Key takeaways
- Data quality is a process, not a one-time cleanup.
- Every dataset needs an owner and a definition.
- Check completeness, uniqueness, validity, and reconciliation.
- Agree on what happens when a check fails.
Ownership
Every important dataset should have an owner: the person who can confirm what it means and who is responsible for fixing problems. Without ownership, data issues bounce between teams.
For a small business, the owner may be the finance manager, operations lead, or founder. The role matters more than the title.
Completeness
Check that the expected rows and periods are present. A missing week or a missing store can make a trend look better or worse than it is.
Compare row counts and date ranges with the source. Add a check that fails when a required period is missing.
Uniqueness
Check for duplicate invoices, customers, orders, or product records. Duplicates inflate totals and distort averages.
Use a stable key and document how duplicates should be resolved. Do not rely on manual deletion without a record.
Validity
Check that values fall within expected ranges. Negative quantities, impossible dates, unknown categories, and blank mandatory fields are common problems.
A validation rule can be simple: quantity cannot be negative, a status must be in an approved list, or a date cannot be in the future where it does not make sense.
Reconciliation
Reconcile the dataset with a trusted source before building the report. For finance, that may be the ledger or bank. For ecommerce, it may be the platform’s sales total.
Document the expected difference, if any, and explain it. An unexplained difference is a risk.
Failure handling
Decide what happens when a check fails: stop the refresh, flag the report, or notify the owner. The process should not silently publish bad data.
A simple data quality log is enough for most SMBs. It creates a history and makes recurring issues visible.
Frequently asked questions
Do I need a data governance team?+
No. A small business can start with named owners, simple definitions, and a recurring quality check.
What is the most important data quality check?+
Reconciliation. If the totals do not match a trusted source, the rest of the report is difficult to trust.
How often should checks run?+
Run automated checks on every refresh. Review the quality log monthly and investigate recurring failures.
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