Big data for climate action or climate action for big data?

Introduction

In 2014, United Nations Global Pulse, a “data innovation hub and knowledge center” promoting public–private partnerships for development projects, launched a Data for Climate Action (D4CA) “challenge”: a competition whereby companies from the technology, retail, finance, and telecommunications sectors provide anonymized, aggregated datasets to teams of data scientists and researchers to come up with pragmatic solutions to address climate change. Inspired by France Telecom Orange’s 2012 Data for Development challenge, the ostensible objective of the D4CA campaign was twofold: first, to show the public sector and the research community how private sector data can be used to achieve Sustainable Development Goals (SDGs); second, to establish a model of secure data provision to encourage multiple companies to participate with minimal risk to their proprietary data elements.

The D4CA challenges (a second one was held in 2017) advance what Global Pulse calls “data philanthropy”—a data sharing practice by which businesses “donate” their data to serve the public good. Also called “data for good” or “data for development,” the practice has gained adherents in both the private and public sectors since the concept was introduced at the World Economic Forum (WEF) in Davos in 2011. Data challenges such as the D4CA campaigns have captured the imagination of the private sector and the general public, singing a new song with harmonies of global participation to drown out the growing public chorus about the harms of consumer data collection in terms of privacy and security, transparency and legality, and rights and equity. Adherents point to the immense potential of Big Data as an information resource to help personnel and citizens respond more quickly and efficiently to urgent social problems such as humanitarian aid distribution or epidemic control, aiming to elevate the promise over the perils of personal data collection. D4CA is now considered a key area of intervention for the broader data philanthropy community; and conversations around D4CA are taking shape in a number of contexts, from UN climate summits to urban and regional planning events, and from business conferences and hackathons (e.g., Bloomberg’s annual Data for Good Exchange) to corporate social responsibility programs (e.g., MasterCard’s Center for Inclusive Growth).

The data for good formulation can be seen as a response, or counteroffensive, to the emergent regulatory oversight of national governments and international organizations over the misuse of personal data (“digital data created by and about people,” WEF, 2011: 5) and the overreach by technology companies. This regulatory impetus reached its apex with the passage in 2018 of the General Data Protection Regulation (GDPR), a law passed by the European Union to maintain the privacy and security of individuals’ personal data, with sweeping impacts on organizations worldwide. Data philanthropy adherents attempt to offset the image of unethical or uncaring data-collecting organizations perpetuated by regulatory regimes such as the GDPR, generating arguments for multiple audiences that present data expropriation as not only safe and just but also essential for knowledge and action around global public problems.

When engaging with private stakeholders, data philanthropy advocates present the practice as a business opportunity, generating what the Harvard Business School professor Michael Porter calls “shared value” (Porter and Kramer, 2006, 2011), whereby social problems are made into “productivity drivers” for firms. In this framework, becoming a “data donor” is a means to maintain supplies and profits, reach new markets, and expand technical infrastructures (Porter and Kramer, 2011: 71–75).

When oriented toward a public audience, data philanthropy frames social problems as “lack of information” problems (e.g., Bulkeley, 2000), where Big Data can fill crucial gaps in knowledge, whether spatial, temporal, or demographic (e.g., Poom et al., 2020), and provoke more robust responses in terms of accuracy, timeliness, or adequate resources. In this frame, the private sector is positioned as a critically important social actor in resolving development problems; their valuable data can be shared with national statistics offices, development agencies, and research centers. Even more consequential, data philanthropy is heralded as a first step toward the creation of a “data commons,” a public space to house valuable social data that can be accessed by multiple actors for the good of all. Sister initiatives to D4CA, such as Artificial Intelligence for Sustainable Development Goals (AI4SDGs), embrace data philanthropy as a move toward a voluntary regime of environmental governance and accountability that can engender global health, equality, and well-being outside formal regulatory structures. ¹

This logic is made palatable to end-users (i.e., the individuals whose behavioral, locational, or other data has been collected into a privately owned dataset, with or without their knowledge) by appealing to the notion of mutual obligation. Individuals who opt out of a data commons are said to create both a “free rider problem” (e.g., benefiting from data-mined policy research without having to contribute their own data) and a “tragedy of the commons” since “the collective benefits derived from the data commons will rapidly degenerate if data subjects opt out to protect themselves” (Yakowitz, 2011: 4). By participating in D4CA and related challenges or initiatives, end-users can work collaboratively with private companies and other “stakeholders” to promote the use of data for good.

