February 10, 2026

The best data strategy is the one you can actually execute.

I’ve seen too many data strategies that look brilliant on paper and fizzle. Not because they were wrong, but because they were designed for a different organization than the one trying to implement them.

Across different industries and organization types, this pattern repeats. The context changes, but the failure mode is remarkably consistent: strategy disconnected from organizational reality.

I’ve started framing data strategy around three questions. They’re not complicated, but they require honesty. And that’s where most organizations struggle.

Question 1: Where is our data maturity today, really?

Not where you want it to be. Not where your board presentation says it is. Where it actually is.

Can your lending team trust the data in loan reports without manually checking it? When marketing needs member segments, how long does it take, and how confident are you in the accuracy?

It’s tempting to see what you’re building rather than what currently exists. But strategy built on an inflated starting point is strategy built to fail.

Most organizations sit at maturity levels 2 or 3 out of 5. That’s not a failure; that’s a starting point. The failure is pretending you’re somewhere you’re not.

Question 2: What business outcome do we need to enable in the next 12 to 18 months?

Notice I didn’t ask “what data capabilities should we build?” Start with the business, not the technology.

The answers will vary by organization:

  • Lending: “Reduce loan decisioning from 3 days to same-day”
  • Member Experience: “Identify at-risk members before they leave”
  • Operations: “Handle 20% more volume without adding staff”
  • Compliance: “Reduce exam prep time by 40%”

Each of these requires different data investments. A $200M credit union focused on loan growth needs different capabilities than a $5B institution focused on operational efficiency. The investment case needs to be specific: What decision will this data capability improve? How much is that improvement worth?

Question 3: What can we realistically sustain?

This is where ambition meets reality.

You can launch almost anything with enough initial energy. The question is whether you can sustain it. A sophisticated analytics program requires ongoing investment in people, technology, and governance. A comprehensive data quality program requires dedicated attention, not spare cycles.

If you are launching data or AI initiatives, ask this difficult question: If we build this, can we run it with the team and budget we’ll actually have? Does the commitment exist to continue the investment and trust me, these initiatives are not necessarily cheap.

How These Questions Connect

Your answers to questions 1 and 2 define your gap. Your answer to question 3 determines how fast you can close it.

A smaller organization at maturity level 2 with limited data staff shouldn’t pursue the same strategy as a larger institution at level 4 with a dedicated team. The principles are the same; the pace and scope differ.

This is what I mean by “right-sized” strategy: not the most ambitious plan you can imagine, but the most impactful plan you can execute.

When you assess your current data maturity versus what your business strategy demands, what’s the biggest gap, and what’s making it hard to close?