Data Governance, Part 2: Models & Implementation

Katy McKinney-Bock | February 20, 2024

A full version of this report is available:

A Better Deal for Data is focusing on empowering people to participate in shared governance of their data, and is about making organizational commitments to data subjects, to data communities, and for the public good. Part of this is building an understanding about emerging data governance models that seek to increase people’s trust in data. This research brief is an abstract of a full report on data governance models, part 2.

In this December research report for a Better Deal for Data, I set out two goals: first, to research and provide a description of recognized models of data governance that could, in principle, become adopted and put into practice. The second goal was to begin to evaluate the implementation cost for each model, addressing the question: what would it take to implement this model?

For the first goal, I collate a few existing typologies of alternative data governance models from a set of core references, including data trusts, data collaboratives, and data collectives. For each model, I provide a description and a set of examples from case studies in these references which illustrate the model in practice, and address any broader research on this model. In the second section of this report, I begin to ask questions about how challenging it is to implement these governance models. I include discussion of a recently published evaluation framework that was developed to improve trust in data institutions through better governance practices (The Global Partnership on Artificial Intelligence, 2023), and I ask questions around what it means to be “lightweight”, as possible first steps toward an analytical framework or approach for evaluating the ease of implementation, though with more questions than answers.

The “status quo” data governance model presents a number of challenges that are addressed by work on alternative data governance models. In short, this model uses contracts like terms of service that have been described as ‘take it or leave it’ or ‘one-way’ approaches to governance of data, which create several problems, such as extractive practices, undermined trust in data, power imbalances, a fragmented/siloed data governance ecology, and an assumption that one size fits all.

Alternative models of governance have arisen that address these challenges. In this report, I examine ten such models from a core set of references (Ada Lovelace Institute, 2021; Data Trusts, 2019; Development Gateway, 2023; Lopez Solano et al., 2022; Mozilla Insights et al., 2020; Soni, 2021):

• • Data collaborative
  • Data marketplace
  • Data trust
  • Data cooperative
  • Data commons
  • Data exchange
  • Personal data store
  • Indigenous Data Governance / Sovereignty
  • Account aggregator
  • Data repository

There is variance in the goals and concerns that emerging models address at different levels of governance, and the models vary in whether they are more theoretical or practical in nature. When comparing data collaboratives to data trusts, the focus is on establishing who the decisionmakers/negotiators are for data sharing and use, and where the accountability lies. When focusing on personal data stores and marketplaces, there is an assumption that most of the decision-making aspects of governance are retained by the individual user, and then the focus is on technical solutions for creating ways to interact and share out that data (Solid, blockchain, etc.) as well as on security of the information. When you look at data sovereignty, it is about who decides who decides – empowering indigenous or other communities to create governance frameworks that work for their data and context. It seems that many of these models overlap in scope or could be hybridized – i.e. an organization could create a data trust that enables indigenous data sovereignty, with a data repository that lets individual users set certain permissions on the use of their data even while the trustee negotiates the general use of the repository – all addressing different aspects of the puzzle of data governance.

The complexity of describing the typology of emergent governance models in the first goal makes the second goal, What would it take to implement these models?, even more challenging. I include discussion of an evaluation framework that was developed to encourage trust in data institutions through better governance practices, written recently by the GPAI (The Global Partnership on Artificial Intelligence, 2023), which measures whether or not policies are in place, IP rights are recognized and managed, data rights and data justice are part of culture and policy, with monitoring, as well as progress on development of a “data commons” – in this case, measured (in a much narrower sense) as organization maturity around data management principles.

Then, I sketch out a criteria for what we mean when we say “lightweight”, and consider what next steps we might take to continue building onto this to be able to measure success of the Better Deal for Data model commitments:

• • Low cost
  • Low barrier to implementation using clear, established methods
  • Structured for immediate implementation (weeks, not years), with an ongoing commitment to longer term development.
  • No need to set up an entirely new institution – can be done with existing parties.
  • Expresses clearly an adherence to processes that are long-term and responsible

Moreover, there are some questions of practical value that are relevant to measure how challenging implementation may be:

• • Has this model been put into practice before (or is it entirely theoretical)?
  • Is there existing technology that can be used to implement the model, or does new technology need to be developed (for example, the Solid specification)?
  • Is there an existing legal framework that is easily copied (like data trusts under trust law)? Does it have external dependencies – i.e., does the model only exist when there is an ecosystem of governance models to interoperate with (‘bottom-up’ data trust ecosystem, a la Delacroix and Lawrence 2018)?

I suspect that many of these decisions and questions need to be addressed upfront, with careful engagement between data stewards, processors, and users, with the communities that originate the data. This, in a sense, may be independent of the selection of any given governance model. I suspect that frameworks such as Verhulst et al.’s (2023) mutual commitment framework are trying to address these upfront decisions that would enable the next kind of discussion of how governance would work. Getting discussion of governance off-the-ground may require a model that is a bit more fundamental than the governance models discussed in this paper.

This work is licensed under CC BY 4.0. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

This work is supported by a subaward from OpenTEAM as an initiative of Wolfe’s Neck Center for Agriculture and the Environment, specifically funded by the U.S. Department of Agriculture under agreement number NR233A750004G032. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author and do not necessarily reflect the views of any funder. In addition, any reference to specific brands or types of products or services does not constitute or imply an endorsement.

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References

Ada Lovelace Institute. (2021). Exploring legal mechanisms for data stewardship. https://www.adalovelaceinstitute.org/report/legal-mechanisms-data-stewardship/

Data trusts: Lessons from three pilots (report). (2019, April 14). The ODI. https://theodi.org/news-andevents/blog/odi-data-trusts-report/

Development Gateway. (2023). Farmer-Centric Data Governance: Towards a New Paradigm. Development Gateway: An IREX Venture. https://developmentgateway.org/blog/farmer-centric-data-governance-modelsprotecting-farmers-and-food-systems-today-and-tomorrow/

Lopez Solano, J., de Souza, S., Martin, A., & Taylor, Linnet. (2022). Governing data and artificial intelligence for all: Models for sustainable and just data governance. European Parliament. https://doi.org/10.2861/915401

Mozilla Insights, Jonathan van Geuns, & Ana Brandusescu. (2020). Shifting Power Through Data Governance. Mozilla Foundation. https://foundation.mozilla.org/en/data-futures-lab/data-for-empowerment/shifting-powerthrough-data-governance/

Soni, S. (2021, September 28). Building the Stewardship Navigator: Our Approach and Methodology. The Data Economy Lab. https://thedataeconomylab.com/2021/09/28/building-the-stewardship-navigator-ourapproach-methodology/

The Global Partnership on Artificial Intelligence. (2023). Trustworthy Data Institutional Framework: A practical tool to improve trustworthiness in data ecosystems. Global Partnership on AI. Verhulst, S., Schroeder, A., & Hoffman, W. (2023). The Time is Now: Establishing a Mutual Commitment Framework (MCF) to Accelerate Data Collaboratives. United Nations University Centre for Policy Research Working 4 Papers. https://unu.edu/publication/time-now-establishing-mutual-commitment-framework-mcf-acceleratedata-collaboratives