The materials available in this article are for informational purposes only and not for the purpose of providing legal advice. You should contact your own advisors with questions regarding the data strategy content herein. The opinions expressed in this article are the opinions of the individual authors and may not reflect the opinions of MeridianLink, Inc.
Originally published on CUInsight.com
After almost three decades of helping organizations unlock the value of their data, I’ve found that one question stands out as one of the most important you can ask: How can we make our data truly work for the entire business, instead of being trapped in a single system?
It’s a powerful shift in perspective. Too often, organizations force themselves to adapt to whatever their systems can spit out. But if we want data to actually drive smarter decisions, growth, and member value, we need to flip the script. We need to build business-first data strategies. Data strategies that unite people, processes, and systems.
In a recent series of interviews I did with Chief Data Magazine, I talked about what separates data-mature organizations from those still finding their footing. The lessons apply directly to credit unions: building data communities, aligning on common definitions, breaking down silos, and identifying the KPIs that truly matter.
Let’s walk through what that means for you.
Building a Data Community Inside Your Credit Union
Data touches every corner of your organization—lending, deposits, member engagement, compliance, IT, marketing, you name it. That’s why I encourage organizations to think about creating a data community.
What’s a data community? It’s a virtual organization made up of people who either work directly with data or depend on it to make decisions. That includes:
- Data analysts and business intelligence staff
- Department leaders who rely heavily on accurate reporting
- Line-of-business managers who live and breathe KPIs
The goal isn’t to create another bureaucracy. It’s to break down boundaries and foster dialogue so that everyone is speaking the same language when it comes to data.
Imagine the difference when lending, finance, and marketing leaders don’t just bring their own spreadsheets to the table but actually work off the same shared definitions. Suddenly, you’re not arguing about whose numbers are right, you’re aligning on what those numbers mean.
Breaking Down Silos and Aligning Definitions
Here’s a common scenario I see: Finance says the credit union has X number of members. Marketing says it’s Y. Operations reports Z. They’re all “right,” but they’re all using different definitions.
That’s what happens when data lives in silos. And silos create confusion, mistrust, and missed opportunities.
The solution isn’t glamorous, but it’s critical: documentation and transparency.
- Agree on enterprise-wide standards when you can.
- Where you can’t, make differences explicit by renaming or labeling them. (A “service week” is not the same as a “calendar week.” Call it what it is.)
- Build a glossary of common terms and definitions.
It sounds simple, but this discipline is what separates immature data organizations from those that thrive.
Why Sustaining Data Alignment Requires People, Not Just Systems
Even if you align everyone in a meeting today, business moves too fast for that alignment to stick without some level of oversight. Definitions drift. Exceptions creep in. Suddenly, you’re back to competing spreadsheets.
That’s why sustaining a data community requires dedicated resources. Not just engineers building pipelines, but people focused on context and alignment.
That can take many forms:
- A recurring meeting cadence open to anyone with data responsibilities
- An internal knowledge hub for definitions, best practices, and certified reports
- A collaboration space—chat channels, forums—where questions can be raised and answered
When credit unions build these mechanisms, they give employees practical ways to stay aligned on data. But here’s the key: these resources don’t always have to be internal. Many credit unions, especially those balancing lean teams, look to trusted partners to help sustain that alignment.
Partners like MeridianLink® can provide not just the technology, but also the consulting expertise and data strategy services that keep definitions consistent, processes clear, and communities engaged. In other words, you don’t have to build the entire framework on your own—leveraging the right tools and guidance can accelerate your journey and free your team to focus on members.
Identifying What Really Matters: Critical KPIs
One of the first questions I hear when it comes to building a mature data strategy is: Where do we even start?
My advice: Start with the metrics that finance reports to regulators and the board. Revenue. Members. Loan portfolios. Those are your immovable anchors. Then, work across teams to understand where different perspectives exist and decide what can be standardized versus differentiated.
From there, move toward certified data models. For credit unions, this means creating trusted, pre-approved data sets that reflect the way your organization defines members, accounts, and lending products. When analysts and business users can pull from a single source of truth, they spend less time wrangling spreadsheets and more time generating insights that improve member service and strategic decision-making.
This is also where many credit unions benefit from the right partner support. By leveraging a centralized data warehouse along with integrated intelligence and analytics solutions, you can bring together data from your core, lending, and digital platforms—transforming fragmented information into certified KPIs that everyone in the credit union can rely on to make consistent, confident decisions.
Building Trust in Data
Here’s the reality: Trust in data isn’t something you fix after it’s broken. It should be built in from day one.
That means:
- Making data pipelines transparent (logs, lineage, refresh cycles)
- Setting clear expectations for accuracy and timeliness
- Documenting how and why data is transformed
- Building business glossaries to make technical concepts accessible
And it means proactively communicating when something goes wrong. Ironically, how you handle data issues can build trust just as much as how you handle data accuracy.
As AI starts to play a bigger role in credit unions, this trust becomes even more important. AI is only as good as the data it’s fed. Without guardrails and context, it won’t know when data is wrong or misleading.
Making Data Work Across Your Operations
Back to the original question: How do we make data work across our operations?
Most credit unions have grown up with a patchwork of legacy systems—core, lending, online banking, payments, CRM, and so on. Each system has its own way of defining, storing, and reporting data.
If you try to manage data system by system, you’ll never get the full picture. Instead, leverage your data community, aligned definitions, critical KPIs, and strong data quality practices to build business-first data models that focus on what matters most to your credit union. When these models are applied across systems, high-quality data can flow seamlessly, enabling intelligence and analytics tools to produce reports and dashboards that are meaningful, consistent, and aligned with the questions your teams need to answer.
That’s when data stops being a siloed resource and becomes a shared, actionable asset—driving better decisions, smoother operations, and stronger member experiences.
Why This Matters for Credit Unions Today
Data is no longer just about reporting. It’s about engagement, personalization, risk management, compliance, and growth. Your members are telling you who they are and what they need every day—through transactions, applications, and digital interactions. The question is: Can your credit union listen at scale?
That’s why the team at MeridianLink is focused on solutions that integrate data across systems and provide the intelligence, analytics, and services you need to act on it.
Because ultimately, it’s not about making the data in your systems work. It’s about making data work across your systems—for your members, your staff, and your long-term strategy.
