Nudges Do Not Undermine Human Agency

Curated on Posted on by Stefaan Verhulst

Cass R. Sunstein in the Journal of Consumer Policy: “Some people believe that nudges undermine human agency, but with appropriate nudges, neither agency nor consumer freedom is at risk. On the contrary, nudges can promote both goals. In some contexts, they are indispensable. There is no opposition between education on the one hand and nudges on the other. Many nudges are educative. Even when they are not, they can complement, and not displace, consumer education….(More)”.

New ODI research shows open data reaching every sector of UK industry

Curated on Posted on by Stefaan Verhulst

ODI: “New research has been published today (1 June) by the Open Data Institute showing that open data is reaching every sector of UK industry.

In various forms, open data is being adopted by a wide variety of businesses – small and large, new and old, from right across the country. The findings from Open data means business: UK innovation across sectors and regions draw on 270 companies with a combined turnover of £92bn and over 500k employees, identified by the ODI as using, producing or investing in open data as part of their business. The project included desk research, surveys and interviews on the companies’ experiences.

Key findings from the research include:

  • Companies using open data come from many sectors; over 46% from outside the information and communication sector. These include finance & insurance, science & technology, business administration & support, arts & entertainment, health, retail, transportation, education and energy.
  • The most popular datasets for companies aregeospatial/mapping data (57%), transport data (43%) and environment data (42%).
  • 39% of companies innovating with open data are over 10 years old, with some more than 25 years old, proving open data isn’t just for new digital startups.
  • ‘Micro-enterprises’ (businesses with fewer than 10 employees) represented 70% of survey respondents, demonstrating athriving open data startup scene. These businesses are using it to create services, products and platforms. 8% of respondents were drawn from large companies of 251 or more employees….
  • The companies surveyed listed 25 different government sources for the data they use. Notably, Ordnance Survey data was cited most frequently, by 14% of the companies. The non-government source most commonly used was OpenStreetMap, an openly licenced map of the world created by volunteers….(More)

Measuring ‘governance’ to improve lives

Curated on Posted on by Stefaan Verhulst

Robert Rotberg at the Conversation: “…Citizens everywhere desire “good governance” – to be governed well within their nation-states, their provinces, their states and their cities.

Governance is more useful than “democracy” if we wish to understand how different political rulers and ruling elites satisfy the aspirations of their citizens.

But to make the notion of “governance” useful, we need both a practical definition and a method of measuring the gradations between good and bad governance.

What’s more, if we can measure well, we can diagnose weak areas of governance and, hence, seek ways to make the weak actors strong.

Governance, defined as “the performance of governments and the delivery of services by governments,” tells us if and when governments are in fact meeting the expectations of their constituents and providing for them effectively and responsibly.

Democracy outcomes, by contrast, are much harder to measure because the meaning of the very word itself is contested and impossible to measure accurately.

For the purposes of making policy decisions, if we seek to learn how citizens are faring under regime X or regime Y, we need to compare governance (not democracy) in those respective places.

In other words, governance is a construct that enables us to discern exactly whether citizens are progressing in meeting life’s goals.

Measuring governance: five bundles and 57 subcategories

Are citizens of a given country better off economically, socially and politically than they were in an earlier decade? Are their various human causes, such as being secure or being free, advancing? Are their governments treating them well, and attempting to respond to their various needs and aspirations and relieving them of anxiety?

Just comparing national gross domestic products (GDPs), life expectancies or literacy rates provides helpful distinguishing data, but governance data are more comprehensive, more telling and much more useful.

Assessing governance tells us far more about life in different developing societies than we would learn by weighing the varieties of democracy or “human development” in such places.

Government’s performance, in turn, is according to the scheme advanced in my book On Governance and in my Index of African Governance, the delivery to citizens of five bundles (divided into 57 underlying subcategories) of political goods that citizens within any kind of political jurisdiction demand.

