizens, possibly as follows;
    Line 509 – Citizens, including the most vulnerable such as children and youth, disabled persons, the aged, report on the realities in their communities and engages in dialogue that feeds into improving development effectiveness and outcomes.Reference

  219. Thank you for sharing the report and taking broader consultation. I think the report is very good, and I’m looking forward to reading the final version.

    I welcome the idea of the importance given to the international statistical community, at the UN level, to set principles and agreeing standards related to data revolution (e.g. lines 570-586). I think it should be further strengthened giving reference to the UN Statistical Commission as the platform for future discussions. I think it is extremely important for the post-2015 development agenda, the SDG indicators, and the developments in data revolution to be driven at the Statistical Commission level.

    The report refers to big data / new data sources / large volumes of data on several occasions. I think it would be very beneficial if the report would make a clear distinction between data revolution and big data, particularly in lines 32-41 and 77-92. Big data and new data sources are an integral part of data revolution, but data revolution is much more than that and I believe that this report should be very clear about that.

    In lines 797-823 ‘quick wins on SDGs’: I agree that we can achieve quick wins for the SDGs, such as setting up the baselines, quick overviews of the gaps, etc. But that doesn’t mean that data revolution, through new data sources / big data, will solve the data gaps. I think that it should be made more explicit that quick wins can provide some quick insight, but that they are not long terms solutions, which lies only in increased statistical capacity.

  220. The MDGs were rightly criticised for the lack of disaggregated data and for the focus on national averages and global aggregates. This strongly contributed to the continued exclusion of certain groups over time, and glossed over rising inequalities. This is why the data revolution needs to aim first and foremost to leave no one behind.Reference

  221. Comments form United Nations Volunteers: suggest to add: …and voluntarily contribute to produce and collect data.Reference

  222. First and foremost it means generating more and better data that is disaggregated by gender, age and the range of other important social and economic dimensions (as per prohibited grounds of discrimination in international human rights law).Reference

  223. This means respecting and protecting human rights throughout the process from data collection through to data management and storage. It also means empowering people by including them in the data collection process and in the use of data, by making all data relevant to SDG progress openly available in usable formats.Reference

  224. Save the Children thanks the UN Secretary General’s IEAG on a data revolution for its draft report and the opportunity to provide comments on its content.

    While the draft report covers many critical aspects of the data revolution, Save the Children considers language on measuring and monitoring inequalities could be strengthened to ensure a truly transformative data revolution for all social and economic groups. Accordingly, we have limited our comments to the importance of addressing inequalities and respectfully urge the IEAG to take into account the following:
    • Line 133: We welcome the overarching principle: ‘This should be a revolution for equity in access to and use of data’. This could be further strengthened as a cross-cutting principle or theme of the report, by being brought forward into section 1.1 on ‘what is the data revolution’. We suggest expanding the text to further embed equity at the core of the revolution, recognising that many of the data gaps that exist relate to the poorest and most marginalised: “Above all, this should be a revolution for equity in coverage of, access to, and use of, data”

    • Line 553: Leaving no one behind under the post-2015 framework will not only require an inclusive approach as suggested on line 28 but an immediate and ongoing focus on meeting the needs of those who are furthest behind – the vulnerable and most marginalised – first. To reinforce the role that data will play in achieving this objective, we suggest including the words “between advantaged and disadvantaged social and economic groups” at Line 553.

    • Line 559: We welcome the recommendation for a comprehensive programme of action in four areas, although it would be helpful if the report was clearer on who should take forward these actions, and whether existing institutions are likely to be capable of this.

    • Line 559: The four action areas capture the main strands of work that are required. However, the principle that this is a revolution about equity is somewhat lost in these recommendations. We recommend strengthening the text in Table 1 at Line 587 as follows:

    a. Line 607: In order to support better understanding of the nature of the barriers that marginalised people face, the principle of data quality and integrity should include the need for measures to improve the perceived legitimacy of qualitative and subjective data, and standards concerning the triangulation of these measures with objective and quantitative measures.

