AI workflows for data engineering and data quality.
Keeping the data the business reports from trustworthy: data models, sources, quality alerts and tables nobody uses any more.
Who does this work
- Data engineer
- Analytics engineer
- Data platform manager
Updated
Examples of the workflows identified
- Find tables ready for retirementIllustrative
- Review the downstream impact of a data model changeIllustrative
- Trace a data-quality alert to its causeIllustrative
- Validate a revised data modelIllustrative
- Write the source guide for a data domainIllustrative
The workflows identified
Marked illustrative: an example of the work, identified, with no workflow template yet.
Find tables ready for retirementA candidate list of warehouse tables with usage, lineage and retention questions.Illustrative · not built yet
Review the downstream impact of a data model changeAn impact review listing the reports and users that depend on a column or table, and what must be validated.Illustrative · not built yet
Trace a data-quality alert to its causeAn investigation brief with affected reports, lineage and candidate causes for the data team.Illustrative · not built yet
Validate a revised data modelA query set comparing row counts, joins and changed business logic between the old and revised model.Illustrative · not built yet
Write the source guide for a data domainA guide to a domain's tables: grain, joins, definitions and known traps, captured from the analyst who knows them.Illustrative · not built yet
Workflow templates
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Where to go next
All of business intelligenceThe broader area this belongs to, with its workflows.All of analytics and reportingEvery area of the function, with workflow templates and examples.Map your team in the PlannerPut this work in your plan, with the roles that do it.
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