A well-presented chart can hide incomplete or misclassified data. The quality of an analysis also depends on the information that feeds it: before looking for an explanation in the results, it is necessary to verify how those results were built.
In a warehouse, the same product registered with different codes may appear as multiple items. In a customer database, duplicates can alter the number of people reached. These problems affect purchases and communication even when the software performs the calculations correctly.
A control proposal starts with common definitions. What counts as an active customer? When is an order considered completed? What costs enter the margin used in the report? Responses should be accessible to those who input data and those who interpret it.
Responsibility and updating must also be established. An archive can be precise at the time of creation and become unreliable if no one corrects changes and anomalies. Checks on missing fields, duplicates, and inconsistent values help identify problems before they arrive in reports.
Data cleanup must keep track of the relevant changes. Correcting without knowing why it makes it difficult to reconstruct previous comparisons and decisions. For a small business, a limited set of well-maintained data can be more useful than a very large archive that no one can interpret. The aim is to make explicit the quality of the information on which it is decided.