    b. Line 615: Data disaggregation: This is an important principle, but suggest strengthening language to include a stronger focus on equity by:
    i. Expanding the title to “Data disaggregation and equity”;
    ii. Explicitly recognising that this principle will be critical to realising the important commitment that should be established by the SDG framework – that no target will be considered met unless met for all social and economic groups;
    iii. Prioritising filling data gaps for the poorest and most marginalised, through including the following text at Line 619: “Strategies to improve the quality and coverage of data in the context of limited resources should focus on expanding coverage of core indicators for geographical areas and population groups that are currently not adequately captured”;
    iv. Recommending disaggregation by disability and by important dimensions of identity, with consensus growing on the need for data disaggregated at least by ethnic group in order to monitor horizontal and intersecting inequalities. An explicit reference at Line 617 to data disaggregated by disability is vital, particularly in light of the reference at Line 286 that ‘it is still impossible to know for sure how many disabled children are in school’. A recommendation should also be included to ensure that surveys are designed to allow for intra-household disaggregation of data to capture discrimination on the basis of gender, disability and age as well as the evolution of social norms; and
    v. Recommending that conventional data collection systems are complemented with additional mechanisms to capture data for vulnerable groups that fall through data gaps, including for example children living on the street and institutions and people displaced from their homes.

    c. Line 752: Need for greater emphasis on increasing data and information literacy for citizens in this recommendation, particularly for poor communities with limited education, ICT access and ICT literacy, and groups who face social and cultural barriers to accessing and using information (e.g. marginalised women and girls).

    d. Line 797: The opening paragraph on ‘quick wins on SDG data’ needs to place greater emphasis on SDG initiatives being part of a longer-term strategy to progressively strengthen data systems and close data equity gaps in coverage and access. This is implicit in the text, but should be made more explicit in order to guard against the SDGs leading to a focus on what is deemed measurable rather than what is valued. The report should recommend that indicators for which data is not immediately available should not be excluded from data strategies, but rather that plans need to be put in place for bringing these indicators on stream as data becomes available and for the innovation in closing these data gaps to be incentivized. This includes disaggregated data for disadvantaged groups, and data for issues not included in the MDGs such as child protection and governance.

    e. Line 800: include reference to the importance of a focus on methods and tools to collect data for disadvantaged groups within the SDGs data lab, and on concrete mechanisms to track inequalities such as stepping stone equity targets.

    f. Line 817: complement the good proposals for a visualisation platform and dashboard with recommendations for using the SDG opportunity to sensitise and improve data literacy amongst poor and marginalised communities. Online platforms should be accompanied by offline ones to ensure that data divides close rather than exacerbate existing inequalities.

    Save the Children considers integration of the above items in the final report to be published on 6 November 2014 would maximise the capacity of the data revolution to address inequalities and the persistent barriers to equal life chances for all children.

  225. Comments form United Nations Volunteers: suggest to add: • A proposal to engage volunteers on a mass scale, especially at the community level, to collect data on SDG targets through participatory forms of monitoring.Reference

  226. More information alone can’t achieve this, it also requires: 1) commitment to transparency, including the necessary freedom of information framework, and 2) a commitment from all duty bearers, both public and private, to uphold the right of all individuals and groups to participate and make use of this information.Reference

  227. The data revolution should be coupled with a human rights based framework of safeguards.Reference

  228. On behalf of the global HelpAge network please find attached suggested wording to be incorporated into the draft document. We hope you find this clear and the wording is helpful.

    Please note that we work with a wider network of organisations called the Stakeholder Group on Ageing, but due to the time constraints imposed by your deadline, we were not able to get their sign-off on these recommendations. We feel confident, however, that the views expressed here reflect those of a much wider group of civil society organisations concerned with ageing and development issues.

    We think more needs to be done in the report to make both the paucity of data on ageing in and the need for better disaggregated data by age more visible in the report; and that this can be achieved with a few relatively modest amends.

