Assess dataset quality against meaningfulness traits
FPM-5 Order: #5 Frame the Problem Has Dependencies
Updated 1 month, 2 weeks ago
Guidance
For each selected catalog entry, check it against the traits of meaningful data: sufficient volume, historical depth, consistency over time, multivariate coverage, atomic-level (not pre-aggregated) records, clean values, clear coding, dimensional structure, rich segmentation, and known pedigree/lineage. Drop or flag entries that fail badly on a trait that matters for your questions.
Details
- Order:
- #5
- Phase:
- Predecessor:
- FPM-4 Select candidate datasets from the datalake catalog
- Successor:
- FPM-6 Write the Framing Brief
- Created:
- Sep 18, 2026
- Last Updated:
- Sep 18, 2026
Workflow
Frame the Problem
Produce a written set of driving questions, a chosen analysis type, and a vetted list of candidate datasets from the …
View WorkflowAssigned Agent
EDA Practitioner
The person or AI system running this playbook end to end — analyst, programmer, and …
Required Skills
- Data Quality Assessment Data Readiness
Rules
No rules linked.
Input Artifacts
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Output Artifacts
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