I find “governance” to be a tricky word. Particularly when we’re talking about the governance of data.
For example, I’ve experienced conversations with people from a public policy background and people with a background in data management, where its clear that there are different perspectives. From a policy perspective, governance of data could be described as the work that governments do to enforce, encourage or enable an environment where data works for everyone. Which is slightly different to the work that organisations do in order to ensure that data is treated as an asset, which is how I tend to think about organisational data governance.
These aren’t mutually exclusive perspectives. But they operate at different scales with a different emphasis, which I think can sometimes lead to crossed wires or missed opportunities.
As another example, reading this interesting piece of open data governance recently, I found myself wondering about that phrase: “open data governance”. Does it refer to the governance of open data? Being open about how data is governed? The use of open data in governance (e.g. as a public policy tool), or the role of open data in demonstrating good governance (e.g. through transparency). I think the article touched on all of these but they seem quite different things. (Personally I’m not sure there is anything special about the governance of open data as opposed to data in general: open data isn’t special).
Now, all of the above might be completely clear to everyone else and I’m just falling into my usual trap of getting caught up on words and meanings. But picking away at definitions is often useful, so here we are.
The way I’ve rationalised the different data management and public policy perspectives is in thinking about the governance of data as a set of (partly) overlapping contexts. Like this:
Whenever we are managing and using data we are doing so within a nested set of rules, processes, legislation and norms.
In the UK our use of data is bounded by a number of contexts. This includes, for example: legislation from the EU (currently!), legislation from the UK government, legislation defined by regulators, best practices that might be defined how a sector operates, our norms as a society and community, and then the governance processes that apply within our specific organisations, departments and even teams.
Depending on what you’re doing with the data, and the type of data you’re working with, then different contexts might apply. The obvious one being the use of personal data. As data moves between organisations and countries, then different contexts will apply, but we can’t necessarily ignore the broader contexts in which it already sits.
The narrowest contexts, e.g. those within an organisations, will focus on questions like: “how are we managing dataset XYZ to ensure it is protected and managed to a high quality?” The broadest contexts are likely to focus on questions like: “how do we safely manage personal data?”
Narrow contexts define the governance and stewardship of individual datasets. Wider contexts guide the stewardship of data more broadly.
What the above diagram hopefully shows is that data, and our use of data, is never free from governance. It’s just that the terms under which it is governed may just be very loosely defined.
This terrible sketch I shared on twitter a while ago shows another way of looking at this. The laws, permissions, norms and guidelines that define the context in which we use data.
One of the ways in which I’ve found this “overlapping contexts” perspective useful, is in thinking about how data moves into and out of different contexts. For example when it is published or shared between organisations and communities. Here’s an example from this week.
IBM have been under fire because they recently released (or re-released) a dataset intended to support facial recognition research. The dataset was constructed by linking to public and openly licensed images already published on the web, e.g. on Flickr. The photographers, and in some cases the people featured in those images, are unhappy about the photographs being used in this new way. In this new context.
In my view, the IBM researchers producing this dataset made two mistakes. Firstly, they didn’t give proper appreciation to the norms and regulations that apply to this data — the broader contexts which inform how it is governed and used, even though its published under an open licence. For example, e.g. people’s expectations about how photographs of them will be used.
An open licence helps data move between organisations — between contexts — but doesn’t absolve anyone from complying with all of the other rules, regulations, norms, etc that will still apply to how it is accessed, used and shared. The statement from Creative Commons helps to clarify that their licenses are not a tool for governance. They just help to support the reuse of information.
This lead to IBM’s second mistake. By creating a new dataset they took on responsibility as its data steward. And being a data steward means having a well-defined, set of data governance processes that are informed and guided by all of the applicable contexts of governance. But they missed some things.
The dataset included content that was created by and features individuals. So their lack of engagement with the community of contributors, in order to discuss norms and expectations was mistaken. The lack of good tools to allow people to remove photos — NBC News created a better tool to allow Flickr users to check the contents of the dataset — is also a shortfall in their duties. Its the combination of these that has lead to the outcry.
If IBM had instead launched an initiative similar where they built this dataset, collaboratively, with the community then they could have avoided this issue. This is the approach that Mozilla took with Voice. IBM, and the world, might even have had a better dataset as a result because people have have opted-in to including more photos. This is important because, as John Wilbanks has pointed out, the market isn’t creating these fairer, more inclusive datasets. We need them to create an open, trustworthy data ecosystem.
Anyway, that’s one example of how I’ve found thinking about the different contexts of governing data, helpful in understanding how to build stronger data infrastructure. What do you think? Am I thinking about this all wrong? What else should I be reading?