The five major bundles are Security and Safety, Rule of Law and Transparency, Political Participation and Respect for Human Rights, Sustainable Economic Opportunity, and Human Development (education and health)….(More)”

Governing methods: policy innovation labs, design and data science in the digital governance of education

Curated on Posted on by Stefaan Verhulst

Paper by Ben Williamson in the Journal of Educational Administration and History: “Policy innovation labs are emerging knowledge actors and technical experts in the governing of education. The article offers a historical and conceptual account of the organisational form of the policy innovation lab. Policy innovation labs are characterised by specific methods and techniques of design, data science, and digitisation in public services such as education. The second half of the article details how labs promote the use of digital data analysis, evidence-based evaluation and ‘design-for-policy’ techniques as methods for the governing of education. In particular, they promote the ‘computational thinking’ associated with computer programming as a capacity required by a ‘reluctant state’ that is increasingly concerned to delegate its responsibilities to digitally enabled citizens with the ‘designerly’ capacities and technical expertise to ‘code’ solutions to public and social problems. Policy innovation labs are experimental laboratories trialling new methods within education for administering and governing the future of the state itself….(More)”

Law school students crowdsource commencement address

Curated on Posted on by Stefaan Verhulst

Chronicle of Higher Education: “Though higher education is constantly changing, commencement ceremonies have largely stayed the same. A graduating student at Stanford Law School is trying to change that.

Marta F. Belcher is crowdsourcing the speech she will give next month at the law school’s precommencement diploma ceremony, offering her classmates an opportunity to share in crafting that final message.

The point of a student commencement speaker, Ms. Belcher said, is to have someone who can speak to the student experience. But as she learned when she gave the student address at her undergraduate ceremony, it’s not easy for one person to represent hundreds, or even thousands, of classmates.

With all the online collaboration tools that are available today, Ms. Belcher saw the possibility of updating the tradition. So she competed to be the student speaker and invited classmates to contribute to her address.

“That was so clearly the right choice — for Stanford, especially, in the Silicon Valley at the cutting edge of innovation — that we should be the ones to sort of pioneer this new kind of way of writing a graduation speech,” she said.

After holding a number of meetings and fielding questions from skeptics, Ms. Belcher set up a wiki to gather ideas. The months-long effort was divided into three stages. First students would establish themes and ideas; next they would start contributing actual content for the speech; and finally, those pieces would be edited into a cohesive narrative during collaborative “edit-a-thons.”

Since the wiki went up, in February, 85 students have contributed to it….(More)”

Big Data. Big Obstacles.

Curated on Posted on by Stefaan Verhulst

Dalton Conley et al. in the Chronicle of Higher Education: “After decades of fretting over declining response rates to traditional surveys (the mainstay of 20th-century social research), an exciting new era would appear to be dawning thanks to the rise of big data. Social contagion can be studied by scraping Twitter feeds; peer effects are tested on Facebook; long-term trends in inequality and mobility can be assessed by linking tax records across years and generations; social-psychology experiments can be run on Amazon’s Mechanical Turk service; and cultural change can be mapped by studying the rise and fall of specific Google search terms. In many ways there has been no better time to be a scholar in sociology, political science, economics, or related fields.

However, what should be an opportunity for social science is now threatened by a three-headed monster of privatization, amateurization, and Balkanization. A coordinated public effort is needed to overcome all of these obstacles.

While the availability of social-media data may obviate the problem of declining response rates, it introduces all sorts of problems with the level of access that researchers enjoy. Although some data can be culled from the web—Twitter feeds and Google searches—other data sit behind proprietary firewalls. And as individual users tune up their privacy settings, the typical university or independent researcher is increasingly locked out. Unlike federally funded studies, there is no mandate for Yahoo or Alibaba to make its data publicly available. The result, we fear, is a two-tiered system of research. Scientists working for or with big Internet companies will feast on humongous data sets—and even conduct experiments—and scholars who do not work in Silicon Valley (or Alley) will be left with proverbial scraps….

To address this triple threat of privatization, amateurization, and Balkanization, public social science needs to be bolstered for the 21st century. In the current political and economic climate, social scientists are not waiting for huge government investment like we saw during the Cold War. Instead, researchers have started to knit together disparate data sources by scraping, harmonizing, and geo­coding any and all information they can get their hands on.

Currently, many firms employ some well-trained social and behavioral scientists free to pursue their own research; likewise, some companies have programs by which scholars can apply to be in residence or work with their data extramurally. However, as Facebook states, its program is “by invitation only and requires an internal Facebook champion.” And while Google provides services like Ngram to the public, such limited efforts at data sharing are not enough for truly transparent and replicable science….(More)”

 

Contest Aims to Harness Low-Cost Devices to Help the Poor

Curated on Posted on by Stefaan Verhulst

Steve Lohr in the New York Times: “The timing and technology are right to bring the power of digital sensing to the poor to improve health, safety and education.

That is the animating assumption behind a new project announced on Tuesday. The initiative is led by Unicef and ARM, the British chip designer whose microprocessors power most smartphones and tablets. They are being joined by Frog, the San Francisco-based product strategy and design firm, along with people described as coaches and advisers from companies and organizations including Google, Orange, Singularity University, the Red Cross and the Senseable City Lab at the Massachusetts Institute of Technology.