    Please find below suggested text changes where we think this might be possible:

    Line 8: we cannot know how many, where, and at what age people are born and die; how many men, women and children still live in…
    Line 9: poverty; how many children need educating and how many people need reskilling, and how many teachers to train or schools to build; how many doctors and nurses there are to serve populations
    Line 12: helping them; how many people are in work and what kind of working conditions they have; what companies are trading and whether demand for their product is expanding
    Line 21: the poorest people in it. But the MDGs did not cover all population groups nor the range of sustainable development issues and despite significant progress, huge …
    Line 22: remain about some of the biggest challenges we face, for example population ageing and climate change, and
    Line 28: focus on being inclusive and thus ensuring that no one, regardless of age, ability, location, ethnicity, religion and gender, is left behind.
    Line 46: about how to create a data revolution – it is already happening – but how to improve its reach and effectiveness and how to mobilise…
    Line 91: standards of honesty, respect of privacy, rigour, comparability and impartiality that have been developed
    Line 238: uncounted. Too much data is out of date, leaves out whole population groups, is unreliable or simply not available. Too many people are
    Line 257 Data on employment, for example, is notoriously unreliable. Data on age and disability is routinely not collected. A great deal of data is difficult
    Line 289: …were not registered with civilian authorities. And despite the number of older people being predicted to rise to 16 percent of the global population by 2030, little data on later life is collected.
    Line 300: is reported, quality is not consistent, data is rarely collected beyond the age of 49 and is not comparable.
    Line 303: economic roles of women of all ages as caregivers to children, older persons and the disabled in the
    Line 489: ‘infomediaries’, ensuring that all people have capacity to input into and evaluate the quality of data and use
    Line 505: development at global, regional, national, and local scales; make demographic and scientific data as open as
    Line 512: Millions of people of all ages in low- and middle-income countries…
    Line 580: interoperability of information systems, standards for demographic and geospatial information
    Line 608: Poor quality and incomplete data can mislead and make an issue invisible.
    Line 617: disaggregated across many dimensions, such as geography, wealth, sex, age and disability
    Line 624: sources, and digital data generated by people. The data cycle must match the decision cycle and reflect national realities.
    Line 694: through systematic research and mapping of how existing and emerging data sources can be used for
    Line696: linking innovators with national statistical offices to improve their reach and effectiveness and the
    Line 709: analytics tools to better capture and evaluate long-term trends affecting sustainable development

    Additional material if it is helpful:
    Rates of HIV testing uptake are lower in older people, which masks relatively high rates of prevalence of the virus. For example, 18 per cent of women aged 50 and over in Swaziland have tested for HIV in comparison with 40.7 per cent of women aged 15-49 .
    365 to collect, manage, and disseminate micro data.

    New box?
    The World Bank has increased its work to collate comparable household data, including indicators relating to old-age poverty. This data was critical to the development of the Global AgeWatch Index which brings together a unique set of internationally comparable data based on older people’s income and health status, education, employment and aspects of the enabling environment, including physical safety and civic participation. These areas have been identified by older women and men as key enablers to their wellbeing.

  229. The focus on inequality here is welcomed, but is framed quite narrowly in terms of information gaps. The data revolution should have a broader focus on leaving no one behind, and should address unacceptably high inequalities of opportunity and development progress.Reference

  230. , and it is the responsibility of all those engaged in generating data to make sure that they count the uncounted, while at the same time protecting the human rights, safety and privacy of each individual.Reference

  231. NSOs will also need to forge partnerships with those generating useful non-official data that meets statistical and human rights standards.Reference

  232. And – there is a chronic gap in reliable disaggregated data to reveal the biggest progress gaps for specific groups and characteristics.Reference

  233. By investing in the provision of timely, quality disaggregated data at national level, which is made publicly available in a usable format.Reference

  234. Governments and multilateral public institutions work effectively with the private sector to regulate the way in which data is collected, managed and stored in accordance with the public good, and specifically in line with human rights principles and standards.Reference

  235. “building on existing human rights standards and intiatives in various domains.”Reference

