Two reports, two revenue figures. Sales works from one, finance from the other, and the meeting ends up debating which version is correct instead of the decision on the table. That is usually how inconsistent data comes to light: not as a technical question, but as a discussion around the table.
Steering by figures that contradict each other is riskier than steering by figures that are visibly missing. An empty cell warns you; two tidy but different numbers do not. The margin of error stays hidden until a decision goes wrong.
The same information lives in more than one place
Inconsistencies appear as soon as the same data is kept in more than one system without those systems knowing about each other. The CRM stores customer details, the accounting package holds the invoices, and somewhere a spreadsheet still gets updated by hand alongside someone’s actual job. Three places, three versions of reality.
Manual entry does the rest. People spell names slightly differently, update an address in one place, or edit a list that has already changed somewhere else. The more data gets transferred between systems by hand, the faster the versions drift apart. Growth does not shrink this issue, it makes it bigger.
The damage does not stop at one wrong report
As long as nobody is steering by the numbers, inconsistent data is a cosmetic flaw. Once decisions rest on it, it becomes a business risk:
- Budgets and investments get allocated based on reports that do not reflect reality.
- Customers get contradictory answers or orders processed incorrectly, because staff each draw on a different system.
- Reporting to an accountant or a regulator rests on figures you cannot actually back up.
- Teams stop trusting each other’s numbers, so every meeting starts with the question of which version is correct.
That last risk is often underestimated. An organisation where the figures are constantly disputed makes decisions more slowly and cautiously than necessary, or falls back on gut feeling because the data cannot be trusted anyway.
Both versions look equally reliable
What makes inconsistent data treacherous is that no error message ever appears. Every version looks complete and plausible; only someone who lays two sources side by side notices they differ. Meanwhile, small discrepancies get corrected by hand by whoever happens to spot them, so the underlying cause stays out of sight. A forecast built on the wrong figures only falls apart months later, by which point the source of the error is barely traceable.
A dashboard makes it visible, not better
The usual instinct is a BI dashboard: put every figure in one overview and the truth will surface on its own. That kind of dashboard has value, but it only shows what is already in your systems. If that data is inconsistent, all you get is a sharper view of the contradiction. Good dashboards and reports therefore start with the underlying data flows, so that what you see is actually correct.
The fix starts at the source
Structurally, you solve this by letting systems talk to each other directly. Map out which systems create, edit and use which data. Agree, per data type, which system is the leading one, and replace manual exports with integrations that synchronise automatically. Validate input at the source and you no longer need to hunt for errors further down the line.
Recognise the recurring discussion about which figures are correct? Start by looking at the places where information gets retyped: that is where the versions start to drift. For the structural work, from API integrations to a single reliable source for the whole organisation, our data integrations page explains how we approach it. The first conversation is about your systems landscape, not a quote.