The long-term ambition is to jump-start an industrial ecosystem for sensing and data technology that serves the needs of mothers and children in developing nations.

The project, called Wearables for Good, is beginning with a contest to generate ideas. Applications can be submitted online on the project’s website until August 4. Two winners will be selected in the fall. Each will receive $15,000, and assistance and advice from ARM, Frog and others on translating their ideas into a product and perhaps a company.

The online application lists the required characteristics for device ideas. They should be, according to the form, “cost-effective, rugged and durable, low-power and scalable.” The form offers no price limits, but it is safe to assume the project is looking for devices priced far less than an Apple Watch or a Fitbit device.

…. the Wearables for Good project goes further, focusing less on aggregated data and more on personal monitoring. “This is the next level of what we’re doing,” said Erica Kochi, co-founder of Unicef Innovation, which pursues technology initiatives that advance the agency’s goals….(More)”

Selected Readings on Data Governance

Curated on Posted on by Jos Berens

Jos Berens (Centre for Innovation, Leiden University) and Stefaan G. Verhulst (GovLab)

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of data governance was originally published in 2015.

Context
The field of Data Collaboratives is premised on the idea that sharing and opening-up private sector datasets has great – and yet untapped – potential for promoting social good. At the same time, the potential of data collaboratives depends on the level of societal trust in the exchange, analysis and use of the data exchanged. Strong data governance frameworks are essential to ensure responsible data use. Without such governance regimes, the emergent data ecosystem will be hampered and the (perceived) risks will dominate the (perceived) benefits. Further, without adopting a human-centered approach to the design of data governance frameworks, including iterative prototyping and careful consideration of the experience, the responses may fail to be flexible and targeted to real needs.

Selected Readings List (in alphabetical order)

Annotated Selected Readings List (in alphabetical order)

Better Place Lab, “Privacy, Transparency and Trust.” Mozilla, 2015. Available from: http://www.betterplace-lab.org/privacy-report.

  • This report looks specifically at the risks involved in the social sector having access to datasets, and the main risks development organizations should focus on to develop a responsible data use practice.
  • Focusing on five specific countries (Brazil, China, Germany, India and Indonesia), the report displays specific country profiles, followed by a comparative analysis centering around the topics of privacy, transparency, online behavior and trust.
  • Some of the key findings mentioned are:
    • A general concern on the importance of privacy, with cultural differences influencing conception of what privacy is.
    • Cultural differences determining how transparency is perceived, and how much value is attached to achieving it.
    • To build trust, individuals need to feel a personal connection or get a personal recommendation – it is hard to build trust regarding automated processes.

Montjoye, Yves Alexandre de; Kendall, Jake and; Kerry, Cameron F. “Enabling Humanitarian Use of Mobile Phone Data.” The Brookings Institution, 2015. Available from: http://www.brookings.edu/research/papers/2014/11/12-enabling-humanitarian-use-mobile-phone-data.

  • Focussing in particular on mobile phone data, this paper explores ways of mitigating privacy harms involved in using call detail records for social good.
  • Key takeaways are the following recommendations for using data for social good:
    • Engaging companies, NGOs, researchers, privacy experts, and governments to agree on a set of best practices for new privacy-conscientious metadata sharing models.
    • Accepting that no framework for maximizing data for the public good will offer perfect protection for privacy, but there must be a balanced application of privacy concerns against the potential for social good.
    • Establishing systems and processes for recognizing trusted third-parties and systems to manage datasets, enable detailed audits, and control the use of data so as to combat the potential for data abuse and re-identification of anonymous data.
    • Simplifying the process among developing governments in regards to the collection and use of mobile phone metadata data for research and public good purposes.

Centre for Democracy and Technology, “Health Big Data in the Commercial Context.” Centre for Democracy and Technology, 2015. Available from: https://cdt.org/insight/health-big-data-in-the-commercial-context/.

  • Focusing particularly on the privacy issues related to using data generated by individuals, this paper explores the overlap in privacy questions this field has with other data uses.
  • The authors note that although the Health Insurance Portability and Accountability Act (HIPAA) has proven a successful approach in ensuring accountability for health data, most of these standards do not apply to developers of the new technologies used to collect these new data sets.
  • For non-HIPAA covered, customer facing technologies, the paper bases an alternative framework for consideration of privacy issues. The framework is based on the Fair Information Practice Principles, and three rounds of stakeholder consultations.

