The following piece is from FinXTech’s latest intelligence report, “Crafting Your Data Strategy,” sponsored by Wealth Access. Access the full report here.
Before he joined the credit union space, Dennis Klemenz worked for a decade in helicopter aviation. When he joined Jovia Financial Federal Credit Union as its chief technology officer in the spring of 2023, the now-$4.5 billion credit union had started building out a data warehouse that came with its new core banking platform.
Creating that data warehouse — which stores processed, transformed and structured data that’s ready to be used in a model or report — was “pretty much” the data management strategy at the time, he says. While this was a major undertaking, his goal was to take the project a step further and develop a way for the Westbury, New York credit union to leverage its data — not just store it.
But that turned out to be easier said than done. Klemenz found that creating a data warehouse for a company that builds military helicopters — what he did in his prior career — was simpler than completing the same project for a community financial institution. The aviation data was tighter and the redundancy of different systems meant that the information effectively came from a single source, creating a more controlled data environment. In contrast, data extracted from a system at a financial institution needs to be reconciled to the general ledger, complicating the efforts to build a data warehouse.
“Ironically, in a company where we defied the laws of nature and gravity, it was easier to [configure a data environment] than it is in financial services,” Klemenz says, comparing the two different industries.
Truly successful initiatives should help an institution think about itself differently, and a data strategy is no different. The goal for many institutions is to become a data-driven enterprise. That means the bank or credit union has integrated analysis into its business and uses it to inform decisions and better serve customers. These initiatives should spur creativity and challenge how employees think about and solve problems.
However, these projects inevitably encounter challenges, as Klemenz found, which means they require the commitment of time and resources from the institution’s board and management team to push them forward.
This commitment was essential at Sonata Bank, a unit of Sonata Financial Holdings, in Brentwood, Tennessee. Management decided early on they wanted to own the institution’s data and processes so they didn’t have to rely on a vendor, which meant prioritizing the necessary resources. Integrating fragmented systems can be cumbersome for small institutions, and the initial work to start these projects can be expensive.
“Making it a strategic priority at the board level — that data was going to be part of our DNA and was going to be how we operated — was really critical,” says Will Rhoads, chief innovation officer at the $222 million bank. “Being strategic about what [aspects] the bank owns and understands is one of the most important decisions it can make. To me, if data is not one of those, you’re missing the boat.”
Sonata set up an advisory technology committee that included directors, executive management and outside advisers. That committee educated the board about the work and resources the bank would need for a project. But the committee also helped build momentum, “galvanizing the entire board and senior management on why this is so important and why it’s so critical to who we want to be,” Rhoads says.
For some banks and credit unions, a successful data management project will also require a cultural and mindset shift. “I don’t think culture is one of those things that you can just mandate,” says Daniel Haisley, chief product officer at digital banking provider Apiture. “It is a [daily] honing of the blade based on the experiences that you have. You start with problems and use data to solve them, and that grows the contagion, which ultimately defines culture over time.”
The cultural shift has been one of the biggest revelations during MSU Federal Credit Union’s decade-plus data journey, says Benjamin Maxim, chief technology officer at the $8.2 billion credit union.
The East Lansing, Michigan-based credit union’s first steps in its data journey included creating a centralized business intelligence team that pulled these metrics together into a SharePoint site, which is a content and knowledge management tool from Microsoft Corp. This allowed business units to share data with other areas of the credit union, and the group eventually added household and membership information. But it also required Maxim to educate his colleagues working in legal, information technology and risk so they could understand why better data collection and analysis mattered.
Along the way, the credit union received an assist when it partnered with core and technology provider Jack Henry & Associates on an early predictive analytics project. That partnership led to MSUFCU realizing that members were likely to buy their next car 22 months after they purchased their previous vehicle — an insight that gave the institution a timeline of when to reach out to members about auto loans. MSUFCU has added more data as it has grown and hired a vice president of data analytics to oversee these efforts.
Maxim realizes all this work, and all these conversations, “seem big and scary — but you need to start somewhere.” He advises executives to pick a straightforward use case that can be solved with a discrete dataset and build from there — a process that will be laid out in the next section of this report. It’s easier, he says, when executives are working toward a specific goal, versus a vague concept such as “using my data better.”
“If you have something small, start small — but start,” Maxim adds. “And then data quality, data governance, the sooner you bring in those things, the easier it’ll be longer term.”
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