  236. SECTION: The State of Data:
    (1) Usability of data: One of the key lessons in SA over the last 5 years is the realisation that ‘official stats’ will probably (given fast changing dynamics) never be ‘up to date’ to address all the needs of decision makers. It is exactly in this ‘gap’ (which will probably always exist) where collaborative initiatives/networks/research institutions can play a huge role in developing ranges of indicators and indicative and integrated data sets that can then be tested & refined. As much as there should be emphasis on the standards and integration etc. as much acknowledgement and support is required for more innovative practices that can feed into ‘official processes’, and that answer specific processes. The role of user groups and active involvement of decision makers is crucial.
    (2) If we are to better support planning (spatial targeting) is the issue of getting more information and detail at a finer scale (one of the reasons for CSIR GAP). Having information only at a very aggregate scale often defeats the purpose. Temporal and teritorial and sectoral integration is key.
    (3) Enough data: Question also about the investment in data collection. If we do not collect what we set out to collect (incomplete sections) could ask why bother? Is not all info then not equally relevant? or is it a collection/survey-er issue.
    (4) User-generated content is mentioned briefly, but it is the most important part of the data revolution, because of the vast number of citizens with sensors who can provide data in real time. The user-generated content can supplement official data sources to make them more effective. Citizens do not always trust the official sources, with reason (eg: South Africa’s crime statistics), and have the ability to challenge the official sources.
    (5) The need for regulation is mentioned but maybe could be refined to include aspects such as metadata (documenting the data) which is mentioned briefly, but is actually critical to the success of any data revolution. Archiving is mentioned only once, yet it is a major problem with digital data sets that can disappear easily and for ever.
    (6) The aspects related to i.e. the need to clean the data before they can be used effectively etc. need to be mentioned as part of the challenge in data scarce regions.
    SECTION: Data Revolution/Data we want for Sustainable Development:
    (1) One of the key points that could probably be raised as part of the ‘need’ is not only the data dynamics, but also the fast changing dynamics in terms of ‘life style’ and household formation, as well as fluidity and movement of people – especially so on the African continents and in contexts of large scale informality.
    SECTION: Data we want for Sustainable Development
    (2) Institutions Involved: Would be useful to not only mention ‘researchers’ & ‘academia’ but also the role of ‘research institutions’ and more importantly collaborative networks where ‘users’ and researchers actually work together in identifying key gaps and creative sollutions. In order to make this revolution real (in our context at least) the different players have to be willing to collaborate and move beyond turf issues/ control of information/ fears that the message will be negative….etc.
    (3) Data for future use: There is a lot of mention about ‘tracking change’, but recent work has also highlighted the critical need for the type of data that can assist fore casting & modeling of different investment and growth scenarios. Not just about MORE data but about the ability to integrate and ‘make sense’ accross sectors, scales and time periods.
    (4) The incredible challenge of accessing data (broad band issues in SA by institutions and municipalities), making data intense processes affordable, capacity issues, and incredible duplication in processes and data related initiatives due to the need for a range of ‘line/national sector departments’ to have their own information systems is part of what needs to be managed through a principle of supporting effective collaboration.
    (5) The ability not only to linking aspects of data from different data sources, but also integrate data from different sources, sectors and geographic and temporal dimensions is crucial for understanding complex interactions – especially given more systemic understandings of changing dynamics.
    Innovative work in rural areas by CSIR, Rensie van Rensburg on concept of infro-preneurs to also use data gathering and integration as job creation initiative.

  237. Agree that this needs to focus more on what this means for national data, specifically:

    1) Leaving no one behind by ensuring the collection of data disaggregated by social groups, in accordance with the grounds of discrimination prohibited by international human rights law, and strengthening capacities to analyse disaggregated data, measure the disparities between social groups and monitor the reduction of inequalities, in order to close the gaps between social groups.

    2) Embedding a human rights-based approach in the data revolution, which means promoting participation, empowerment and the right to information (of both rights-holders and their representatives, such as relevant national human rights institutions) in the identification, collection, processing and dissemination of data. Embedding a human rights approach also means measuring not only outcomes, but the means (legal, institutional and policy) necessary to achieve the targets to assess the extent of efforts being made.

    3) Taking a forward-looking approach to measuring the new goals and targets, so that the priorities of the new development agenda are not limited by existing data and data sources, but promote serious investment in the development of new data and data sources, going beyond traditional statistical data to include human rights events-based data, perception surveys and non-official sources of data which meet relevant statistical and human rights standards, e.g. verified data collected in human rights organizations, academia or civil society.Reference

  238. I agree with Samantha but it might be tricky to specify specific efforts because you run the risk of leaving out some key initiatives might be better to leave it at “… that builds upon existing efforts in other domains..”Reference

  239. There might be a whole new set of quality assurance methods developed when it comes to public private partnerships related to data. The basis have been set with existing frameworks but it would need a lot of development and a dedicated effort would have to be made in this regard.Reference

