Data analytics for credit unions and banks who mean business

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 ...

Sometimes, financial institutions mistake their data warehouses for nothing more than massive data dumps. But in reality, a data warehouse should be your business powerhouse, not an operational data store. The Gemineye Team Explains Why Data Warehouses are More than Data Dumps Gemineye crew Brewster Knowlton, CEO and Maggie Chopp, Director of Business Development, discuss why true data warehouses are about much more than storage. A proper data warehouse should model, normalize, and unify data from multiple sources to create a single source of truth that provides the foundation for ALL decision-making and AI initiatives.  Key Takeaways in this Video Include: Data warehouses are about the quality of the data process, not just storage The true power of a data warehouse lies in its ability to model, clean, and define data consistently Organizations often mistake staging layers or data lakes for warehouses Effective data warehouses implement strict modeling, business logic, and normalization to enable scalable, insightful analysis The unsexy groundwork is the real differentiator in data maturity  Full Transcript Alicia Disantis: Maggie, aren’t data warehouses just big data dumps? Maggie Chopp: No, they’re not, but I can understand why some people might think that way. I’m a data warehousing originally became a topic. People were treating it that way and just plopping everything in the spot and checking off the box and hoping that got them some level of result. We know now. It’s been a long time since those days. Your data doesn’t do anything for you when it’s just sitting there. So, theoretically, yeah, you could have one of these at your organization. But if data warehousing is done well and right, it’s got a lot of things that are happening from the ingestion to the modeling, the centralizing, the logic, data cleaning, if you have it going on, out to the actual analysis. So, no, a good data warehouse is actually producing an effect that the teams are then using to drive business outcomes. So, no, they’re not just big data dumps, but we understand why. That’s a really common misconception. Brewster Knowlton: The reality is, if you think your data warehouse is just a big data dump, congratulations. You don’t have a data warehouse. You have an operational data set like what you’ve just described. And a lot of cases is a staging layer or an ODS where you’re accumulating all this information. All of it is more or less raw, maybe with some minor date and D tagging and metadata, but it’s generally just a large swath of data. When you get into the warehouse, that’s when you actually get into dimensionalization and modeling. I might have four different subject areas for different sources, excuse me, that have records about a member. Well, that needs to be in one spot so that I don’t have to go to four separate places to get my definition of a member. If I’ve done that, I’ve just created a fancy version of the isolated and disparate systems that I already have today. So the data warehouse is where it comes up a lot more now in the context of AI is where that context, that awareness, that curation of not just data from a normalization perspective, but from an actual business definition standpoint has to be stored. Because if I have to go to seven different places and know all of these rules intuitively, there’s no scalability and there’s no leveraging the idea of what a data warehouse or lakehouse or whatever you want to call it, just this centralized, consolidated, mastered, normalize where definitions and logics are applied. That has to be there. Everyone wants to talk about all the stuff that they want to do with AI. That’s like trying to design your bathroom in your kitchen before you figured out how big of a house you want to have. Do I need a foundation? It’s like you can’t just go shopping for all the cool stuff until you’ve done the unsexy stuff, but that’s the important pieces that lay the groundwork, the foundation, literally and metaphorically for what you want to accomplish with AI in the future. And of course, all of your natural other business focused data outcomes. Maggie Chopp: And the last thing I to add, Alicia, because this is a really interesting question, is we talked to lots of credit unions that are going through a self-assessment process, and what we find is that they may say, hey, we have ten data sources in our data warehouse, but when you really look under the hood, they have two that are maybe, you know, being adjusted and modeled and used, and they have maybe eight other that are just being kind of dumped in. So we really try to look beyond the surface level of is the data there, and present, to is the data being used, is kind of a different question. Want More Content on Starting Your Analytics Journey? Download Our Whitepaper.