This article subjects these various premises to critique through a detailed examination of the activities of Global Pulse and its various collaborators in the promotion of D4CA initiatives. Drawing on in-depth interviews with data-for-good experts, policy advisors, and data researchers working in the realm of sustainability and/or climate change; participant observation at data-for-good events; and analysis of publicly available documentation on the uses of data philanthropy paradigms to respond to environmental and climate concerns, we argue that D4CA is better seen as a strategy to promote the ongoing collection and control of user data by private companies than as a means to achieve goals of global environmental sustainability.

Researchers attentive to the uses of corporate D4CA have noted the performative dimensions of corporate activities, whereby companies engage in “creating numbers” such as carbon markets to measure the sustainability efforts of the firm (Lippert, 2016; Vesty et al., 2015) or promote environmental information systems (EIS) that rely on private-sector data and infrastructure for decision-making around environmental issues. In the process, these performative techniques change what counts as an environmental problem and which actors are best equipped to solve it (Fortun, 2004a; Gabrys, 2016a; Mah, 2017). Indeed, since the 1980s, the “greening” of corporations by means of their adoption of voluntary (i.e. independently developed, self-imposed, and non-binding) regimes of environmental sustainability have not only involved new accounting, information, and audit regimes but have also given rise to new forms of authority (Levy, 1997; Levy and Newell, 2005; Power, 1997) that decenter government and other public sector information and experience in favor of business expertise. As Levy (1997: 127) explains, these forms of environmental management are often more about the political sustainability of corporations than about their contributions to environmental sustainability.

Building on these insights, we examine the logics by which D4CA is presented to private and public sector actors as a secure, trustworthy, and legitimate means of data collection and an opportunity to participate in responding to the climate crisis. We argue that while this campaign seeks to uphold the social value of Big Data by presenting it as a source of necessary knowledge to solve global public problems like climate change, its ultimate goal is to preserve the practice of corporate collection and targeting of user data and to maintain the value of this data as a private asset. As such, rather than legitimating the use of Big Data for climate change, we show that climate change is used to maintain the legitimacy of Big Data.

The article is organized as follows. First, we examine perspectives on the uses of data by private-sector actors for environmental and climate-related response, considering how claims to use private data for public good are frequently offset by their practical limitations. We then outline our research method and data collection process, and review the conceptual origins of data philanthropy and the principles by which it has been made meaningful in policy contexts. We next show how the D4CA campaign and the key players involved promoted it as a safe and secure way to generate value for all participants, demonstrating the relevance of this campaign for thinking more broadly about the problems posed by “data for good” paradigms in the realm of global governance. We conclude with a discussion of the impact of D4CA and related initiatives and the implications these present for responding to the enormous challenges of global climate change.

Civilizing data: Big Data and global development

On 31 March 2009, amidst mounting concerns in Europe about the rapid proliferation of techniques by commercial organizations to collect vast amounts of digital data about individual consumers and their online behaviors, a meeting was held in Brussels to discuss potential responses. In her keynote at the event, European Consumer Commissioner Meglena Kuneva attempted to balance consumer and regulator concerns with an acknowledgement of the economic opportunities presented by information and communication technologies:

It is precisely because we want these new opportunities to grow and evolve, that we need to promote the trust and confidence that will encourage people to participate. Internet is an advertisement supported service and the development of marketing based on profiling and personal data is what makes it go round. Personal data is the new oil of the internet and the new currency of the digital world. (Kuneva, 2009; emphasis added)

For proponents, data is indeed the gushing resource of the digital economy, with enormous value to be derived from its extractrion and refinement. A primary argument along these lines comes from economic organizations such as the WEF, which argues that personal data “will emerge as a new asset class touching all aspects of society” (2011: 5). At the core of this view is the strongly held perspective that a so-called multistakeholder approach, by which private companies participate in the problem-solving, is essential to develop innovative responses to ongoing global problems, such as the climate crisis (Hajer et al., 2015; WEF, 2014).