Center for Information Policy Leadership, “A Risk-based Approach to Privacy: Improving Effectiveness in Practice.” Centre for Information Policy Leadership, Hunton & Williams LLP, 2015. Available from: https://www.informationpolicycentre.com/uploads/5/7/1/0/57104281/white_paper_1-a_risk_based_approach_to_privacy_improving_effectiveness_in_practice.pdf.

  • This white paper is part of a project aiming to explain what is often referred to as a new, risk-based approach to privacy, and the development of a privacy risk framework and methodology.
  • With the pace of technological progress often outstripping the capabilities of privacy officers to keep up, this method aims to offer the ability to approach privacy matters in a structured way, assessing privacy implications from the perspective of possible negative impact on individuals.
  • With the intended outcomes of the project being “materials to help policy-makers and legislators to identify desired outcomes and shape rules for the future which are more effective and less burdensome”, insights from this paper might also feed into the development of innovative governance mechanisms aimed specifically at preventing individual harm.

Centre for Information Policy Leadership, “Data Governance for the Evolving Digital Market Place”, Centre for Information Policy Leadership, Hunton & Williams LLP, 2011. Available from: http://www.huntonfiles.com/files/webupload/CIPL_Centre_Accountability_Data_Governance_Paper_2011.pdf.

  • This paper argues that as a result of the proliferation of large scale data analytics, new models governing data inferred from society will shift responsibility to the side of organizations deriving and creating value from that data.
  • It is noted that, with the reality of the challenge corporations face of enabling agile and innovative data use “In exchange for increased corporate responsibility, accountability [and the governance models it mandates, ed.] allows for more flexible use of data.”
  • Proposed as a means to shift responsibility to the side of data-users, the accountability principle has been researched by a worldwide group of policymakers. Tailing the history of the accountability principle, the paper argues that it “(…) requires that companies implement programs that foster compliance with data protection principles, and be able to describe how those programs provide the required protections for individuals.”
  • The following essential elements of accountability are listed:
    • Organisation commitment to accountability and adoption of internal policies consistent with external criteria
    • Mechanisms to put privacy policies into effect, including tools, training and education
    • Systems for internal, ongoing oversight and assurance reviews and external verification
    • Transparency and mechanisms for individual participation
    • Means of remediation and external enforcement

Crawford, Kate; Schulz, Jason. “Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harm.” NYU School of Law, 2014. Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2325784&download=yes.

  • Considering the privacy implications of large-scale analysis of numerous data sources, this paper proposes the implementation of a ‘procedural data due process’ mechanism to arm data subjects against potential privacy intrusions.
  • The authors acknowledge that some privacy protection structures already know similar mechanisms. However, due to the “inherent analytical assumptions and methodological biases” of big data systems, the authors argue for a more rigorous framework.

Letouze, Emmanuel, and; Vinck, Patrick. “The Ethics and Politics of Call Data Analytics”, DataPop Alliance, 2015. Available from: http://static1.squarespace.com/static/531a2b4be4b009ca7e474c05/t/54b97f82e4b0ff9569874fe9/1421442946517/WhitePaperCDRsEthicFrameworkDec10-2014Draft-2.pdf.

  • Focusing on the use of Call Detail Records (CDRs) for social good in development contexts, this whitepaper explores both the potential of these datasets – in part by detailing recent successful efforts in the space – and political and ethical constraints to their use.
  • Drawing from the Menlo Report Ethical Principles Guiding ICT Research, the paper explores how these principles might be unpacked to inform an ethics framework for the analysis of CDRs.

Data for Development External Ethics Panel, “Report of the External Ethics Review Panel.” Orange, 2015. Available from: http://www.d4d.orange.com/fr/content/download/43823/426571/version/2/file/D4D_Challenge_DEEP_Report_IBE.pdf.

  • This report presents the findings of the external expert panel overseeing the Orange Data for Development Challenge.
  • Several types of issues faced by the panel are described, along with the various ways in which the panel dealt with those issues.

Federal Trade Commission Staff Report, “Mobile Privacy Disclosures: Building Trust Through Transparency.” Federal Trade Commission, 2013. Available from: www.ftc.gov/os/2013/02/130201mobileprivacyreport.pdf.

