Sort Data Quality Risks With FAIR
Categorize data readiness issues using FAIR principles before starting an AI data science project.
Sort each issue into the FAIR readiness category it most directly violates. Findable Accessible Interoperable Reusable No one knows whether the warehouse or CRM export is the canonical source The dataset can only be downloaded manually by one analyst Customer IDs use different formats across billing and support systems The meaning of churn_date changed after a pricing migration The source has no data owner in the catalog The model pipeline cannot reach the table in the approved production environment Region codes are free text in one system and numeric in another No provenance note explains which rows were excluded from…
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