For critics, “data is the new oil” has a rather different meaning: the activities of corporate owners to capture and derive value from personal data is nothing less than a new phase of colonialism. Couldry and Mejias (2019) argue that data companies redefine social relations to normalize the act of digital dispossession, echoing historical appropriations of resources, territory, and personhood. They identify four discursive logics by which companies obscure their practices of personal data extraction and control. First, personal data is promoted as a vast and largely untapped natural resource whose value lies exclusively in its extraction and refinement. As such, data “are ‘merely’ the ‘exhaust’ exuded by people’s lives, and so not capable of being owned by anyone” (Couldry and Mejias, 2019: 340). Second, companies’ use of consumer data is not about deprivation of ownership but “just sharing,” a benign form of reciprocity that conduces to the benefit of all. ² Third, corporations are uniquely positioned to wield the skills and knowledge required to collect, process, and analyze such vast and complex quantities of digital data. Finally, companies espouse a rationality that “operates to position society as the natural beneficiary of corporations’ extractive efforts, just as humanity was supposed to benefit from historical colonialism as a ‘civilizational’ project” (Couldry and Mejias, 2019: 340).

Isin and Ruppert (2019) offer a portrait of data colonialism by attending to the complex issues arising from digital development, or information and communication technologies for development (ICT4D). Though arguments in favor of ICT4D present data extraction as necessary complements to existing data sources such as national statistics and demographics, ICT4D often reinforces hierarchical perceptions of global regions, portraying countries that are “information poor” as beneficiaries of knowledge and insights from “information rich” sites. Moreover, though longstanding manifestations of imperial data politics such as the census or the metric system produced power arrangements between colonizers and colonized (Anderson, 1991; Scott, 1998) contemporary ICT4D produce data that not only identify attributes of a population but subject them to monitoring on a constant basis. This emergent “data empire” allows the Global North to set the terms of data collection and interpretation in the Global South, with dramatic implications for decision-making around development issues (Isin and Ruppert, 2019).

Nevertheless, Big Data enthusiasts persist in seeing datasets as a diverse, integrated, and timely source of information, one that could fill considerable gaps in global knowledge and action. Corporate uses of Big Data for environmental or climate action have mainly focused on introducing new standards, norms, and infrastructures of environmental responsibility. Michael Power (1997) examines the emergence of environmental accounting techniques in the UK in the mid-1990s, by which the jurisdiction of environmental concerns is enlarged to accommodate professional accounting language, practices, and expertise. By focusing on the claims to legitimacy as opposed to investigating the actual legitimacy of environmental auditing, Power allows us to apprehend the performative dimensions of the various representational strategies in which environmental auditors engage as well as the making of private-sector expertise in dealing with matters of environmental concern. Lippert’s work on carbon accounting recognizes the co-constitution of such markets by a range of actors (see also Lohmann, 2005). Not only corporate firms but also organizations such as the Organization for Economic Cooperation and Development (OECD) and the World Business Council for Sustainable Development (WBCSD) are involved in the legitimation of global carbon reports and metrics (Lippert, 2016; Williams et al., 2019).

While some critiques of corporate uses of Big Data for environmental issues and climate action focus on regimes of calculation and agency in the process of commodification and market exchange (per Callon, 1998; Latour, 1999), others consider the symbolic implications of the management strategies themselves in their ability to dedifferentiate the concepts of environment and data—such as the use of the metaphor of the “cloud” in computing services (Pasek, 2019). ³ Taken as a whole, these corporate initiatives can be seen as attempts to shift the needle, not on actual environmental problems at hand, but on the way problems are defined, managed, and evaluated.

This problematic—redefining the problem instead of making inroads to solve it—is especially complex in the realm of climate change. Climate change has been defined as a “super-wicked” problem for its unprecedented spatial and temporal challenges, its obstacles to cognitive and social judgments, and its low “incentive” structure for those paradoxically best placed to address it in policy settings (Lazarus, 2009). It is partly for this reason that private-sector data processing has gained a foothold as a potential contributor to climate problems: as a new kind of Environmental Information System that can fill gaps in global climate data sources by providing more diverse, integrated, and timely datasets. As Faghmous and Kumar (2014) note, current climate data sources present significant challenges for researchers. The lack of long-term data; problems of heterogeneity (i.e., having to deal with a wide array of data sources that are complementary but also possibly redundant); constantly changing observation systems; limited understandings of how data was collected and with what purpose; and limited data representation models that acknowledge the climate system as a multivariate and ever evolving spatiotemporal network are some of the challenges. While Big Data analytics could help complement current observational, remotely sensed, and model output sources of climate data, just as with any data-driven exploration, it raises questions over sampling bias, autocorrelation, and causal inferences in predictive models. For Faghmous and Kumar, the greater risk with Big Data analytics is to present it as the “silver bullet” of modern research, where findings can be interpreted using a “theory-free” mindset (2014: 161). While these methods will produce results, they will yield few insights without theory.

