Data Governance in the Digital Age

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Centre for International Governance Innovation: “Data is being hailed as “the new oil.” The analogy seems appropriate given the growing amount of data being collected, and the advances made in its gathering, storage, manipulation and use for commercial, social and political purposes.

Big data and its application in artificial intelligence, for example, promises to transform the way we live and work — and will generate considerable wealth in the process. But data’s transformative nature also raises important questions around how the benefits are shared, privacy, public security, openness and democracy, and the institutions that will govern the data revolution.

The delicate interplay between these considerations means that they have to be treated jointly, and at every level of the governance process, from local communities to the international arena. This series of essays by leading scholars and practitioners, which is also published as a special report, will explore topics including the rationale for a data strategy, the role of a data strategy for Canadian industries, and policy considerations for domestic and international data governance…

RATIONALE OF A DATA STRATEGY

THE ROLE OF A DATA STRATEGY FOR CANADIAN INDUSTRIES

BALANCING PRIVACY AND COMMERCIAL VALUES

DOMESTIC POLICY FOR DATA GOVERNANCE

INTERNATIONAL POLICY CONSIDERATIONS

EPILOGUE

Ten Reasons Not to Measure Impact—and What to Do Instead

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Essay by Mary Kay Gugerty & Dean Karlan in the Stanford Social Innovation Review: “Good impact evaluations—those that answer policy-relevant questions with rigor—have improved development knowledge, policy, and practice. For example, the NGO Living Goods conducted a rigorous evaluation to measure the impact of its community health model based on door-to-door sales and promotions. The evidence of impact was strong: Their model generated a 27-percent reduction in child mortality. This evidence subsequently persuaded policy makers, replication partners, and major funders to support the rapid expansion of Living Goods’ reach to five million people. Meanwhile, rigorous evidence continues to further validate the model and help to make it work even better.

Of course, not all rigorous research offers such quick and rosy results. Consider the many studies required to discover a successful drug and the lengthy process of seeking regulatory approval and adoption by the healthcare system. The same holds true for fighting poverty: Innovations for Poverty Action (IPA), a research and policy nonprofit that promotes impact evaluations for finding solutions to global poverty, has conducted more than 650 randomized controlled trials (RCTs) since its inception in 2002. These studies have sometimes provided evidence about how best to use scarce resources (e.g., give away bed nets for free to fight malaria), as well as how to avoid wasting them (e.g., don’t expand traditional microcredit). But the vast majority of studies did not paint a clear picture that led to immediate policy changes. Developing an evidence base is more like building a mosaic: Each individual piece does not make the picture, but bit by bit a picture becomes clearer and clearer.

How do these investments in evidence pay off? IPA estimated the benefits of its research by looking at its return on investment—the ratio of the benefit from the scale-up of the demonstrated large-scale successes divided by the total costs since IPA’s founding. The ratio was 74x—a huge result. But this is far from a precise measure of impact, since IPA cannot establish what would have happened had IPA never existed. (Yes, IPA recognizes the irony of advocating for RCTs while being unable to subject its own operations to that standard. Yet IPA’s approach is intellectually consistent: Many questions and circumstances do not call for RCTs.)

Even so, a simple thought exercise helps to demonstrate the potential payoff. IPA never works alone—all evaluations and policy engagements are conducted in partnership with academics and implementing organizations, and increasingly with governments. Moving from an idea to the research phase to policy takes multiple steps and actors, often over many years. But even if IPA deserves only 10 percent of the credit for the policy changes behind the benefits calculated above, the ratio of benefits to costs is still 7.4x. That is a solid return on investment.

Despite the demonstrated value of high-quality impact evaluations, a great deal of money and time has been wasted on poorly designed, poorly implemented, and poorly conceived impact evaluations. Perhaps some studies had too small of a sample or paid insufficient attention to establishing causality and quality data, and hence any results should be ignored; others perhaps failed to engage stakeholders appropriately, and as a consequence useful results were never put to use.

The push for more and more impact measurement can not only lead to poor studies and wasted money, but also distract and take resources from collecting data that can actually help improve the performance of an effort. To address these difficulties, we wrote a book, The Goldilocks Challenge, to help guide organizations in designing “right-fit” evidence strategies. The struggle to find the right fit in evidence resembles the predicament that Goldilocks faces in the classic children’s fable. Goldilocks, lost in the forest, finds an empty house with a large number of options: chairs, bowls of porridge, and beds of all sizes. She tries each but finds that most do not suit her: The porridge is too hot or too cold, the bed too hard or too soft—she struggles to find options that are “just right.” Like Goldilocks, the social sector has to navigate many choices and challenges to build monitoring and evaluation systems that fit their needs. Some will push for more and more data; others will not push for enough….(More)”.

