Does data governance affect the success of a credit union or bank’s data program in reality? Join Brewster Knowlton, CEO, Matt Jefferson, COO, and Maggie Chopp, Director of Business Development at Gemineye as they dive into the reality of the influence data governance has on data programs.

In the words of Brewster, “If you don’t have data governance, you have data anarchy.”

The Gemineye Team Discusses How Data Governance Affects FIs’ Data Programs

Gemineye crew Brewster Knowlton, CEO and Maggie Chopp, Director of Business Development, and Matt Jefferson, COO, dive into the reality of the influence data governance has on data programs. Learn why data governance is the foundation of a successful data a program at a community financial institution, regardless of size. Whether a credit union or bank is $300 million or $25 billion, data governance basics, like ownership, data definitions, and business use are key to identify early. 

Key Takeaways in this Video Include:

• Why data governance must be owned at the organizational level, with executive buy-in
• Why data governance prevents teams from arguing over whose data and definitions are “right”
• How centralized data definitions prevent silos and conflicting reports across departments
• Why clear business definitions (like “member” or “application turnaround time”) are the first and most critical step
• How data governance is an ongoing journey, not a one-time initiative

Full Transcript

Alicia Disantis: Okay. So, Matt, how much does good data governance really play into the success of data?

Matt Jefferson: Yeah, I think it matters pretty pretty significantly in terms of, you know, analytics. And, you know, what would you do in that space?

I think one of the key things that we see is it’s not just something that you can outsource to some group, you know, three levels down from the exact team.

His exact team really has to take ownership of data governance and, and really push from a culture perspective to say, hey, are we all talking about the same business definitions? Are we all, you know, reading from the same sheet? Right. That has to be the culture, right? You can’t you can’t just take get a data governance and push it down a couple levels.

It has to be an organizational initiative. And I think that’s one of the key things that we we maybe see sometimes, and then I’ve seen in the past, is that you don’t have buy in from, from the highest levels of the organization. That is important because you can’t do data governance and still have a bunch of silos.

Maggie Chopp: Data governance is the ballgame. Data governance is your data program. If you don’t have data governance, the alternative is you have just a lot of use cases out there floating around. But again, if people can’t trust the data, they’re not going to work with it is what we’ve found. And so governance is the foundation of that.

We have a lot of credit unions that we work with, community banks where they maybe have 1 or 2 data analysts within the organization that everybody trusts. And unless it came from that person, they don’t count it. And in order to move on from that model and actually have reusable, trustworthy assets, there’s got to be a shift when it comes to data governance, the only way you get out of that is by implementing and having trusted assets.

Brewster Knowlton: If you don’t have data governance, you have data anarchy, which is where you spend 45 minutes in a 60 minute meeting arguing about whose data is more right. And then everyone walks away pissed off. So the reality is that without governance, you don’t have your business logic centralized, which means that one person A from department A, person B from department B go to try to pull the data in two different answers because they’re doing it two different ways.

Themes that we’ve talked about throughout this entire time around, that centralization of context and curating that information so that when we pull it, there’s no ambiguity about what it means. Those are all things that I don’t care how fancy the tech. You can go spend $1 billion and try to build out the coolest tech in the world. If you don’t have those basic foundational data governance elements in place.

And we’re not talking, you know, you can go overly complicated when it comes to data governance. But the foundational elements agreed upon and universally accepted or at least adhere to, it’s not fully accepted. You’re not going to have success, period, with your data program.

So what would you say the key foundational elements of data governance are that you mentioned that all need to be agreed upon. Start with the basics. What are your key business definitions. And I know this is everyone’s eye roll. Question and credit union land. But define a member and don’t just define a member from some abstract conceptual perspective very tactically, and translate that to a technical definition of what that means.

Are we including people that are under 18? What about POA a trust guarantor joint account? Very granular. We get into arguments all the time. Oh. Whether or not considered a member, if they had a charged off account or a negative share. But does that really make sense? And does your organization agree on that? You can have a varying, you know, definition and whatnot. I’m not saying one is right or wrong, but your organization needs to agree on that from there.