Promoting private sector data and its analytics as essential information resources for climate concerns is rooted in the promise of this data to both define and govern environmental problems as well as to support evidence-based decision-making. EIS are essential for the definition and governance of environmental problems (Aronczyk, 2018; Fortun, 2004a; Gabrys, 2016b). ⁴ Based on the principle that all citizens, corporations, and state agents require equal access to information to judge environmental problems (Fortun, 2004a; Lippert, 2016: 2), the use of EIS for decision-making has since the 1980s been the norm among environmental justice activists, government agencies, nonprofits, and corporations. EIS have been adopted to address water quality, pollution, deforestation, environmental justice, and climate change. Scholars have referred to the proliferation of EIS to support evidence-based environmental governance as the “informating” of environmentalism (Fortun, 2004a). Through various means, and via varying formats, EIS help to control what a system of environmental topics, data, and expertise consists of; and how this information is communicated to different audiences. EIS, Fortun writes, “structure what people see in the environment, and how they collaborate to deal with environmental problems … they are technologies designed to produce new truths, new social relationships, new forms of political decision-making, and ultimately, a renewed environment” (Fortun, 2004b: 54).

Recent studies on the cross-pollination of Big Data and environmental governance have shown how environmental activists are increasingly adopting EIS that rely on Big Data. Environmental activists have primarily engaged with EIS that depend on voluntary data collected through participatory citizen sensing or crowdsourcing (Gabrys, 2016a, 2017; Mah, 2017) and secondarily with data mining projects that collect social media posts about pollution and health (Mah, 2017). Despite the participatory and voluntary nature of some of these initiatives, however, many of them still rely on corporate information and technology infrastructures (e.g. the Google Maps platform) for data collection and analysis (Mah, 2017). Ideas about how to partner with the private sector to secure access to big datasets that would otherwise only be used for profit (or remain unattended) have thus taken center stage in the conversation about how to harness the “data revolution” to advance the agenda of the 2030 SDGs.

Research process and data collection

In order to assess the relevance of Global Pulse and the D4CA challenges in popularizing the concepts of “data philanthropy” and “data for climate action,” we first conducted a thorough review of public documentation pertaining specifically to those terms, including news articles, technology magazines, and documents published by intergovernmental and international organizations like the UN and WEF (see online Appendix 1). We also reviewed reports and white papers authored by collaborators of the Global Pulse innovation lab, such as participants in the UN World Data Forum and members of the UN Secretary-General’s Data Revolution Group.

After this initial stage, we contacted a list of actors who appeared prominently in the documentation. We prepared a semi-structured interview guide designed to elicit perspectives on the emergence and development of the aforementioned concepts, especially on the “data for climate action” approach. Questions covered individual professional trajectories and engagements with the field of data for good before and after participating in the D4CA challenges and other data-for-good events as advisors, evaluators, or organizers. Interviewees were also asked to reflect on what constitutes the emerging field of data for good, the practice of data philanthropy, and initiatives like D4CA; and their perceived implications for data sharing, corporate culture, and climate governance.

After an initial round of interviews with a small pool, we adhered to a limited snowball sampling method in which interviewees were asked to recommend other data-for-good experts. We repeated our method of research, approach, and interview with this secondary pool. Thirty-eight experts were contacted; 19 were interviewed. Given the high profile of the interviewees (e.g., senior executives, founders, and CEOs of tech companies), we consider the total interview sample to be significant (Table 1).

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Table 1.

List of respondents.

