Know what the report has checked
Data validation compares records with agreed rules before they are used. For a business report, those rules might require a customer number, a real date within the reporting period and quantities that make sense together. IBM's overview describes these checks for presence, dates, ranges and consistency.
Agree what happens when a rule fails. A missing customer number can stop a record until it is corrected. An unusually large order may be genuine: flag it for the order owner to confirm. A warning needs a decision; it is not a reason to replace an unusual value with an average.
Check missing values explicitly, including empty fields. A quantity that is missing is not zero. Keep the original input and record the reason for each correction so colleagues can follow the approved figure back to its source.
Follow five orders through one review
Synthetic example, not client results: a distributor wants a September report showing units still to dispatch. Five orders cover one product, with no returns or cancellations. The source is assumed complete, with each order included once. The calculation is ordered units minus dispatched units.
One order record passes. Three are held: one lacks a customer number, one says 30 February, and one shows 7 units dispatched against 6 ordered. A fifth order requests 100 units, above this example's review threshold of 50. Its record waits for confirmation, not an automatic correction.
The owners check the source documents, supply the missing number, correct the date and confirm that the quantity recorded as 7 should be 4. They also confirm the 100-unit order is genuine. After the checks are rerun and the reason for accepting the large order is recorded, the five orders show 96 units still to dispatch: 129 ordered minus 33 dispatched.
Until those reviews finish, the report stays on hold. The passing order alone is not a complete total. The worked rule catalogue and exception log show every input, decision and calculation. Save the example and planning template for your own reporting discussion.
Make the next review easier to trust
Start with one report and the checks that matter to its readers. Try records that should pass, records that should fail and records with missing values before setting the checks to run regularly. Name the person who corrects each source and who decides whether a warning is acceptable. If a required value cannot be confirmed, keep the affected report on hold.
Passing rules does not prove every fact is true. A date can exist on the calendar but still be wrong for this order. You still need to confirm that all expected records arrived, that they were linked to the right orders and that the report owner has reviewed the result. Once the checks are agreed, a recurring reporting routine can bring the checked version to the team.
Datimore can help build data checks into your reporting routine and clarify who reviews flagged records. Our cold-chain review case shows evidence and exceptions brought together; it does not prove this order example. Bring one recurring report and a few approved sample records. The aim is a review where the team can follow the figures and knows who is resolving the remaining questions.