You open a report in a meeting. A colleague brings up a spreadsheet with a different number. Someone asks whether canceled orders were excluded, someone else asks about VAT, and eventually you discover that each person is comparing a slightly different period. You came to decide whether to hire another person. Now you’re trying to work out whose spreadsheet to trust.
A better-looking chart won’t help here. You need to know what the numbers mean and whether they’re reliable enough for this particular decision. A new tool may make some of the work easier, but it won’t resolve a disagreement about what you’re counting.
Data quality also depends on what you want to use the data for. An order summary may be enough to plan next week’s workload. To assess customer profitability, though, it may be missing costs, discounts, or returns. The same data can answer one question well and fall short on another.
Choose one decision you need to make
You don’t have to examine every database in the company. Start with something specific: do we need more people, should we change the price of a project, or should we order more stock? Pick a number that matters to that decision and walk it through the following six checks.
Suppose you want to know which projects are profitable. Revenue gives you part of the answer. You also need to know which costs belong in the calculation, whether they’ve all been recorded, and how they’re allocated across projects. Those questions deserve an answer before you start comparing margins.
1. Does everyone mean the same thing by this number?
Ask two people to explain an important metric. When does an order count: when it’s accepted, delivered, or paid for? And who counts as an active customer: someone with a valid contract, or someone who actually bought something in the past three months?
Both answers may make sense. But mix them in one report and you’ll be comparing different things. Write down what’s included, what’s excluded, the time period, and the source. Name someone who can explain the definition and help resolve changes to it. A few sentences and one disputed example are often enough.
2. Can you trace the number back to its source?
Take one value from the report and try to reach the original records. Which system did it come from? What was added up, recalculated, or excluded along the way? Will a later correction to an order also appear in the report?
The CEO doesn’t need to read SQL. But someone in the company should be able to explain the calculation and demonstrate it with a few specific records. When you find an error, you’ll have somewhere to start and a better chance of understanding which other results it affects.
3. Is the data current enough for this decision?
Yesterday’s figures are fine for some decisions and too late for others. For a monthly profitability review, you want a closed accounting period. To promise a delivery date, you need to know what’s in stock now. How current the report needs to be depends on what you use it for.
Agree on an acceptable delay and display the time of the last successful data update. The time someone opened the page isn’t enough. If the latest load failed, the person needs to know before making a promise to a customer based on old information.
4. What’s missing from the total?
A total can look reasonable even when some inputs are missing. A margin report might include all revenue while leaving out costs that couldn’t be assigned to a project. The result then looks better than reality.
Alongside the total, look at excluded or unmatched records. Check missing currencies, duplicates, and unusual values. A negative quantity might be an error, or it might be a correctly recorded return. You need to know which.
Also distinguish zero from a missing value or a failed data load. Zero means something different from “we don’t know.” If the report displays them the same way, it hides uncertainty that may matter.
5. Does someone know how to fix an error?
Finding a discrepancy is a start. Who decides which value is correct? Is the error in the order, the calculation, or just the display? And who will finish the correction and check it?
If someone fixes the number only in a slide for leadership, the next report may bring back the same problem. Fix the cause where it lives: sometimes in the source system, sometimes in the calculation rules. Record what changed and why. If you need a temporary manual adjustment, label it and agree on who will address the underlying cause.
6. What will you do differently because of the report?
Now return to the original question. Does the number help you decide whether to hire someone or change a price? Do you know when a difference calls for action and when you need to investigate first?
You don’t need an automatic rule for every situation. But it should be clear who will look at the result and what happens next. If all that happens each month is another chart being added to a presentation, ask who still uses it and why.
The answer may not be a new system
These checks may show that you only need to agree on a revenue definition, add a missing cost, or put someone in charge of monitoring updates. You may also find that the current setup is sufficient for the decision at hand. That’s a useful result too.
An initial audit should show which numbers you can rely on, where you need caution, and what’s worth fixing first. Buying a platform is a possible next step. On the data audit for owners and leadership teams page, I explain how I approach this kind of independent review.
What to bring to your next meeting
Choose one report you regularly use to make decisions. Instead of going through every chart, ask these six questions about its most important number:
- Do we all understand what this number means?
- Can we trace it to the original records and explain the calculation?
- Is the information current enough for our decision?
- Do we know what’s missing from the result and why?
- Is it clear who will fix and verify an error?
- Do we know what we’ll do based on the result?
If an answer gives you pause, you have a specific place to start. You don’t have to stop making decisions or rebuild your reporting. First, establish whether the uncertainty could change the decision you’re working on.
For the broader context, see Why data teams need business context when adopting AI agents. The same question comes up with AI: do we understand the inputs well enough to rely on the result?
Further reading: Open Data & Metadata Quality covers accuracy, completeness, consistency, timeliness, and fitness for a particular use. The six checks above are a practical approach to business decisions, not a certification test.