Interviewee	Date	Professional affiliation	Professional title	Relation to the data for good movement
Respondent 1	30 April 2018	Center for International Earth Science Information Network (CIESIN)	Director of Customer Success & Advocacy	D4CA Strategic Advisor (2017), UN Secretary-General Expert Advisory Group on the Data Revolution (2014)
Respondent 2	6 May 2018	World Economic Forum (WEF)	Data Driver Development	D4CA Evaluation Committee (2017)
Respondent 3	10 May 2018	Former UN and Skoll Global Threats Fund	Program Officer	D4CA Evaluation Committee (2017)
Respondent 4	6 June 2018	Former UN Global Pulse	Data Innovation Specialist	Big data and SDGs
Respondent 5	8 June 2018	Big Data Research, LIRNEasia	Team Leader	D4CA Evaluation Committee (2017)
Respondent 6	12 June 2018	Former UN Secretary General's Climate Change Support Team	Climate Policy Advisor	D4CA Technical Committee (2014)
Respondent 7	19 June 2018	UN Global Pulse	Director	D4CA Strategic Advisor (2014 & 2017), UN Secretary-General Expert Advisory Group on the Data Revolution (2014)
Respondent 8	19 June 2018	UN Global Pulse	Research consultant	D4CA Organizer (2017)
Respondent 9	26 June 2018	Pulse Lab Jakarta	Chief Technical Advisor	Big data and SDGs
Respondent 10	29 June 2018	CEPEI Colombia	Data Coordinator	Big data and SDGs
Respondent 11	3 July 2018	The Centre for Internet and Society (CIS)	Research Director at CIS India	Big data and SDGs
Respondent 12	16 July 2018	UN Population Fund (UNFPA)	Humanitarian Data and Resilience	D4CA Technical Committee (2014)
Respondent 13	19 July 2018	Dalberg Data Insights	Project Manager	Big data and SDGs
Respondent 14	31 July 2018	Dalberg Data Insights	Data Scientist	Big data and SDGs
Respondent 15	17 August 2018	Humanitarian OpenStreetMap (HOT)	Director of Community and Partnerships	Big data and SDGs
Respondent 16	24 September 2018	Crimson Hexagon	Director of Customer Success & Advocacy	D4CA Data Donor (2017)
Respondent 17	27 September 2018	FSG	Co-Founder and Managing Director	Shared-value expert
Respondent 18	15 October 2018	DTN	Chief Meteorological Officer	D4CA Data Donor (2017)
Respondent 19	17 October 2018	Earth Networks	Chief Marketing Officer	D4CA Data Donor (2017)

D4CA: Data for Climate Action; SDG: Sustainable Development Goal.

All interviewees work (or have worked) in data companies, think tanks, foundations, intergovernmental organizations, and international organizations, where they occupy roles promoting private–public cooperation to advance the achievement of the SDGs or other climate change mitigation through Big Data. Some were data scientists, app developers, and public relations consultants; others had a background in development, climate science, or policymaking. Interviewees landed in the field of data philanthropy from a number of paths. Some had worked or served as an advisor for the UN Secretary-General’s Expert Advisory Group on the Data Revolution. A number had careers in development and climate science and had worked in different UN agencies using Big Data to address risk reduction, disaster management, and humanitarian response. Some had backgrounds in tech companies working as developers or communications managers. At the time we conducted the interviews, a year after the second D4CA challenge, most of the respondents occupied senior-level positions in their organizations. Their ages varied from 30 to 50 years old.

We also participated in three data-for-good events: Bloomberg’s 2017 Data for Good Exchange (#D4GX); WEF’s teleconference on Big Data for health, “Epidemic Readiness and Trustworthy Data”; and the 2019 CIBC Analytics Day, an event by the Canadian Imperial Bank of Commerce focused on the theme of data for good. These participant-observation activities helped us supplement the interview data with in situ considerations of the organizational discourses, practices, and tensions among D4CA advocates.

“A world that counts”: Promoting data as a global good

“If you can’t measure it, you can’t manage it.” This is how Michael Bloomberg announced, via Twitter, the fifth Bloomberg Data for Good Exchange (#D4GX): an annual event that brings together corporations, policymakers, nonprofits, charitable foundations, and researchers to explore how Big Data can solve the most pressing social problems of our time. The 2018 conference theme was, “Our Data for Good?” (emphasis in original), reflecting on ways the private sector could deploy its data assets to develop data science projects that focus “on everyone having a stake, making it solid, fair, and equitable” (Bloomberg Finance LP, 2018).

Conference presenters spoke of the power of Big Data to tackle an array of social issues, from gender equity to climate resilience. Disaster recovery specialists explained how mobile finance and credit-card transaction data can help city leaders prevent price gouging after hurricanes and other extreme weather events, suggesting that mobile data could allow hurricane victims to find gas and groceries or assess who is creditworthy in a post-disaster setting. Catchphrases such as, “When you have data that informs, you have data that transforms” or “The power of data is to drive good decisions based on fact and not politics” were frequently invoked to emphasize how private Big Data can catalyze social change.