  240. Strongly agree with Sam Custer’s suggested edit. A lot of progress has been made over the past 5 years with establishing data standards, particularly for development flows, and this should be referenced.Reference

  241. Creating more and more data alone will not lead to a ‘revolution’ – when the data actually starts to lead to change that is when we will a revolution occurring. The application and use of data is critical to a data revolution. The real opportunity presented by the global interest in data is the ability to push it beyond the techies to build wider trust, engagement and interest in data and ensure we are also collecting data on results and impact, not simply counting people, schools and hospitals alone.Reference

  242. Building on Sam’s and Jed’s comments, there should be a reference not just to assessing research gaps and creating incentives to use the data, but also to monitoring and assessing data quality.Reference

  243. WaterAid and the wider WASH sector commend the inclusion of a focus on disaggregation. Consideration should also be given to including dimensions of land rights i.e. slums and formal urban settlement.

    For more information on the proposals of the WHO/UNICEF facilitated consultation of WASH expert, please see

  244. Very much agree with the need for disaggregated and sub-national data. The variation between least deprived and most deprived regions in a country can be stark for example India – where in some states the there are over 75% of the population living in deprivation whilst in other locations this is as low as 15-20%. National deprivation statistics therefore will conceal the extremity of the problem preventing appropriate mechanisms to be applied to manage this.Reference

  245. Success in launching a date revolution will be to make open data that is currently not available the public. In terms of Governance and leadership, it is crucial that where district, region, national and multi-national commitments at a regional and global level have been made, they are monitored and reviewed at a sector level. The WASH sector has which allows Governments and NGOs to have an open dialogue on the evidence for implementation of WASH policy. This section should explcitly note the value for improved Governance where evidence for policy implementation is made available for analysis.Reference

  246. 16-17 It will be useful to include making it accessible to people where lack of technology may prevent easy access to information
    21-23 Specific areas and issues remain hard to measure due to them being low priorities of the state/ international community, and particularly difficult to measure are areas which require urgent attention but have not been a concern to the state previously e.g. ebola (communicable diseases)/ humanitarian crisis, deaths and implications of violence in conflict etc
    41 Creation of lots of data and the open data movement is the first of many steps of a data revolution – the beginnings maybe but a revolution will occur when the data is applied and leads to results/change
    101 Data comes with a lot of risk especially if there is no trust in those who are collecting the data and how they are using it. Furthermore, bodies/organisations/ individuals can be threaten with the use of surveillance through technology and data collected to control populations, therefore the ‘revolution’ can often be interpreted as strengthening the power in the few.
    169 Some line ministries are stronger on data collection than the NSO – therefore this needs to be aligned with national governments approach to increase their capacity to collect and communicate data
    386 people should be able to look at this information whenever they choose to.

    399 There is a difference between data for decision-making and data for research and analysis (backward looking, high quality. Shows trends over time, of good statistical value but not useful for decisions as its too late and looks backwards.
    615 2. Data disaggregations
    Disaggregation should be at sub-national level. Disaggregation should lead to verifiable data especially for public services such as health, education and water and sanitation. This should also apply to revenue collection and spend
    714 Last bullet on the importance of citizen feedback to strength data and its use for monitoring and making decisions
    773 How can it benefit from data being produced by other GPs so there is no re-producing the same data but better generation and use of data across initatives?

    781-784 Feedback loops should focus on more national and sub-national users. This is where things matter

    797 One of the big challenges on targeting resources for the SDGs and also monitoring their success will be the disparity between countries of what is important SDG wise for them and how they measure this. Identifying the data gaps will be critical to then overcoming them to achieve success.

    809 This will undermine the data revolution, who will use It and for what purposes is not clear etc

  247. You appear to suggest that mobile phone records, collected without explicit consent of citizens are a legitimate data source. I would encourage you to take out this example. Cell phone records certainly are not open data and should not be mixed into discussions about open data. Some recent research suggests that it is easy to de-anonymize mobile phone usage data (see:

  248. I would suggest to include a reference to the Right to Information (RTI) in this paper. You could highlighting that RTI legislation (Freedom of Information Acts), if also properly applied in practice, is a powerful tool for citizens, the media, civil society organizations and the private sector, to receive access to data, surveys, documents, records and other information that is held by public bodies. As experience from a number of countries shows, RTI often works best in practice when there are institutions in place that can mediate between citizens requesting data/information and government agencies, and who can independently assess if the publication of certain data might violate the right to privacy, and if there is strong public interest that justifies a publication (see, for example, the work of Access Info Europe and their RTI rating).Reference

