An automated report is not working merely because a file appears on schedule. A report can arrive on time while using stale inputs, dropping records, applying the wrong filter, or exposing data to the wrong audience. Measure the whole chain from source data to recipient action, and define the expected result before you evaluate the automation.
Write a small acceptance contract
Start with a plain-language contract: what question the report answers, which source systems it uses, the coverage period, required fields, calculation rules, delivery deadline, intended recipients, and acceptable error rate. Include at least one known input with a known output. This turns "looks right" into a repeatable test. If the report is used for consequential decisions, name the person who reviews exceptions instead of assuming automation removes human responsibility.
Test a sample back to the source
The U.S. Government Accountability Office's data reliability guidance describes several practical assessment methods: electronic testing, reviewing information about the system and data, interviewing knowledgeable people, and tracing a sample of records to source documents. For an automated report, select records from normal cases and edge cases, reproduce the calculations, and confirm that inclusions and exclusions follow the written rules. Record any limitation that makes the data fit for one purpose but not another.
Track completeness and freshness
Count expected records at the source and compare them with the report after every run. Monitor the newest source timestamp, the extraction time, and the report period so a successful delivery cannot hide stale data. Useful measures include missing-record rate, duplicate rate, null rate in required fields, calculation mismatch rate, and minutes from source availability to delivery. Set thresholds from the decision the report supports rather than choosing a convenient number after failures occur.
Make delivery and failure observable
The OWASP Logging Cheat Sheet recommends recording application events with enough context for security and operational review while avoiding sensitive data that should not be logged. Give each run an identifier, log its start and end, source version, row counts, validation result, destination, and error state. Alert on a missed run, partial output, failed validation, or unauthorized access. Do not log passwords, access tokens, or unnecessary personal data just to make troubleshooting easier.
Review access and outcomes
Test the report as an intended recipient and as a user who should be denied. Confirm links expire or permissions are removed when roles change. Then measure whether recipients can make the intended decision: time to find the answer, number of manual corrections, reopened issues, and unresolved exceptions. A technically perfect report that nobody understands is not working as intended.
Recheck after every material change
Rerun the acceptance tests after a source schema change, filter change, new recipient group, time-zone change, or migration. Keep a small baseline dataset and expected output in version control or another controlled repository. That makes regressions visible and gives future owners a concrete definition of success.
Sources
- Assessing Data Reliability — U.S. Government Accountability Office
- Logging Cheat Sheet — OWASP Foundation