A data quality checklist before starting a BI project
The checks that prevent a dashboard from failing after launch: ownership, definitions, completeness, duplicates, and reconciliation.
Topic hub
Reliable reporting starts with data that can be traced, cleaned, and queried. These guides cover SQL patterns, Python and pandas, data cleaning, quality checks, spreadsheet-to-database decisions, and building reporting pipelines. Use them to make the data behind a report defensible.
Start here
Three recent starting points. The full list is below.
The checks that prevent a dashboard from failing after launch: ownership, definitions, completeness, duplicates, and reconciliation.
The signals that a spreadsheet has become a database, and the simplest path to a more reliable system.
How to calculate running totals with SUM OVER, choose the right window frame, reset by partition, and check ties and gaps.
Every guide is listed by publication date. Use the titles to find the question closest to the work in front of you.
Next step
Tell us what is slow, unclear, or unreliable. We will reply with the right service, a realistic scope, and a clear price.