City Data Exchange – Lessons Learned From A Public/Private Data Collaboration

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Report by the Municipality of Copenhagen: “The City Data Exchange (CDE) is the product of a collaborative project between the Municipality of Copenhagen, the Capital Region of Denmark, and Hitachi. The purpose of the project is to examine the possibilities of creating a marketplace for the exchange of data between public and private organizations.

The CDE consists of three parts:

  • A collaboration between the different partners on supply, and demand of specific data;
  • A platform for selling and purchasing data aimed at both public, and private organizations;
  • An effort to establish further experience in the field of data exchange between public, and private organizations.

In 2013, the City of Copenhagen, and the Copenhagen Region decided to invest in the creation of a marketplace for the exchange of public, and private sector data. The initial investment was meant as a seed towards a self-sustained marketplace. This was an innovative approach to test the readiness of the market to deliver new data-sharing solutions.

The CDE is the result of a tender by the Municipality of Copenhagen and the Capital Region of Denmark in 2015. Hitachi Consulting won the tender and has invested, and worked with the Municipality of Copenhagen, and the Capital Region of Denmark to establish an organization and a technical platform.

The City Data Exchange (CDE) has closed a gap in regional data infrastructure. Both public-and private sector organizations have used the CDE to gain insights into data use cases, new external data sources, GDPR issues, and to explore the value of their data. Before the CDE was launched, there were only a few options available to purchase or sell data.

The City and the Region of Copenhagen are utilizing the insights from the CDE project to improve their internal activities and to shape new policies. The lessons from the CDE also provide insights into a wider national infrastructure for effective data sharing. Based on the insights from approximately 1000 people that the CDE has been in contact with, the recommendations are:

  • Start with the use case, as it is key to engage the data community that will use the data;
  • Create a data competence hub, where the data community can meet and get support;
  • Create simple standards and guidelines for data publishing.

The following paper presents some of the key findings from our work with the CDE. It has been compiled by Smart City Insights on behalf of the partners of the City Data Exchange project…(More)”.

Free Speech is a Triangle

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Essay by Jack Balkin: “The vision of free expression that characterized much of the twentieth century is inadequate to protect free expression today.

The twentieth century featured a dyadic or dualist model of speech regulation with two basic kinds of players: territorial governments on the one hand, and speakers on the other. The twenty-first century model is pluralist, with multiple players. It is easiest to think of it as a triangle. On one corner are nation states and the European Union. On the second corner are privately-owned Internet infrastructure companies, including social media companies, search engines, broadband providers, and electronic payment systems. On the third corner are many different kinds of speakers, legacy media, civil society organizations, hackers, and trolls.

Territorial goverments continue to regulate speakers and legacy media through traditional or “old-school” speech regulation. But nation states and the European Union also now employ “new-school” speech regulation that is aimed at Internet infrastructure owners and designed to get these private companies to surveil, censor, and regulate speakers for them. Finally, infrastructure companies like Facebook also regulate and govern speakers through techniques of private governance and surveillance.

The practical ability to speak in the digital world emerges from the struggle for power between these various forces, with old-school, new-school and private regulation directed at speakers, and both nation states and civil society organizations pressuring infrastructure owners to regulate speech.

If the characteristic feature of free speech regulation in our time is a triangle that combines new school speech regulation with private governance, then the best way to protect free speech values today is to combat and compensate for that triangle’s evolving logic of public and private regulation. The first goal is to prevent or ameliorate as much as possible collateral censorship and new forms of digital prior restraint. The second goal is to protect people from new methods of digital surveillance and manipulation—methods that emerged from the rise of large multinational companies that depend on data collection, surveillance, analysis, control, and distribution of personal data.

This essay describes how nation states should and should not regulate the digital infrastructure consistent with the values of freedom of speech and press; it emphasizes that different models of regulation are appropriate for different parts of the digital infrastructure. Some parts of the digital infrastructure are best regulated along the lines of common carriers or places of public accommodation. But governments should not impose First Amendment-style or common carriage obligations on social media and search engines. Rather, governments should require these companies to provide due process toward their end-users. Governments should also treat these companies as information fiduciaries who have duties of good faith and non-manipulation toward their end-users. Governments can implement all of these reforms—properly designed—consistent with constitutional guarantees of free speech and free press….(More)”.

Doing Research In and On the Digital: Research Methods across Fields of Inquiry

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Book edited by Cristina Costa and Jenna Condie: “As a social space, the web provides researchers both with a tool and an environment to explore the intricacies of everyday life. As a site of mediated interactions and interrelationships, the ‘digital’ has evolved from being a space of information to a space of creation, thus providing new opportunities regarding how, where and, why to conduct social research.

Doing Research In and On the Digital aims to deliver on two fronts: first, by detailing how researchers are devising and applying innovative research methods for and within the digital sphere, and, secondly, by discussing the ethical challenges and issues implied and encountered in such approaches.

