Data-Quality Checks Example
Python-generated checks report for making missing values, duplicate records, reconciliation differences, refresh timing and review status visible before figures are discussed.
Demonstration work only. Built with synthetic data. Not a client notebook, client system or Power BI screenshot, and no protected data is used.
- Environment
- Python / Data-quality checks
- Preview shown
- Python script output
- Demo status
- Built artefact
- Artefact source
- demos/python-data-quality-profiling/
What this example shows
Who the artefact is for and what review question it is designed to answer.
Audience
Current setup
Figures are prepared from source exports, but missing values, duplicate records, reconciliation differences and refresh timing are not visible before the review meeting.
Review question
Which source records, fields or exceptions need checking before the figures are discussed?
What a client could receive
A finished Python-generated checks report showing source record counts, field profile, missing values, duplicate IDs, reconciliation differences, stale refresh checks, exception rows and review status.
How this could be used
A real build would be agreed around your data, users, refresh needs and review questions.
Pages, filters and measures would be shaped around the data sources, refresh rhythm and audience for the final output.
The first review would agree which figures need checking, which questions the output must answer and what maintenance notes are needed.
The finished artefact can stay focused for leadership review or expand into a multi-page model where that is useful.
Questions it helps answer
Practical questions this pattern could help a team discuss.
Missing values
Flagged
Shows where required fields are absent or incomplete
Unmatched records
Reviewed
Highlights records that fail source matching or reconciliation
Review status
Tracked
Separates completed checks from items still needing attention
Data and delivery notes
The example shows the artefact without exposing real business data.
What gets reviewed
- Field profile
- Missing-field review
- Duplicate review
- Refresh status
- Reconciliation points
- Exception summary
- Review status
Data sources
Next step
Discuss data-quality review
Quanta Meridian can review the source data, report structure, measures, checking points, documentation and whether Power BI, Excel or process improvement is the right route.