Executed validation evidence
This source-backed view is rendered from the executed Python checks, 40 source rows, failed record output and retest log.
KPIs, Definitions & Data Quality
Data Quality Checks
Executed validation report showing which source rows failed, which outputs are affected, who owns the correction and what must be retested.
Data quality review overviewOpen image
A polished overview of the validation rules, failed records, affected outputs, owner action and reporting readiness.
More of the work
A closer look at what sits behind the overview.
This view shows the source records, checks and output used to prepare the overview above.
Quality gate
Failed checks made visible before the report is trusted.
Failed checks are easier to route, affected outputs are clearer and accepted limitations can be carried into the reporting notes.
Useful forReporting analystsData ownersFinance teamsOperations teams
Reports become unreliable when missing owners, duplicate records, stale source files, invalid periods and unmatched references are discovered during review meetings rather than before reporting.
Can this data be used in the report, what failed, how severe is it, who owns the fix and which output is affected?
A Python validation run with five rules, a 29-row failed record output, affected report routing, readiness decision and retest log.
How the work fits together
The check result leads to an owner decision and a recorded retest.
Each failed record shows the rule it broke, the affected report, the person responsible and the evidence needed before reporting can continue.
01Source
What the work starts with
- 40-row reporting extract
- 6-row active reference lookup
- 5-rule validation register
02Prepare
How it is prepared
- Executable Python validation script
- Owner, period, lookup, duplicate key and due date checks
- Failed record and retest output writers
03Check
Checks applied
- 18 records with missing owners
- 11 unmatched reference records
- Period, duplicate key and due date controls passed
04Output
What the review receives
- Executed validation report
- 29-row failed record table
- 5-rule retest log
05Handover
Notes for the next update
- Affected rows held from use
- Owner and data-owner corrections retained
- Retest route written with the executed output
- Python
- CSV source rows
- Reference lookup
- Validation outputs
- Validation rule design
- Failed record review
- Owner routing
- Reporting readiness
Failed checks are easier to route, affected outputs are clearer and accepted limitations can be carried into the reporting notes.
Built from a non-client example dataset. No protected data is used.
Before figures are used
Failed records are assigned and reviewed before the report is cleared.
Decide which checks matter before reporting, who owns failed records and how accepted limitations are carried forward.
- 01
Checks are grouped by category, severity and affected output before the report is trusted.
- 02
Failed records carry an owner, next action and retest result so fixes can be reviewed before the meeting.
- 03
Accepted limitations and reports that remain on hold are included in the handover note.
Quality results
What failed, which report is affected and whether the record passed its retest.
Owner, period, reference, duplicate key and due date rules were run against the source rows.
Eighteen missing owners and eleven unmatched references still need correction.
Affected rows stay out of use while passing controls remain available for the review.
Checks and boundaries
The validation rules, source records and limits covered here.
What is included
- Executed Python checks
- Validation rule results
- Failed record output
- Affected reports
- Owner action
- Readiness decision
- Retest state
What it can start from
- 40-row reporting extract
- Reference lookup
- Validation rule register
- Failed record output
- Retest log
What this example does not claim
- The evidence covers 40 rows and two intentional failure types rather than production volume.
- Validation rules, severity and accepted limitations would require agreement with source owners.
- The report shows one executed run and is not a live monitoring service.
Definitions pack overview
KPI Dictionary and Reporting Definitions Pack
- What is difficult now
- Reports lose trust when a shared KPI name hides different calculations, unclear source fields, assumptions or ownership.
- What the work helps decide
- Which KPI definitions are agreed, which need review and which downstream reports move when source fields or calculation rules change?
- KPI dictionary
- Reporting definitions
- Source mapping
Metric reconciliation workbook
Metric Reconciliation Pack
- What is difficult now
- Review time is lost when the same measure uses different formulas, grains or source fields across recurring reports.
- What the work helps decide
- Which version of the metric should be used, why do reports differ, what sits outside tolerance and who signs off the agreed definition?
- Metric reconciliation
- Formula comparison
- Source confidence
Data issue triage register
Data Issue Triage Register
- What is difficult now
- Data issues become hard to resolve when intake notes, severity, impacted reports, source owners, fix status and retest outcomes sit in separate places.
- What the work helps decide
- Which issue blocks a report, who owns the source fix, what has been retested and what limitation needs to be carried into review?
- Data issue triage
- Impacted report
- Retest status
Next step
Put failed checks into an owned review route.
Start with the measures people dispute or the failed checks that need clearer ownership before reporting.