Jonathan van Geuns on Bridging the Gap Between People and Technology
Q&A with Jonathan van Geuns
Written by the DSH team*
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The Data for Society Hub (DSH) team spoke with Jonathan van Geuns to learn more about his work with Ana Brandusescu, their involvement in this project, and their objectives for the year.
Can you tell me a bit about your background?
Jonathan van Geuns (JvG): I’m from the Netherlands and I originally worked at Leiden University. After studying international law, I sort of moved into innovation management, and I did projects with many cities in the Netherlands to build different types of open data portals. I’ve been doing projects related to innovation management and data governance for the last two or three years, and I’ve also been working with the U.S. government on projects related to participatory data governance.
I’m always a bit on the fringe between theory and the practical, or the implementation of the theory. I also have a background in design-thinking approaches — all of this will be useful to the project [with the Data for Society Hub].
Ana [Brandusescu] has a slightly similar background. She worked for the Web Foundation for a while, and then she became a Professor of Practice at the Centre for Interdisciplinary Research on Montréal (CIRM). She’s currently doing her PhD on AI governance at McGill, and she’s been involved with the CIRM for over two years. So, Ana has been involved longer than I have, but our work has really just started.
How are you involved with the Data for Society Hub (DSH)?
JvG: When Ana and I started talking about our role in assisting the CIRM, we looked at the core elements that should be present in any technical project like this — we always look at the “people, process, and technology” [diagram]. We have Luc [Véronneau] who’s working on the technical side, and there is more high-level work and a lot of questions around legal structures and data governance on the process side.
And then you have the people. Projects can feel more technically-focused because there’s always the latest vendor’s newest tool, and with this project, there’s a lot of attention on the process side around data governance. But we need to look at all three elements, and sometimes the “people” side gets neglected because we take it for granted. We want to combine the three sides, which means first figuring out what types of people this project is [serving]. What do they do for work? How will they use the platform the DSH is building?
We’ll start doing interviews with all the partners soon to figure out the different types of users. We already have a broad perspective on it, but I’d like to dig deeper. Each partner organization probably faces different challenges and has different objectives, so we need to take a look at that and figure out the most important problems to solve and ideas to implement.
There’s a lot of theory around data governance, but what we really need is to know what people actually do on the ground and what is useful to them.
What type of work will you be doing?
JvG: Well, once we know what problems and ideas we’re working with, we’ll do a scenario-mapping workshop. That’s a fun little exercise where we validate our ideas with the people who do this as a day-to-day job, and we ask them if we’re missing anything and if there are ideas we can help them come up with. We also have a second workshop scheduled, which will be a prototyping workshop. Basically, we want to gather all the people who have an important perspective on either Commun Axiom or the platforms that we’re building in the data governance processes. We want to have them build these processes together; we want the people with the technical expertise to say what is and isn’t possible, and we want someone with a legal background to say, “Well, if we’re going to do that, we’ll have to look into this specific thing.” It’s a very collaborative exercise. I think that’s the best way to have people committed to using technologies and processes.
So, that’s what we’ll be doing until August or September [2022], and at the end of the year we’ll probably write up a report with recommendations.
What motivates you to be a part of this project?
JvG: There’s a lot of theory around data governance, but what we really need is to know what people actually do on the ground and what is useful to them. That’s something that I [care about] and I’m always very happy to work directly with people and to listen to them and see how their interests match up with the technical side. Being between the two sides is always interesting.
Some people are really tech-savvy, but for others…When you talk to a developer, the language they use can sometimes go above your head, which can be a bit complicated. I like aligning and translating between technical processes and departments, and that role is going to be important for this project.
How do you personally define the concept of “data governance”?
JvG: I look at it in a very practical sense. All the processes that are involved around ownership, roles, responsibilities, and people. Whatever is needed to get the job done. I’d like to delve more deeply into what kind of activities each organization needs to have, because it really depends on the type of organization. That’s something we still need to figure out.
What are some best practices that you envision for the DSH’s data governance model?
JvG: That’s a good question. I think a lot of it has to do with creating the cluster and sharing the data, and giving the organizations the ability to deliver the right quality of data and to keep using it. That’s probably what I personally aim to do here, but I’m really speaking from my own perspective.
What do you mean by “right quality” of data?
JvG: Well, a lot of the data is very sensitive, so you need to aggregate it to a point where the personal and identifiable information is not there anymore. But how useful is the data anymore if, for example, you want to look at different neighbourhoods in Montréal? Can you actually still use that information? We will have to look into different data sets, and we’ll have to see how much of it we can actually use.
What challenges do you believe the DSH is facing between now and 2024?
JvG: Having the commitment from everyone involved will be important. Another challenge is that I’d like us to keep having practical solutions. For instance, a data trust is an interesting concept and something that we can do in Québec, but we might also find out that it would take a long time to set it up, even if it doesn’t take much work. It may be “buzzy” and interesting to do a certain thing, but in the end, I want us to have something that the partners will actually use.
We want to build something for and with the people who will use the tool we make; it’s not just someone alone behind a desk making assumptions.
What are your next steps?
JvG: We are doing interviews with the partners in April, and we’ll hopefully get to speak with multiple people within each partner organization. I’d like to get to know the people as much as possible. I know that’s a commitment on their end, but we want to figure out what the word “people” means in this project. We want to know what perspective they will have on the DSH and how they’ll use what we build.
A citizen will have a different point of view than a data analyst at a partner organization or a policymaker in government. A data analyst will probably want to dig into all the specific data, while a policymaker or a citizen may only want a quick overview of what’s happening in their neighbourhood. You build an entirely different tool if you look at each perspective. We’ll have to narrow it down a bit because there might be 20 different use cases*, and we want to know which of the most important use cases the developers can build first.
What guides your work?
JvG: We use a lot of people-centred (or human-centred) types of processes to involve everyone in the project. We want to build something for and with the people who will use the tool we make; it’s not just someone alone behind a desk making assumptions. It’s important to involve people and to create a sense of ownership within them [regarding] the project.
It’s really rewarding to help people figure out and solve certain problems. I might not have the answer for them at first, but we can help each other get there as long as we’re involving the right people. I’m looking forward to it.
*“A way of using data that are of value or use to all involved parties. A “data use case” corresponds to a well-defined problem within a specific context, as well as to a set of actions carried out by the parties involved with the data in question, in order to achieve a final objective or purpose.” (Definition provided by Open North, Montréal in Common’s data governance framework. Toward a More Accountable, Efficient, and Collaborative Data Governance Process, Montréal, Montréal in Common, January 2022.)
**Since this interview was conducted, Ana Brandusescu has stepped back from the project to devote more time to her doctoral studies. Under the supervision of Renée Seiber (McGill), she is examining the role of artificial intelligence in governance and its impact on the use of automated decision-making systems in government.
This Q&A has been edited for length and clarity.
Compiled by Angelina Mazza; content editing: Karolyne Arseneault, Julie Levasseur, and Alexia Wildhaber-Riley.
