By Tyler Pihl
Boards provide strategic oversight rather than manage operations. That distinction matters because the most consequential technology decisions facing credit unions today, from core processors to digital banking, lending platforms, CRM, fintech partnerships, and the deployment of AI across the organization, belong squarely with management. And yet, the strategic agility of the institution increasingly depends on how well management governs its data.
That makes it a legitimate board topic. Directors don’t need to understand the technical details. They need to ensure management is positioning the institution for the next decade rather than the last one.
Why This is Becoming a Strategic Conversation
Three things have changed what’s at stake. First, the speed of business has compressed decision windows across the board. Payments speed, credit decisions, and member service expectations have all moved from days to minutes. Institutions whose data moves on legacy cycles cannot operate at the speed their members and partners now expect.
Second, AI has accelerated both the opportunities and the threats. AI is reshaping every function inside the institution, while bad actors use the same technology to deepfake, mimic voices, and run social engineering at scale. Vendors are releasing AI capabilities faster than most institutions can absorb them. The institutions positioned to benefit, and to defend, are the ones whose data foundation can actually feed those capabilities. Bad data into AI produces bad outcomes, regardless of how good the model is.
Third, product and partner evolution is outpacing legacy infrastructure. Fintech partnerships and new product types increasingly require data to flow in ways legacy systems were not designed to support. The infrastructure choices being made today will determine what is possible five years from now.
The Data Governance, Strategic Agility Link
Strategic agility is the ability to absorb new products, threats, vendor capabilities, and regulatory expectations without rebuilding the foundation each time. Institutions that struggle tend to share certain traits: disconnected platforms, fixed data models, point solutions whose signals never come together, and data flows built reactively over time.
Institutions with strategic agility share the opposite: flexible ingestion, an intentional view of where data lives, and a coherent picture of how signals come together for decisioning.
A class of widely adopted AML and fraud platforms in the credit union space offers a useful illustration. These platforms operate on standardized data models. The vendor defines the schema and pushes consistent models across clients. The benefits are real: consistency, lower internal capability requirements, vendor accountability.
The strategic cost is also real: institutions on these platforms cannot accept data outside the schema, cannot author rules for emerging trends, and lose AI explainability, creating problems with examiners, the board, and operations alike. The same trade-off exists anywhere the institution accepts vendor-defined data models.
Questions the Board Should be Asking
A small set of questions, applied across technology investments, will surface whether the institution is building toward agility or accumulating constraint.
- Data foundation. Is institutional data routed through point-to-point integrations built for single use cases, or through a data layer that can serve multiple consumers?
- Flexibility. Can existing organizational data be accessed for new use cases easily, or does each new application require new integrations and mapping?
- Vendor control. Can the institution influence what data is captured, how it is structured, and at what frequency it moves? Or is the institution locked into the vendor’s defaults?
- AI readiness. When systems produce an output, whether a fraud alert, credit decision, or AI recommendation, can management explain why? Is the data foundation prepared to use new AI capabilities responsibly, or is the institution defaulting to “wait and see”?
- Member experience. Is the institution using its data for real-world member applications today, whether stopping fraud before loss or delivering personalized service, or are the use cases still theoretical?
A Maturity Framework
- Foundational. Vendor-defined integration paths. Fragmented data, manual reconciliation, batch processing across most use cases, stale analytics. Every new product is a one-off project.
- Developing. Data flows mapped intentionally. Some platforms chosen for flexibility. Real-time exists for the highest-stakes channels but is not pervasive. Cross-system visibility exists in pockets.
- Advanced. Intentional architecture with a multi-use data layer that serves multiple consumers from common sources rather than point-to-point feeds. Real-time or rapid data access covers the use cases that depend on it. New products, partners, and channels can be onboarded into the data environment without rebuilding integrations.
- Leading. Data governance underwrites every major technology investment. Decisioning operates against a unified, real-time view of the member across functions. Explainability is preserved across the entire stack, including AI components.
Most credit unions operate between foundational and developing. The strategic question is whether management has a clear plan to advance.
What This Means for Boards
The principles of data governance extend to every significant technology investment. The decisions made today show up in member experience. Boards that ask the right questions are doing what strategic oversight requires: ensuring the institution is positioned to operate, compete, and adapt in an environment moving faster than legacy infrastructure can handle.
The institutions that thrive over the next decade will be the ones whose boards understood early that data governance is a strategic topic.
Tyler Pihl, CPA, is Strategy, Risk & Assurance Partner with Rochdale.
