In 2018, the Centre for International Governance Innovation (CIGI) released an essay series titled Data Governance in the Digital Age.1 It was a far-reaching compendium covering topics such as the rationale of a data strategy, how to balance privacy and commercial values, and international policy considerations. And it anticipated many of the issues that have emerged, such as surveillance capitalism. One area that was not covered in depth, however, was data valuation.
Against that background, in November 2023, CIGI co-hosted an international conference, in conjunction with the International Association for Research in Income and Wealth, to advance discussion on the valuation of data as an asset.2 Despite some meaningful progress by national account statisticians to value data as an asset, this value is still not included in national balance sheets, nor is it included in corporate balance sheets. No ideal or agreed-upon methodology has yet emerged to measure data’s value, largely because its value depends on its usefulness in a particular context (Coyle and Manley 2022; Mitchell, Ker and Lesher 2021). That context is framed by governance — the rules and regulations that determine how data, especially personal data, can or should be used — and includes standards (such as those set by accounting and other regulatory bodies), intellectual property rights, trade treaties, competition, privacy and other frameworks that will vary across jurisdictions and even within them. Achieving a coherent framework that encompasses these areas — and others — is a substantial challenge for any country and obtaining coherence globally is even more difficult.
No ideal or agreed-upon methodology has yet emerged to measure data’s value, largely because its value depends on its usefulness in a particular context.
CIGI therefore commissioned some global thought leaders to share their ideas on how to advance data governance to unleash the value of data. This essay series explores four themes: the current state of global data governance; different perspectives on notions of value; governance frameworks to unleash the value of data; and mechanisms for governance cooperation.
The Current State of Global Data Governance
Establishing country-level data governance frameworks is an ongoing and complex undertaking. Countries have very different capacities to develop these frameworks, implement them and then enforce them. They face a variety of challenges, including the interconnected nature of this governance; the digital divide in creating and enforcing frameworks; an ever-changing legislative landscape that includes laws, regulations and standards at both the national and international levels; and constantly evolving data-intensive technologies. Yet these frameworks are essential for the trustworthy sharing of data that can drive economic activity.
Drawing upon findings from the Global Data Barometer, Silvana Fumega examines the progress that has been made by countries globally in establishing legal frameworks and data policies as well as documenting the many challenges that exist while offering some potential solutions. Susan Ariel Aaronson looks specifically at the interplay of data and artificial intelligence (AI) governance in country policies, the apparent disconnect between data governance and AI governance, and the risks that this gap presents nationally and internationally. Against this background, Lorrayne Porciuncula discusses ways to put data governance at the forefront of policy discussions and redefine value creation in ways that prioritize societal well-being and sustainability over the short-term profits of firms.
Different Perspectives on Notions of Value
The focus of data governance has typically been on the individual and their personal data, but data may also be inferred from groups of individuals and their activities. Meanwhile, individuals and groups may have different perspectives on how their data should be governed and the value attached to it. For example, aggregated data may bring value to society that may not be part of an individual’s perspective. It can also raise a variety of risks. This situation is evolving over time as new technologies emerge and harness different types of data. How to represent the diverse perspectives and interests and the weights to place upon them is an ongoing challenge. To address that challenge, various data stewardship models have been developed, each with strengths and weaknesses. Nevertheless, these models can allow individuals and groups to control how their data will be used, how to derive value from it and how to share that value.
Teresa Scassa discusses the evolution of individual and collective privacy and emerging data rights that give individuals — and perhaps communities — more control over both the personal and non-personal data that they generate. Sean Martin McDonald points out that the value of data reflects whose interests are being represented and the integrity of the supply chain by which the data is produced. He discusses how fiduciary models provide one mechanism for rights holders to participate in the governance of their data and to protect the supply chain that creates the data. Kean Birch argues that such governance models, including not only data trusts but also data commons and national statistical agencies, could be used as models to create a data wealth fund along the lines of those based on commodities, although doing so in practice is not straightforward.
Governance Frameworks to Unleash the Value of Data
Data forms a value chain: data, and especially big data, can be used with AI technologies to create powerful analytics that can vastly improve policy and business decision making. Digital technology firms that are first movers in big data have tremendous advantages through economies of scale and scope, network effects and information asymmetries; these characteristics have not only driven up their value, but also give these firms tremendous market power, sometimes in multiple markets. This situation has led to different proposals on how to address this power since it can stifle innovation, be privacy invasive and impact how value is created and distributed.
Keldon Bester discusses the renewed focus on competition policy and how essential its role is in unleashing the value of data, and how it can capture non-monetary issues such as privacy. The monopolization of key elements of infrastructure in the “tech stack” by big firms has also led to an exploration of the role that digital public infrastructure (DPI) might play. Soujanya Sridharan, Vinay Narayan and Jack Hardinges describe how DPI can be viewed as an alternative to private monopolies and explain that how and why DPI will be used — its orientation — is critical to ensure proper governance. Market-based mechanisms are an obvious way to unleash the value of data. Alex He and Rebecca Arcesati document the experiences of China with local data exchanges that allow the public trading of various types of data, including personal data, and note that while trading exchanges are usually seen as a primary means of value discovery, even such exchanges require solid data governance to run effectively.
Mechanisms for Governance Cooperation
Multi-stakeholder input is an oft-mentioned approach to create inclusive data governance, although how to achieve it in practice is not straightforward, especially since the term multi-stakeholder can take on a variety of different meanings whereas stakeholders likely have different objectives and capacities to participate. CIGI’s Global Platform Governance Network (GPGN) was created to bring different perspectives to platform governance issues in a multi-stakeholder environment, recognizing that this governance needs to be multidisciplinary, representative and transnational. More formally, given that data flows globally, trade agreements are already being used in various ways to deal with elements of data governance, but these agreements are not comprehensive, and may reflect power imbalances among signatories as well as impact those who are not party to the agreement(s).
Jeni Tennison makes the case for the necessity to have multi-stakeholder representation in data governance and discusses how the inclusion of civil society is not only democratic, but also creates a shared understanding that breaks down barriers, generates trust, boosts literacy and encourages adoption of digital technologies. Chris Beall documents CIGI’s experience with the GPGN and offers concrete recommendations on how this type of network can be applied to other areas, including data governance. Finally, Patrick Leblond explores the growing “digital noodle bowl” of regulations in trade agreements related to cross-border data flows and suggests some ways forward to create effective global governance.
In summary, these essays reveal the complicated governance background that lies behind more technical discussions on how to measure the value of data. Countries are making substantive progress on data governance frameworks, but there are still gaps and silos at the national and international levels that need to be addressed so that decisions made on data governance are representative and inclusive and reflect the values of various stakeholders. In doing so, data governance can create a trusted environment to share data that, in turn, can create value for the individual and for society.
