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Melissa Aronczyk

Maria Isabel Espinoza

May 4th, 2021

Who benefits from data for good?

0 comments | 222 shares

Estimated reading time: 7 minutes

Melissa Aronczyk

Maria Isabel Espinoza

May 4th, 2021

Who benefits from data for good?

0 comments | 222 shares

Estimated reading time: 7 minutes

The central proposition of ‘data for good’ is that corporations should publicly share data sets derived from their business activities across various areas of the economy to improve and guide policymaking. Based on their study of contributors to the Big Data for Climate Action initiative, Maria Isabel Espinoza and Melissa Aronczyk, argue that whilst these initiatives may hold some public benefits, they also serve a political purpose to normalise and neutralise public concerns over mass data collection and subtly shift the focus on global challenges towards questions and solutions for which the answer is always more data collection.


In the last twenty years, vast increases in data volume, processing speed, and methods to collect and analyse human activities have resulted in datasets with previously unknown scales of precision and feedback on dynamic, real-time interactions between people and their environments. The proliferation of data’s potential to provide insights into people’s behaviour has sparked interest among private actors and policymakers. Big data is now presented as a strategic asset that could be used to solve the world’s most pressing social problems, from humanitarian crises to climate change. Conversations around “data for good” are taking place in a number of contexts, from United Nations climate summits to urban planning events, and from business conferences to hackathons. But our recent work analysing “big data for climate action” initiatives suggests that “data for good” is more about maintaining the political conditions that enable mass data collection than its contributions to environmental sustainability.

What are the origins of the “data for good” movement? What is at stake for its promoters? What are the consequences?

Private sector participation is undeniably essential in addressing climate crises and other urgent global problems. But before embracing the “data for good” movement, we must ask what is implied by this formulation. What are the origins of the “data for good” movement? What is at stake for its promoters? What are the consequences? As sociologists who study the impact of media and public relations on the framing of environmental issues, we decided to address these questions by interviewing “data for good” experts, attending “data for good” events, and reviewing media coverage, specialised journals, and documents published by intergovernmental and international organizations like the United Nations (UN) and the World Economic Forum (WEF).

The “data for good” movement builds upon the concept of “data philanthropy,” a data sharing practice by which businesses “donate” their data or insights generated from their data for the public good. The concept can be traced back to the 2009 WEF annual meeting in Davos where, in the aftermath of the global financial crisis, economic development experts and executives introduced the idea of big data as an untapped resource for development. In a series of reports published between 2011 and 2014, the WEF and UN Global Pulse — the UN’s first digital research “incubator” — advanced the idea of data philanthropy as an important step towards creating a “new personal data ecosystem” where big data creates both private and public value. The reports emphasized the importance of balancing the needs of government, private industry, and individuals to make use of big data as a source of inclusion and equality. Tellingly, they hit on the idea that the public’s fears of the “misuse” of their data should not overshadow the risk of the “missed uses” of data—that is, the risk to companies of not taking advantage of big data’s potential to create social value.

UN Global Pulse, a key supporter of the “data philanthropy” model, has promoted the UN’s Sustainable Development Goals (SDGs) as a proving ground for big data and AI applications. It has launched “data challenges” and other events to showcase the model’s potential. One example is the Big Data for Climate Action initiative (D4CA), a competition in which companies from the technology, retail, finance, and telecommunications sectors provide access to anonymized datasets to teams of scientists who propose ways to use the data for climate action (SDG #13). While some interesting proposals came out of the competition, the focus of our study was not to debate whether the projects showed promise, but to examine what was at stake for the corporate participants. Besides, the initiative’s limited nature (e.g., datasets were shared for only 4 months) prevented any long-term progress towards climate policy.

Bloomberg’s Data for Good Exchange 2018. | Maria I. Espinoza

We interviewed the promoters and “data donors” of the D4CA campaigns, asking what drew them to the event. Three explanations emerged. First, climate change was presented to them as a “neutral” problem, one that would help showcase the power of big data without seeming “political” (compared to data applications for humanitarian aid or post-disaster relief, for instance). Climate action was also promoted as a “safer” context for business by leaning on the idea that data mining for environmental benefit would be seen as less intrusive and therefore less risky from an investment standpoint.

Second, the D4CA initiative linked data philanthropy to an evidence-based logic that sees population data as the only means to justify policy interventions. It framed big data as the missing link in the policy-research chain, attempting to steamroll public anxieties over the growth of data collection — from the perception of corporations as data hoarders and unreliable players, to end users’ lack of control over their personal data.

A third justification for the D4CA event was that it not only provided the opportunity to partner with the UN brand, but also positioned the private sector’s expertise and tools as urgently needed for addressing sustainability goals. This is an increasingly popular “crisis-as-opportunity” or “shared value” management approach, where social problems are made into “productivity drivers” for firms. Becoming a data donor was promoted as a means for companies to showcase the power of their data product, maintain data collection practices, and reach new markets.

Becoming a data donor was promoted as a means for companies to showcase the power of their data product, maintain data collection practices, and reach new markets

The idea that private sector data and tools can be used for the public’s benefit is not new. Since at least the 1970s, corporations have “created numbers” to show off their sustainability initiatives (carbon markets are a recent example of this). However, studies have shown how corporate uses of data for environmental sustainability have mainly focused on introducing new standards, norms, and infrastructures instead of actively trying to adopt responsible practices. These efforts can be better understood as performative techniques that promote voluntary (i.e., independently developed, self-imposed, and non-binding) regimes of environmental management. In some contexts, these voluntary data-driven initiatives end up changing what counts as an environmental problem and which actors are best equipped to solve it.

As efforts to show the applicability of big data analytics to public problems continue to grow, we must keep in mind that behind the enthusiasm of scientists and policy-makers there is a tech sector suffering from an image problem. The “data for good” formulation was envisioned as an opportunity to maintain current data collection infrastructure and keep end-users engaged by highlighting the social value that data philanthropy can produce. Attempts to frame big data as necessary evidence for policy-making can shift attention away from well-founded anxieties over current data collection practices and towards justifications of more data collection. Data for good also runs the risk of reducing our commitment to strong and lasting climate action.  While big data that contributes towards climate action is welcome, achieving this goal ultimately depends on our political will. Climate action is not just about data or individual empowerment; it’s about collectively committing to an environmentally safe future.

 


This post draws on the authors’ article, Big data for climate action or climate action for big data?, published in Big Data & Society.

Note: This review gives the views of the authors, and not the position of the LSE Impact Blog, or of the London School of Economics.

Featured image Credit: Science in HD via Unsplash, In text image: Bloomberg’s Data for Good Exchange 2018. | Maria I. Espinoza.


 

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About the author

Melissa Aronczyk

Melissa Aronczyk is associate professor in the School of Communication and Information at Rutgers University. With Maria Espinoza, she is the author of A Strategic Nature: Public Relations and the Politics of American Environmentalism, forthcoming in November 2021 from Oxford University Press.

Maria Isabel Espinoza

Maria Isabel Espinoza is a Ph.D. candidate in the Sociology department at Rutgers University. Her research centers on risk communication, environmental politics, and the ethics of data. She's the co-author of A Strategic Nature: Public Relations and the Politics of American Environmentalism, forthcoming in November 2021 from Oxford University Press.

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