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Open Data Institute explores diverse range of data governance structures

Unlocking the economic and social potential of data will require a diverse range of governance structures to meet the needs of different contexts, use cases and stakeholders

The Open Data Institute (ODI) is continuing to explore how best to increase access to datasets to a wider variety of stakeholders, given that a one-size-fits-all approach to governance rarely works.

Founded in 2012 by Oxford academic Nigel Shadbolt, and World Wide Web inventor Tim Berners-Lee, the ODI aims to help enterprises and governments build an open, trustworthy data ecosystem so that information can be used to produce better outcomes for society.

The aim of data institutions, which the ODI began work on in late 2018, is to create a system of data pooling between organisations – from government departments to private and third sector entities – that will allow them to share data in safer, fairer and more ethical ways.

The ODI initially focused on exploring the potential of the data trusts data access model, whereby trustees take on a fiduciary duty on behalf of others for how their data is shared, but its remit has since expanded to account for other forms of “data stewardship.”

“We’re still interested in that particular approach … but we see them as sitting alongside a much more diverse family of approaches,” Jack Hardinges, programme lead for Data institutions at the ODI, told Computer Weekly.

“I would situate Data Institutions and stewardship at that foundational level of who gets to use the data, for what purposes and under what conditions.”

Hardinges said interest in data governance and stewardship can be broadly divided into two domains: the corporate and the personal.

The former includes industrial use cases where new forms of stewardship can be used to “unlock latent economic value” from previously unconnected data sources, whereas the personal use cases refers to where individuals are more directly involved in the stewardship of their own data.   

“Those are useful ways of thinking about what’s happening and who’s involved in doing it, but they’re not mutually exclusive,” he said.

“Any sort of sector-based approach to bringing data together or making use of it should involve people that are affected by it, especially if it’s personal data.

“[However] the fact that data is so contextual is probably the significant barrier as it means there’s not one simple off-the-shelf data governance structure that you can pick up and apply to your particular context, and I don’t think that’s ever going to be the case.

“The difference between data in one sector to another, from one organisation to another, from one person and their interest in it to another is so different … there’s going to be so many different types of approaches that are required,” said Hardinges.

For example, a structure that governs how construction companies pool and share geospatial data would need to be completely different from a structure designed to facilitate the pooling and sharing of healthcare data between university researchers and doctors.

The involvement of different kinds of stakeholders in any particular institution also has an effect on what kinds of governance structures would be appropriate, as different incentives are needed to motivate different actors to behave as responsible and ethical stewards of the data.

In the context of the private sector, for example, enterprises that would normally adopt a cut-throat, competitive mindset need to be incentivised for collaboration. Meanwhile, cash-strapped third-sector organisations, such as charities and non-governmental organisations (NGOs), need more financial backing to realise the potential benefits of data institutions.

“Many [private sector] organisations are well-versed in stewarding data for their own benefit, so part of the challenge here is for existing data institutions in the private sector to steward it in ways that unlock value for other actors, whether that’s economic value from say a competition point of view, but then also from a societal point of view,” said Hardinges.

“Getting organisations to consider themselves data institutions, and in ways that unlock public value from private data, is a really important part of it.”

As with any initiative or technology designed to facilitate increased data sharing, data institutions will need to focus on building trust and, according to Hardinges, will “gain their mandates in different ways”.

For example, statistical agencies such as the Office for National Statistics (ONS) have a natural public mandate as a data institution to gather and steward data on behalf of others for public aims, while others’ mandates could arise from their trusted place within the business ecosystem they operate.

“There definitely is a cultural point of view which is to stop thinking about data as oil, and instead for organisations across the public, private and third sectors to think of themselves as stewards of that data, perhaps in the way that we think of the National Trust as the steward of our land – it has a duty to protect it, but then also to enable access to it for things to happen,” said Hardinges.

Finally, despite the significant interest in how ordinary people can be more involved in data governance, Hardinges warned that data institutions alone, even after securing their mandates, will not be enough to “move the dial and recalibrate and who has power in the data economy”.

“This doesn’t speak to the other actors and other behaviours that need to change in the data ecosystems, such as legislating for new rights or enforcing existing rights around personal data, or developing new standards and technologies – there’s a whole variety of things that need to happen if we are to see people become more empowered and involved in the stewardship of data about them,” he added.

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