  249. Every country need to mobilize own organisations et find strategies for succeful.

  250. The Global Framework for Climate Services (GFCS) is an an example of a global UN led platform for informing governments and the public on changing climate and providing vital information for mitigation and building resilience.Reference

  251. Data from near real time global network of monitoring infrastructures such as the WMO’s Global Atmospheric Watch System (GAW) helps governments in rapid decision makingReference

  252. The Meteorological community has developed protocols for exchange of data and the maintenance of standards. The increasing commercialization of data providers will provide challenge for the free and open access to this important information resource.Reference

  253. The GFCS (Global Framework for Climate Services) is an ambitious international initiative spearheaded by WMO in partnership with other United Nations agencies to improve climate services over the next ten years to help user communities and countries cope with natural variations in climate as well as human-induced climate change. The Potential socio-economic development benefits from this data are huge – the global gains from early warnings systems would reach between 4 and 6 billion U.S. dollars per year, with additional co-benefits, according to a World Bank study.Reference

  254. Existing and established partnerships with support from UN agencies and governments such as the Global Framework for Climate Services GFCS can greatly assist in the rapid implementation and delivery of data related to sustainable development goals.Reference

  255. Might want to rework the first sentence a little. The point of the following sentences in this first paragraph is that these are the areas in which the UN works. I’d make everything about this first paragraph about that, emphasizing how data is poised to help.Reference

  256. This seems like a throwaway paragraph to me. It’s not necessary, especially if the first paragraph is better defined and clearly related to the goals of this project.Reference

  257. What are the specific challenges that relate between data and MDG goals? Too much data? Not enough? Does it not get to where it needs to be in a timely manner? Do we not know how to data yet to reach our goals? These are the kinds of questions I would like to see set up the challenge and be responded in this time paper. The opening paragraphs should sets the tone for the rest of the content.Reference

  258. Dear team, thanks for the work that has been done thus far. The draft report is really comprehensive. Harnessing the volume and detailed level data requires everyone to fully participate to translate data into information that will bring about positive change to the lives of many. Capacity building in the areas of data messaging (munging) is critical to bring structure to data. Collaboration is key amongst government, private sector, and civil society (CSOs) especially since CSO’s are at the coalface of communities and have very valuable administrative data that is critical to measure progress on a range of indicators.

  259. This paragraph and the chart GROSSLY under estimates both the amount of data and types of data that are available for exploitation with various tools. You site only two of at least 25 different data types that are currently being used in different data driven projects, not to mention the current advances in research labs that will fuel the next level of projects. For example, what about blogs, social media, news articles? And how about the multitude of reports and expert knowledge that the UN and others produce? These are all data that can and should be accessed and made available as part of a data driven revolution. And what about other data like financial data, bibliographic data, other websites with content of specific import to disparate UN missions? Policy and strategy papers? These are all pieces in this vast data puzzle all of which are candidates for the appropriate data driven technique to find direct content of relevance to a specific UN need. And how about the Internet of Things? That network of smart applications that will start to be able to provide offset data on behaviors? For better or for worse, these are all candidates for a data revolution and will need to be addressed in the context of appropriate usage for good works. This paragraph need to be completely reworked to show better understanding of the problem space and scope of what this data revolution really means. And the graph does no service to your hard work.Reference

  260. Surveys and mobile data are only 2 of 25+ data types that can and should be part of the data revolution, especially when it comes to the breadth and scope of UN missions. Either get rid of this chart or redo it to better show how much data is actually out there and the rate by which it is growing.Reference

  261. I haven’t had to write a report for the UN for over 15 years, but if space is a consideration, this seems like a throwaway paragraph.Reference

  262. I think this is the most important part of the introduction so far. There is a compelling reason why the Secretary General made this request, but it’s not stated. What is that reason? Why now? What is the vision here? What does the UN hope will emerge out of this revolution? This is a great starting paragraph from which to provide the emotional link for report readers, and through which to sell them on the process.Reference

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