How to know if your data is reliable (without being technical)

Five signs any manager can spot without opening a database. If two or more sound familiar, your decisions are at risk.

At some point in your company, this happened: someone presents a number, someone else says “that doesn’t look right,” and suddenly the meeting stops being about the decision and becomes a debate about the data.

That’s not a technical problem. It’s a business problem. And it has clear signs that anyone can spot — no SQL knowledge required.

Why it matters more than it seems

When data isn’t reliable, decisions still get made — but on gut feeling dressed up as analysis. The report is there, the number is there, but deep down nobody fully trusts it.

The cost isn’t just the time wasted arguing about data instead of deciding. It’s that the important decisions — which product to push, where to cut costs, which client to prioritize — get made on information that might be wrong.

The five signs

1. Two teams have the same metric and get different numbers

Finance says the month’s margin was X. Sales says it was Y. Both exported from the system, both ran their calculations, and arrived at different results.

This happens when there’s no single source of truth. Each team built its own version of the data — with its own filters, formulas, and criteria for what to include. The result is two “official versions” of the same number, and neither is definitively correct.

2. Nobody knows exactly where the report everyone uses comes from

The Monday sales report. The monthly close. The dashboard the board reviews every week. Does anyone know exactly what data it uses, which system it pulls from, what calculations it applies?

If the answer is “someone set it up two years ago” or “it’s a spreadsheet that downloads from the ERP and then has some formulas applied,” that report is a black box. When something changes in the source system, the report can become outdated without anyone noticing.

3. Before an important decision, someone asks to “verify the number”

There’s a board meeting. A decision needs to be made — open a new location, adjust pricing, renew a contract. And someone says: “before we present this, can we verify the number is right?”

That manual verification — calling someone, cross-referencing another spreadsheet, “double-checking” — is the clearest sign that the system doesn’t generate trust on its own. Reliable data doesn’t need verification before every important use. It arrives with the validation process already built in.

4. Closings change after they’re closed

The March close came in at $X. Two weeks later, someone finds an adjustment, a missing invoice, a return that wasn’t captured. The close becomes $Y.

A closing that changes is a closing that didn’t have complete data when it was closed. This isn’t necessarily anyone’s fault — data from some systems might arrive late. But if it happens regularly, there’s a structural problem in how information gets consolidated.

5. There’s one person who “knows how the spreadsheet works”

Almost every mid-sized company has one. The person who built the model, who knows which tab touches which formula, who has to be present when the monthly report gets updated because otherwise “it breaks.”

When knowledge about how data works lives in a person rather than in the system, the reliability of the data depends on that person being available, not leaving the company, not changing anything without warning.

What each sign implies

None of these signs reflect a moral failing or indicate that anyone did their job poorly. They’re natural consequences of companies that grew without their data growing with them.

What they do imply is that the decisions made with that data carry an unquantified margin of error. You don’t know how much might be wrong — you just know it could be.

Where to start

The first step isn’t implementing anything. It’s understanding exactly what data you have, where it comes from, what state it’s in, and where the failure points are.

With that clarity, you can build infrastructure where data arrives validated, where there’s one version of every number, and where every change is tracked.

If two or more of these signs sound familiar, schedule a call. In 30 minutes we’ll tell you how deep the problem goes and what the first concrete step would be.