Optimizing Data Strategy Through Resource Allocation

One of the more compelling truths about data in banking is that most institutions already possess all the data they need to drive change at their organization.  

The issue? Most are missing the key to unlock it.  

We’ve often seen that banks will try to add more to improve their data strategy after gaps are identified — more staff, more systems, more enhancements, more data. But doing so can be akin to putting a band-aid on a broken bone if they’re not focusing on the right problem.

Banks typically don’t need to add more to the equation, they need to strengthen what they already have internally by centralizing their data and setting efficient processes in place around it. 

Two KlariVis clients, Raymond Goodrich, EVP & Chief Lending Officer of Bank of Charles Town ($870 million in assets out of Charles Town, WV) and Jim Donnelly, President & CEO of Wayne Bank ($2.28 billion in assets out of Honesdale, PA) recently addressed a room full of bank executives at the inaugural KlariVis Data & Innovation Summit. They shared that layering multiple resources on top of their data problems did not help them optimize their operations. It was through a clear vision of their data  in KlariVis’ platform that enabled both banks to adjust processes and reallocate existing resources to better position their teams to excel at their jobs and drive growth.

Shifting Mindsets, Shifting Culture  

The transformation has to begin with updating community banking’s historical narrative around data.  Data and the bank’s strategy around it can no longer be considered the responsibility of one single person, department, or even system: It needs to be managed as a shared resource that provides the insights needed for everyone to do their jobs better.  

We’ve seen that banks who successfully democratize their data see a significant increase in operational efficiency. This means that data across silos is compiled into one single-source-of-truth, accessible to everyone. A platform like KlariVis achieves this, giving banks the opportunity to optimize their operations to increase efficiency, accelerate growth, and manage risk. 

Donnelly states that despite being profitable and efficient by most measures, before implementing KlariVis, Wayne Bank struggled with different departments bringing conflicting numbers to joint meetings. Time was wasted debating when and where reports were run rather than spending that time making strategic decisions to drive the bank forward. As he notes, being such analytical professionals by nature, it ’s is particularly frustrating for bankers to lack a single version of truth to draw from. 

Shifting the bankers’ mindsets from locating the data to leveraging it took an organizational culture shift, but has since proved immeasurably worthwhile to the team. 

Clear Data, Clear Action 

Cultural change follows naturally when data becomes accessible across the institution. Monday morning meetings evolve from vague updates such as, “The loan pipeline is doing great!” to strategic discussions about weighted average rates, portfolio concentrations, and market opportunities. At Bank of Charles Town, Goodrich’s meetings with production staff now include specific, insightful metrics like: “We have a pipeline of $[X]M with an average weighted interest rate of [X]% concentrated [X]% in C&I loans.” This precision allows the team to focus their attention on driving meaningful action. 

The Harvard Business Review found that organizations with data-driven cultures are three times more likely to report significant improvements in decision-making, helping shorten the time-to-action gap. And we see this in practice: lending teams become more deposit-focused when they can see relationship profitability at a glance. Commercial lenders, naturally competitive, improve their performance numbers because their department data becomes available. Donnelly reveals to the group, “I didn’t put any money on the table,” to incentivize his producers. “I put up a chart.” 

Start Small, Think Big 

Financial institutions should prioritize making current data more accessible and understanding the desired business outcomes before expanding their data infrastructure. It can be tempting to try and implement the flashy, trendy technology your employees can’t stop talking about (AI, anyone?), but you must ask yourself: Is this solving a real problem, or are we just adding more complexity to an already strained system? 

TD Bank Executive Product Owner and Data Strategy Head Juan Gorricho, keynote speaker of the KlariVis Data & Innovation Summit, reinforces this approach: “Scaling up doesn’t mean more technology, it means that your proof of concept is viable to grow.” This philosophy has proven effective in practice. Banks attempting too many simultaneous initiatives often struggle with implementation, while those focusing on optimizing a few existing resources are primed to drive tangible growth. 

As the banking industry continues to move at a faster pace, success increasingly depends on a bank’s ability to transform their existing data into actionable insights. When institutions focus on strategic resource allocation, they don’t just improve efficiency — they build cultures where data drives accountability, innovation, and growth.