Every January, data leaders announce bold visions: “We’re building a data mesh!” or “AI will transform our business!” By Q2, you’re still fighting about RACI charts and the ‘definition of done’.
The gap between data strategy and execution isn’t a technology problem, it’s an alignment problem. Here are five resolutions that bridge that gap, starting with the conversations most teams avoid.
1. Define What “Data-Driven” Actually Means (For Your Company)
Everyone wants to be “data-driven.” Nobody agrees what that means. Does it mean every decision needs a dashboard? That gut instinct doesn’t count? That we pause until we have perfect data?
The fix: Run a 60-minute workshop with leadership. Ask: “Show me a recent decision where data changed the outcome.” Then ask: “Show me one where we had data but ignored it, and why.”
These stories reveal your actual decision-making culture, not the aspirational one. Now you can build a strategy that fits reality.
What changes: You stop building analytics nobody trusts and start fixing the decision points that matter.
2. Stop Treating Every Data Request Like It’s Strategic
Marketing wants daily Instagram metrics. Finance needs quarterly board reports. Engineering needs real-time system monitors. Your data team treats them all the same, and burns out.
The fix: Create a simple tiering system:
- Tier 1: Impacts company strategy or regulatory compliance
- Tier 2: Drives departmental decisions
- Tier 3: Nice-to-have reporting
Tier 1 gets custom solutions and white-glove support. Tier 3 gets self-service tools or a polite “no.” This isn’t about playing favorites, it’s about aligning resources with business impact.
3. Map Your Real Data Supply Chain
You probably have an org chart showing who reports to whom. Do you have a map showing where your revenue number comes from? Which systems feed it, who owns those systems, where the calculations happen?
The fix: Pick your most important business metric. Trace it backward through every system, transformation, and hand-off. Document who’s responsible at each step. Publish it.
Note: This should fit on a napkin at first. This isn’t an extensive effort to document data-lineage. Start with the broad strokes.
Why this matters: When that number breaks (and it will), you’ll know exactly who to call. When leadership questions it, you can explain the lineage. When you plan improvements, you know where the leverage points are.
Start with: The KPI you get pestered about the most.
4. Have the “Buy vs Build” Conversation You’ve Been Avoiding
Your team spent 6 months building a customer data platform. Turns out xyz would’ve worked fine for 1/10th the cost. Or vice versa, you’re paying for an enterprise tool when a simple pipeline would suffice.
The fix: For every major data initiative in 2026, explicitly answer:
- Can we buy this capability? What would it cost?
- If we build it, what’s the true TCO (engineering time, maintenance, opportunity cost)?
- Does building this create competitive advantage, or are we just rebuilding commodity infrastructure?
Hard truth: Most companies should buy more and build less. Your data engineers should solve problems unique to your business, not rebuild what Airflow already does.
Note: This exercise isn’t just for solutions, consider team structure as well.
5. Establish ONE Shared Definition (Just One)
Your sales team says you have 1,247 customers. Finance says 1,189. Product says 1,356. Everyone’s right based on their definition, and every executive meeting devolves into definitional debates.
The fix: Pick the most contentious term in your organization (“customer,” “active user,” “revenue,” etc.). Get the stakeholders in a room. Debate until you have one definition everyone commits to. Document it. Enforce it.
Don’t aim for perfection: The definition matters less than the agreement. A “good enough” shared definition beats three perfect but conflicting ones.
Success metric: Your next exec meeting spends zero minutes arguing about what words mean.
The Meta-Resolution: Measure Strategy Success Differently
Stop measuring your data strategy by:
- ❌ Pipelines built
- ❌ Dashboards created
- ❌ Data quality scores
Start measuring by:
- ✅ Decisions that changed because of data
- ✅ Questions answered without custom analysis
- ✅ Time from question to insight
Your Q4 2026 review should answer: “What decision did we make differently because our data strategy improved?” If you can’t name three, your strategy isn’t working.
If all of this still feels aspirational, just pick one and make it your pet project. Split it up into smaller parts if that still seems too much.
The new year is a time where we all attempt to generate entirely new personalities, and reach for goals that never make it to February. Why not take the down time to knock out the things that you’ve been putting off. Things that can really move the needle.
Once you get some momentum, here are some ideas for how to dig deeper: