The most common finding in every data audit we run.

If there's one finding that shows up in nearly every data and technology audit we run, it's this: the same metric exists in three different systems, calculated three different ways, and nobody in the room realized that until we put all three side by side.

"Revenue" sounds like it should be unambiguous. In practice, the finance system's revenue figure includes different adjustments than the CRM's forecast-to-actuals view, which itself differs from whatever number the operations dashboard was built against a year ago by someone who's since left the company. Each version was correct for the purpose it was originally built for. None of them were reconciled against each other, because reconciling them was never anyone's explicit job.

This single fact -- not a lack of data, not bad tooling, just unreconciled definitions -- explains an outsized share of the disagreements that show up in executive meetings. Two people can look at genuinely different numbers, both be right by their own system's logic, and spend half a meeting arguing about a discrepancy that was never actually a disagreement about the business -- it was a disagreement about which spreadsheet's math to trust.

The fix isn't a bigger BI tool. It's a single source of truth for the handful of metrics that actually run the business, with one agreed definition, one owner, and one system of record -- everything else references that, rather than recalculating it independently. This is deliberately positioned as the last deliverable of a diagnosis in our process, not the first request taken at face value: building a rationalized analytics foundation before you've agreed which numbers matter and how they're defined just gives you a faster way to disagree.

If your leadership team has ever spent a meeting debating whose number is right instead of what to do about it, that's usually not a data quality problem. It's a definition problem wearing a data quality costume.