  • This report looks at ways to address privacy concerns regarding mobile phone data use. Specific advise is provided for the following actors:
    • Platforms, or operating systems providers
    • App developers
    • Advertising networks and other third parties
    • App developer trade associations, along with academics, usability experts and privacy researchers

Mirani, Leo. “How to use mobile phone data for good without invading anyone’s privacy.” Quartz, 2015. Available from: http://qz.com/398257/how-to-use-mobile-phone-data-for-good-without-invading-anyones-privacy/.

  • This paper considers the privacy implications of using call detail records for social good, and ways to mitigate risks of privacy intrusion.
  • Taking example of the Orange D4D challenge and the anonymization strategy that was employed there, the paper describes how classic ‘anonymization’ is often not enough. The paper then lists further measures that can be taken to ensure adequate privacy protection.

Bernholz, Lucy. “Several Examples of Digital Ethics and Proposed Practices” Stanford Ethics of Data conference, 2014, Available from: http://www.scribd.com/doc/237527226/Several-Examples-of-Digital-Ethics-and-Proposed-Practices.

  • This list of readings prepared for Stanford’s Ethics of Data conference lists some of the leading available literature regarding ethical data use.

Abrams, Martin. “A Unified Ethical Frame for Big Data Analysis.” The Information Accountability Foundation, 2014. Available from: http://www.privacyconference2014.org/media/17388/Plenary5-Martin-Abrams-Ethics-Fundamental-Rights-and-BigData.pdf.

  • Going beyond privacy, this paper discusses the following elements as central to developing a broad framework for data analysis:
    • Beneficial
    • Progressive
    • Sustainable
    • Respectful
    • Fair

Lane, Julia; Stodden, Victoria; Bender, Stefan, and; Nissenbaum, Helen, “Privacy, Big Data and the Public Good”, Cambridge University Press, 2014. Available from: http://www.dataprivacybook.org.

  • This book treats the privacy issues surrounding the use of big data for promoting the public good.
  • The questions being asked include the following:
    • What are the ethical and legal requirements for scientists and government officials seeking to serve the public good without harming individual citizens?
    • What are the rules of engagement?
    • What are the best ways to provide access while protecting confidentiality?
    • Are there reasonable mechanisms to compensate citizens for privacy loss?

Richards, Neil M, and; King, Jonathan H. “Big Data Ethics”. Wake Forest Law Review, 2014. Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2384174.

  • This paper describes the growing impact of big data analytics on society, and argues that because of this impact, a set of ethical principles to guide data use is called for.
  • The four proposed themes are: privacy, confidentiality, transparency and identity.
  • Finally, the paper discusses how big data can be integrated into society, going into multiple facets of this integration, including the law, roles of institutions and ethical principles.

OECD, “OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data”. Available from: http://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm.

  • A globally used set of principles to inform thought about handling personal data, the OECD privacy guidelines serve as one the leading standards for informing privacy policies and data governance structures.
  • The basic principles of national application are the following:
    • Collection Limitation Principle
    • Data Quality Principle
    • Purpose Specification Principle
    • Use Limitation Principle
    • Security Safeguards Principle
    • Openness Principle
    • Individual Participation Principle
    • Accountability Principle

The White House Big Data and Privacy Working Group, “Big Data: Seizing Opportunities, Preserving Values”, White House, 2015. Available from: https://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_5.1.14_final_print.pdf.

  • Documenting the findings of the White House big data and privacy working group, this report lists i.a. the following key recommendations regarding data governance:
    • Bringing greater transparency to the data services industry
    • Stimulating international conversation on big data, with multiple stakeholders
    • With regard to educational data: ensuring data is used for the purpose it is collected for
    • Paying attention to the potential for big data to facilitate discrimination, and expanding technical understanding to stop discrimination

William Hoffman, “Pathways for Progress” World Economic Forum, 2015. Available from: http://www3.weforum.org/docs/WEFUSA_DataDrivenDevelopment_Report2015.pdf.

  • This paper treats i.a. the lack of well-defined and balanced governance mechanisms as one of the key obstacles preventing particularly corporate sector data from being shared in a controlled space.
  • An approach that balances the benefits against the risks of large scale data usage in a development context, building trust among all stake holders in the data ecosystem, is viewed as key.
  • Furthermore, this whitepaper notes that new governance models are required not just by the growing amount of data and analytical capacity, and more refined methods for analysis. The current “super-structure” of information flows between institutions is also seen as one of the key reasons to develop alternatives to the current – outdated – approaches to data governance.

What Is Community Anyway?