Many of the #D4GX presentations described their initiatives in terms of “data philanthropy,” an emerging practice whereby corporations donate data or insights generated from their data to the public (or to a public-serving analyst such as a nonprofit institution) to yield new insights that could improve public policies or social programs and services (McKeever et al., 2018). In addition to providing “evidence-based, data-driven” insights, data philanthropy intends to align business and philanthropic activities in a “shared value” strategy whereby companies link corporate social responsibility with competitive advantage to create social and economic value (Aakhus and Bzdak, 2012; Porter and Kramer, 2006, 2011).

The origins of data philanthropy can be traced to the 2009 WEF annual meeting in Davos where, in the aftermath of the global financial crisis, executives, government officials, and development experts introduced the idea of Big Data as an untapped resource for human well-being. In a series of reports following the Davos meeting, the WEF and UN Global Pulse introduced the principles of its project to build a new “ecosystem” of personal data management. ⁵ The report advanced three primary and interrelated concerns to which this new ecosystem would respond: (a) creating value, (b) managing risk, and (c) strengthening trust. We describe how these concerns were elaborated below:

Creating value: A central objective of the WEF and its partners was to promote personal data as a valuable economic resource in a post-industrial (and post-financial crisis) environment. Echoing the “data is oil” metaphor, WEF and UN Global Pulse reports highlighted the potential for innovation, real-time connectivity, and “unprecedented” global reach of Big Data insights. Key to the achievement of this value was a multistakeholder approach, in which all parties to the transaction (as well as all of the datasets each party could contribute) could be mobilized in the service of collective gains. A personal data “ecosystem” was therefore imagined as a way to “balance” the needs of government, private industry and individuals in order to create value (WEF, 2011). In the case of data for sustainable development, a data ecosystem that brought together the “disparate worlds of public, private and civil society data” to “develop a global consensus on principles and standards” (UN Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development, 2014: 2) was especially important to promote Big Data as a source of inclusion and equality.

These trust networks were proposed as the necessary infrastructure to deliver meaningful transparency, ensure accountability, and empower individuals. For the WEF’s global dialogue on personal data, “meaningful” transparency and accountability are a matter of balance between demystifying ambiguity surrounding privacy and overwhelming the public with too much information about the complex nature of data flows. Full transparency “overwhelms and creates opacity” and “threatens the economics of secondary [data] usage,” whereas “effective” transparency is “contextual and relevant only on specific data usages” (WEF, 2014: 7; see Wood and Aronczyk, 2020). The report distinguished a “user-centered” approach from a “user-centric” one: while the former focuses on providing just the right amount of data to “empower individuals in meaningful transactions and experiences that are consistent with their expectations” (WEF, 2014: 8), the latter overburdened individuals with the need to make decisions regarding data management despite their “limited capacities and tools for making appropriate decisions to preserve their interests…undermining the larger goal” of a balanced data ecosystem (2014: 7–8). In the face of the inevitable growth of passive data collection, the proposed trust networks and emerging legal frameworks will help “recognize context and do so in a way that simplifies rather than adds to the complexity of the environment” (2014: 9).

These trust networks, with WEF and UN Global Pulse at their core, would find additional partners to help them frame ongoing data collection as a public good, while maintaining the promise of benefits to all parties and generating economic value for private sector participants:

Aligning the different interests to create a true “win-win-win” state for all stakeholders represents a challenge – but it can be done. The solution lies in developing policies, incentives and rewards that motivate all stakeholders – private firms, policy makers, end users – to participate in the creation, protection, sharing and value generation from personal data. (WEF, 2011:19)

UN Global Pulse and the D4CA campaign

The United Nations foresaw the rise of Big Data analytics as an opportunity to support the achievement of its SDGs. In 2013, UN Secretary-General Ban Ki-Moon authorized the formation of an Independent Expert Advisory Group on a Data Revolution for Sustainable Development. In November 2014, the Advisory Group released its first report, “A World that Counts: Mobilizing the Data Revolution for Sustainable Development.” The report makes three cases for Big Data as a crucial support to the achievement of the SDGs. First, it positions Big Data as an appropriate technological intervention that can “paint a richer picture of human development” (Giovannini and Jespersen, 2014), one where, for instance, a measurement like the Human Development Index could be expanded to include alternative development dimensions like “voice, equality, sustainability, freedom and dignity” (Jahan, 2014). Second, Big Data is presented as a complement to national statistical systems, increasing the diversity and accessibility of relevant data that can lead to better dialogues and decision-making. Third, the report proposes that Big Data could “move the world onto a path of information equality,” where every government, organization and citizen can access—and be accountable to—the knowledge it generates (UN Secretary-General’s Independent Expert Advisory Group on a Data Revolution for Sustainable Development, 2014: 27).