In two core Parts, this collection explores:

  • content collection: methods for harvesting digital data
  • engaging research informants: digital participatory methods and data stories .

With contributions from a diverse range of fields such as anthropology, sociology, education, healthcare and psychology, this volume will particularly appeal to post-graduate students and early career researchers who are navigating through new terrain in their digital-mediated research endeavours….(More)”.

Skills for a Lifetime

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Nate Silver’s commencement address at Kenyon College: “….Power has shifted toward people and companies with a lot of proficiency in data science.

I obviously don’t think that’s entirely a bad thing. But it’s by no means entirely a good thing, either. You should still inherently harbor some suspicion of big, powerful institutions and their potentially self-serving and short-sighted motivations. Companies and governments that are capable of using data in powerful ways are also capable of abusing it.

What worries me the most, especially at companies like Facebook and at other Silicon Valley behemoths, is the idea that using data science allows one to remove human judgment from the equation. For instance, in announcing a recent change to Facebook’s News Feed algorithm, Mark Zuckerberg claimed that Facebook was not “comfortable” trying to come up with a way to determine which news organizations were most trustworthy; rather, the “most objective” solution was to have readers vote on trustworthiness instead. Maybe this is a good idea and maybe it isn’t — but what bothered me was in the notion that Facebook could avoid responsibility for its algorithm by outsourcing the judgment to its readers.

I also worry about this attitude when I hear people use terms such as “artificial intelligence” and “machine learning” (instead of simpler terms like “computer program”). Phrases like “machine learning” appeal to people’s notion of a push-button solution — meaning, push a button, and the computer does all your thinking for you, no human judgment required.

But the reality is that working with data requires lots of judgment. First, it requires critical judgment — and experience — when drawing inferences from data. And second, it requires moral judgment in deciding what your goals are and in establishing boundaries for your work.

Let’s talk about that first type of judgment — critical judgment. The more experience you have in working with different data sets, the more you’ll realize that the correct interpretation of the data is rarely obvious, and that the obvious-seeming interpretation isn’t always correct. Sometimes changing a single assumption or a single line of code can radically change your conclusion. In the 2016 U.S. presidential election, for instance, there were a series of models that all used almost exactly the same inputs — but they ranged in giving Trump as high as roughly a one-in-three chance of winning the presidency (that was FiveThirtyEight’s model) to as low as one chance in 100, based on fairly subtle aspects of how each algorithm was designed….(More)”.

The 2018 Atlas of Sustainable Development Goals: an all-new visual guide to data and development

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World Bank Data Team: “We’re pleased to release the 2018 Atlas of Sustainable Development Goals. With over 180 maps and charts, the new publication shows the progress societies are making towards the 17 SDGs.

It’s filled with annotated data visualizations, which can be reproducibly built from source code and data. You can view the SDG Atlas online, download the PDF publication (30Mb), and access the data and source code behind the figures.

This Atlas would not be possible without the efforts of statisticians and data scientists working in national and international agencies around the world. It is produced in collaboration with the professionals across the World Bank’s data and research groups, and our sectoral global practices.

Trends and analysis for the 17 SDGs

The Atlas draws on World Development Indicators, a database of over 1,400 indicators for more than 220 economies, many going back over 50 years. For example, the chapter on SDG4 includes data from the UNESCO Institute for Statistics on education and its impact around the world.

Throughout the Atlas, data are presented by country, region and income group and often disaggregated by sex, wealth and geography.

The Atlas also explores new data from scientists and researchers where standards for measuring SDG targets are still being developed. For example, the chapter on SDG14 features research led by Global Fishing Watch, published this year in Science. Their team has tracked over 70,000 industrial fishing vessels from 2012 to 2016, processed 22 billion automatic identification system messages to map and quantify fishing around the world….(More)”.

Digital Government Review of Colombia

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OECD Report: “This review analyses the shift from e-government to digital government in Colombia. It looks at the governance framework for digital government, the use of digital platforms and open data to engage and collaborate with citizens, conditions for a data-driven public sector, and policy coherence in a context of significant regional disparities. It provides concrete policy recommendations on how digital technologies and data can be harnessed for citizen-driven policy making and public service delivery…(More)”.

What kind of Evidence Influences local officials? A great example from Guatemala

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Paper  by Walter Flores: “Between 2007 and up to now, we have implemented five different methods for gathering evidence:

1) Surveys of health clinics with random sampling,

2) Surveys using tracers and convenience-based sampling,

3) Life histories of the users of health services,

4) User complaints submitted via text messages,

5) Video and photography documenting service delivery problems.

Each of these methods was deployed for a period of 2-3 years and accompanied by detailed monitoring to track its effects on two outcome variables:

1) the level of community participation in planning, data collection and analysis; and

2) the responsiveness of the authorities to the evidence presented.