Then it becomes a little bit more branched off in terms of whether you want to focus on securing your data assets and how we want to define them, whether that’s GBA, whether that’s PII, whether that’s any other type of from a regulatory compliance perspective, starting to tag those, whether you want to get into kind of looking more along the lines of, data lineage and understanding.

So thinking about our future state where we may have to say if we change X, what is the downstream impact of that? That’s when you start to branch into maybe some of the more nuanced aspects of data governance. From my perspective, if you just start off with the basic business definitions and those key data elements in both practical conceptual business terms as well as a very clear binary definition of what that means and whether something adheres to it. If you can do that, you’re off to a probably 98% better head start than most of your competition.

Yeah, I agree. I mean, I agree 100%, right? It’s the it’s the business definitions. And it’s to Brewster’s point, it’s it’s not easy. Right. It’s not easy to sit people down and say, hey can we agree on what a member is? Right, Brewster use that example and that’s actually pretty difficult, right? You have to agree on a definition and you can’t, at the end of the day say, well, we we’ve been reporting, you know, you know, members this way or customers this way or this and that, like in this group.

Matt Jefferson: So this group’s reporting that. So we’ll just have to you have to come down to one definition to get the organization speaking from the same sheet of music. You have to do it. And it’s difficult and it’s going to be some difficult conversations.

Most of our credit unions use some kind of auto decisioning tool. And so it’s taking an application that took zero seconds to decision and moving it into the pool of the applications that took 20 minutes to decision. And so unless you have somebody from lending and somebody from the branches in the room to agree on what application turn time is, you’re not going to come to a very clear answer about what’s going on.

Brewster Knowlton: And that’s exactly why you need not just cross department, but as I think Matt said, you have to have both executives as well as people that are more tactical involved, because there’s going to be some of those things where the executive is trying to understand something from a strategic level that needs to be appropriately translated to the tactical team.

And on the flip side of that, those tactical individuals need to be able to say, well, we can’t do that, or we actually have to take this into account because X, Y, and Z, you have to bring across the organization and top down to be able to get a clear picture of what the impact of these. And that’s where data warehouse comes in.

We have a lot of clients that will come to us and say, well, we want to get our data governance in order first. You’re never going to get it fully in order, because just like data as an entire journey, it never ends. Some of the best things that we’ve seen our clients employ Gemini for, specifically through a data governance context, is to be able to test what it would have looked like had they used definition A or definition B to understand the impact of that.

That could be credit decisioning, that could be member definitions, that could be a host of other things. Those are scenarios where you don’t have to go in having all of the answers, but start to set the stage for knowing that you have to answer these questions throughout the process.

Maggie Chopp: Most of our credit unions use some kind of auto decisioning tool. And so it’s taking an application that took zero seconds to decision and moving it into the pool of the applications that took 20 minutes. The decision. And so unless you have somebody from lending and somebody from the branches in the room to agree on what application turn time is, you’re not going to come to a very clear answer about what’s going on.

Brewster Knowlton: And that’s exactly why you need not just cross department. But as I think Matt said, you have to have both executives as well as people that are more technical involved, because there’s going to be some of those things where the executive is trying to understand something from a strategic level that needs to be appropriately translated to the tactically and on the flip side of that, those tactical individuals need to be able to say, well, we can’t do that, or we actually have to take this into account because X, Y, and Z, you have to bring across the organization and top down to be able to get a clear picture of what the impact of these.

And that’s where data warehouse comes in. We have a lot of clients that will come to us and say, well, we want to get our data governance in order first. You’re never going to get it fully in order, because just like data as an entire journey, it never ends. So some of the best things that we’ve seen our clients employ Gemini for, specifically through a data governance context, is to be able to test what it would have looked like had they used definition A or definition B to understand the impact of that.

That could be credit decisioning, that could be member definitions, that could be a host of other things. Those are scenarios where you don’t have to go in having all of the answers, but start to set the stage for knowing that you have to answer these questions throughout the process.

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