Curated on Posted on by Stefaan Verhulst

David M. Chavis & Kien Lee at Stanford Social Innovation Review: “Community” is so easy to say. The word itself connects us with each other. It describes an experience so common that we never really take time to explain it. It seems so simple, so natural, and so human. In the social sector, we often add it to the names of social innovations as a symbol of good intentions (for example, community mental health, community policing, community-based philanthropy, community economic development).

But the meaning of community is complex. And, unfortunately, insufficient understanding of what a community is and its role in the lives of people in diverse societies has led to the downfall of many well-intended “community” efforts.

Adding precision to our understanding of community can help funders and evaluators identify, understand, and strengthen the communities they work with. There has been a great deal of research in the social sciences about what a human community is (see for example, Chavis and Wandersman, 1990; Nesbit, 1953; Putnam, 2000). Here, we blend that research with our experience as evaluators and implementers of community change initiatives.

It’s about people.

First and foremost, community is not a place, a building, or an organization; nor is it an exchange of information over the Internet. Community is both a feeling and a set of relationships among people. People form and maintain communities to meet common needs….

People live in multiple communities.

Since meeting common needs is the driving force behind the formation of communities, most people identify and participate in several of them, often based on neighborhood, nation, faith, politics, race or ethnicity, age, gender, hobby, or sexual orientation….

Communities are nested within each other.

Just like Russian Matryoshka dolls, communities often sit within other communities. For example, in a neighborhood—a community in and of itself—there may be ethnic or racial communities, communities based on people of different ages and with different needs, and communities based on common economic interests….

Communities have formal and informal institutions.

Communities form institutions—what we usually think of as large organizations and systems such as schools, government, faith, law enforcement, or the nonprofit sector—to more effectively fulfill their needs….

Communities are organized in different ways.

Every community is organized to meet its members’ needs, but they operate differently based on the cultures, religions, and other experiences of their members. For example, while the African American church is generally understood as playing an important role in promoting health education and social justice for that community, not all faith institutions such as the mosque or Buddhist temple are organized and operate in the same way….(More)

Data for Development

Curated on Posted on by Stefaan Verhulst

Jeffrey D. Sachs at Project Syndicate: “The data revolution is rapidly transforming every part of society. Elections are managed with biometrics, forests are monitored by satellite imagery, banking has migrated from branch offices to smartphones, and medical x-rays are examined halfway around the world. With a bit of investment and foresight, spelled out in a new report, prepared by the UN Sustainable Development Solutions Network (SDSN), on Data for Development, the data revolution can drive a sustainable development revolution, and accelerate progress toward ending poverty, promoting social inclusion, and protecting the environment.
The world’s governments will adopt the new Sustainable Development Goals (SDGs) at a special United Nations summit on September 25. The occasion will likely be the largest gathering of world leaders in history, as some 170 heads of state and government adopt shared goals that will guide global development efforts until 2030. Of course, goals are easier to adopt than to achieve. So we will need new tools, including new data systems, to turn the SDGs into reality by 2030. In developing these new data systems, governments, businesses, and civil-society groups should promote four distinct purposes.

The first, and most important, is data for service delivery. The data revolution gives governments and businesses new and greatly improved ways to deliver services, fight corruption, cut red tape, and guarantee access in previously isolated places. Information technology is already revolutionizing the delivery of health care, education, governance, infrastructure (for example, prepaid electricity), banking, emergency response, and much more.
The second purpose is data for public management. Officials can now maintain real-time dashboards informing them of the current state of government facilities, transport networks, emergency relief operations, public health surveillance, violent crimes, and much more. Citizen feedback can also improve functioning, such as by crowd-sourcing traffic information from drivers. Geographic information systems (GIS) allow for real-time monitoring across local governments and districts in far-flung regions.
The third purpose is data for accountability of governments and businesses. It is a truism that government bureaucracies cut corners, hide gaps in service delivery, exaggerate performance, or, in the worst cases, simply steal when they can get away with it. Many businesses are no better. The data revolution can help to ensure that verifiable data are accessible to the general public and the intended recipients of public and private services. When services do not arrive on schedule (owing to, say, a bottleneck in construction or corruption in the supply chain), the data system will enable the public to pinpoint problems and hold governments and businesses to account.
Finally, the data revolution should enable the public to know whether or not a global goal or target has actually been achieved. The Millennium Development Goals, which were set in the year 2000, established quantitative targets for the year 2015. But, although we are now in the MDGs’ final year, we still lack precise knowledge of whether certain MDG targets have been achieved, owing to the absence of high-quality, timely data. Some of the most important MDG targets are reported with a lag of several years. The World Bank, for example, has not published detailed poverty data since 2010…..(More)”