In order to unlock the capacity of Big Data and data analytics to provide insights into sustainable development problems, gaining the participation of the private sector was key. This is the role of Global Pulse, an “innovation lab” created in 2009 to “bring[] together expertise from inside and outside the UN to harness today’s new world of digital data and real-time analytics for global development” (UN Global Pulse, 2012: 2). A vocal proponent of the “data for good” model (Tatevossian, 2011) for creating social value and gaining public trust in the data economy, for the last 10 years, Global Pulse has sought out private sector data partnerships with companies such as social media businesses and mobile telecommunications operators. As a director at Global Pulse describes it:

Global Pulse is the result of the only request the G20 ever made to the UN – it’s not a well-known fact but it’s interesting…Most of what we do is not really about measuring progress. This is not about generating statistical indicators. It’s about smarter implementation of programs and more effective management of risk. This is really about looking at how we can use digital evidence of human behavior to make reliable inferences about what’s happening offline at the household level. (Respondent 7)

Conclusion

In 2018, the UN Economic and Social Council (ECOSOC) Forum, which brings together over 300 representatives of member states and a wide range of non-state actors, considered the issue of private–public collaboration in the age of Big Data. ECOSOC concluded that Big Data is a valuable “business asset that the private sector can donate to governments for more informed public policy-making” (UN ECOSOC, 2018, emphasis ours). In 2019, the High-Level Panel on Digital Cooperation—established by UN Secretary-General António Guterres and chaired by philanthropist Melinda Gates and chairman of the Alibaba Group, Jack Ma—further explored the issue of private-sector data sharing and proposed more concrete alternatives for private–public cooperation in the data economy (UN News, 2019). Chapter 4 of the High-Level Panel on Digital Cooperation report, titled “The Age of Digital Interdependence,” outlines three potential mechanisms for digital cooperation: one that builds on the multi-stakeholder Internet Governance Forum; one that proposes a distributed architecture building on existing UN mechanisms; and a third that advocates for a “data commons” approach with little coordination by the UN (UN Secretary-General’s High-level Panel on Digital Cooperation, 2019).

This latter mechanism is at work in the report, Sharing is Caring: Four Key Requirements for Private Data Sharing and Use for Public Good, published by the Data-Pop Alliance in 2019. The Data-Pop Alliance was founded by a former UN Global Pulse member, Emmanuel Letouzé, who became ambivalent about the UN Global Pulse efforts. ⁸ As he explained in an interview in 2015, “…I didn’t think the ‘techno-scientific’ approach and the ‘data-for-good’ narrative they embodied would make much of a difference. I thought it overlooked many aspects of the problems it faces…” (Rajpurohit, 2015).

The Data-Pop Alliance lays claim to a more cautious stance, attempting to parse the difference between data sharing and ethical considerations. In situations of global health or humanitarian crisis, the goal is to save lives; and by this metric, the idea that data must be shared is a powerful exhortation. But for some, the question is not how more information, delivered more quickly to more places, can be mobilized in an “all hands on deck,” consensus-oriented approach; it is to consider which human rights are most fundamental to our humanity. There is, as Letouzé argues, a considerable tension between different rights—the right to privacy, for instance—and especially between what we “can” do and what we “should” do to resolve global public problems. Climate change is by its nature a crisis of global proportions. It demands widespread participation to recognize it as a problem and to devise collective responses to apprehend it:

Our [Data-Pop Alliance’s] stance is that in modern pluralistic data-infused societies, the most fundamental human right is political participation, specifically the right and ability of citizens and data producers to weigh in on debates about what constitutes a harm, notably through greater legal and effective control over the rights and use of their data. This perspective highlights the fundamental political nature and requirements of the (Big) Data Revolution—one that is about people’s empowerment, not just about the ability of politicians and corporations to get and use or misuse more individual data. (Letouzé and Vinck, 2015)

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