Our initial intervention generated evidence by surveying a random sample of health clinics—widely considered to be a highly rigorous method for collecting evidence. As the surveys were long and technically complicated, participation from the community was close to zero. Yet our expectation was that, given its scientific rigor, authorities would be responsive to the evidence we presented. The government instead used technical methodological objections as a pretext to reject the service delivery problems we identified. It was clear that such arguments were an excuse and authorities did not want to act.

Flores fig 1Our next effort was to simplify the survey and involve communities in surveying, analysis, and report writing. However, as the table shows, participation was still “minimal,” as was the responsiveness of the authorities. Many community members still struggled to participate and the authorities rejected the evidence as unreliable, again citing methodological concerns. Together with community leaders, we decided to move away from surveys altogether, so authorities could no longer use technical arguments to disregard the evidence.

For our next method, we introduced collecting life-stories of real patients and users of health services. The decision about this new method was taken together with communities. Community members were trained to identify cases of poor service delivery, interview users, and write down their experiences. These testimonies vividly described the impact of poor health services: children unable to go to school because they needed to attend to sick relatives; sick parents unable to care for young children; breadwinners unable go to work, leaving families destitute.

This type of evidence changed the meetings between community leaders and authorities considerably, shifting from arguments over data to discussing the struggles real people faced due to nonresponsive services. After a year of responding to individual life-stories, however, authorities started to treat the information presented as “isolated cases” and became less responsive.

We regrouped again with community leaders to reflect on how to further boost community participation and achieve a response from authorities. We agreed that more agile and less burdensome methods for community volunteers to collect and disseminate evidence might increase the response from authorities. After reviewing different options, we agreed to build a complaint system that allowed users to send coded text messages to an open-access platform….(More)”.

Plunging response rates to household surveys worry policymakers

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The Economist: “Response rates to surveys are plummeting all across the rich world. Last year only around 43% of households contacted by the British government responded to the LFS, down from 70% in 2001 (see chart). In America the share of households responding to the Current Population Survey (CPS) has fallen from 94% to 85% over the same period. The rest of Europe and Canada have seen similar trends.

Poor response rates drain budgets, as it takes surveyors more effort to hunt down interviewees. And a growing reluctance to give interviewers information threatens the quality of the data. Politicians often complain about inaccurate election polls. Increasingly misleading economic surveys would be even more disconcerting.

Household surveys derive their power from randomness. Since it is impractical to get every citizen to complete a long questionnaire regularly, statisticians interview what they hope is a representative sample instead. But some types are less likely to respond than others—people who live in flats not houses, for example. A study by Christopher Bollinger of the University of Kentucky and three others matched data from the CPS with social-security records and found that poorer and very rich households were more likely to ignore surveyors than middle-income ones. Survey results will be skewed if the types who do not answer are different from those who do, or if certain types of people are more loth to answer some questions, or more likely to fib….

Statisticians have been experimenting with methods of improving response rates: new ways to ask questions, or shorter questionnaires, for example. Payment raises response rates, and some surveys offer more money for the most reluctant interviewees. But such persistence can have drawbacks. One study found that more frequent attempts to contact interviewees raised the average response rate, but lowered the average quality of answers.

Statisticians have also been exploring supplementary data sources, including administrative data. Such statistics come with two big advantages. One is that administrative data sets can include many more people and observations than is practical in a household survey, giving researchers the statistical power to run more detailed studies. Another is that governments already collect them, so they can offer huge cost savings over household surveys. For instance, Finland’s 2010 census, which was based on administrative records rather than surveys, cost its government just €850,000 ($1.1m) to produce. In contrast, America’s government spent $12.3bn on its 2010 census, roughly 200 times as much on a per-person basis.

Recent advances in computing mean that vast data sets are no longer too unwieldy for use by researchers. However, in many rich countries (those in Scandinavia are exceptions), socioeconomic statistics are collected by several agencies, meaning that researchers who want to combine, say, health records with tax data, face formidable bureaucratic and legal challenges.

Governments in English-speaking countries are especially keen to experiment. In January HMRC, the British tax authority, started publishing real-time tax data as an “experimental statistic” to be compared with labour-market data from household surveys. Two-fifths of Canada’s main statistical agency’s programmes are based at least in part on administrative records. Last year, Britain passed the Digital Economy Act, which will give its Office of National Statistics (ONS) the right to requisition data from other departments and from private sources for statistics-and-research purposes. America is exploring using such data as part of its 2020 census.

Administrative data also have their limitations (see article). They are generally not designed to be used in statistical analyses. A data set on income taxes might be representative of the population receiving benefits or earning wages, but not the population as a whole. Most important, some things are not captured in administrative records, such as well-being, informal employment and religious affiliation….(More)”.