Most data leaders aren’t solving the wrong problems. They’re solving the loudest ones, rather than the ones that would drive the greatest measurable business impact.
New platforms get implemented. AI initiatives get approved. Modern data stacks get built. And yet the same friction sticks around.
Reports get questioned. Decisions take too long. Teams still argue about which number is correct.
The issue usually isn’t effort or investment.
It’s that nobody stopped to quantify what the gaps are costing the business before deciding what to fix first.
That changes the conversation entirely. It’s the difference between telling your CFO:
"We need to improve data quality."
...and telling them:
"Our data quality issues are costing us $340K a year in wasted marketing spend and support tickets."
One gets a nod. The other gets a budget.
Once you can start quantifying the problem, a few things usually become obvious:
- The real cost of manual workarounds. Not “we have inefficiencies,” but exactly what those workarounds cost in hours, dollars, and delayed decisions
- Which AI initiatives are ready to move, and which ones will quietly fail because the foundation isn’t there yet
- Where business and IT are misaligned, and what that misalignment costs in duplicated work and stalled projects
- What should be fixed first, ranked by business impact, feasibility, and speed to value
