Data Ownership & Stewardship (RACI)
Who's accountable for each dataset vs who does the day-to-day work. Clear roles kill the 'whose problem is this?' paralysis.
Every dataset needs a (accountable business figure, usually a director+) and a (the person who does the daily work). Without this duo, when something breaks, everyone looks at each other and nothing gets fixed.
Analogy: a restaurant. The **Owner** is the legal owner — they get the license, they sign the checks, they're liable. The **Steward** is the chef — they cook every day, know the recipes, handle incidents. The **Custodian** is the supplier — they deliver raw ingredients on time.
framework splits responsibilities precisely: **R** (Responsible — does the work), **A** (Accountable — one person who signs off, ultimate buck-stop), **C** (Consulted — input before decisions), **I** (Informed — told after the fact). For each decision about a dataset, exactly ONE person is Accountable.
Typical setup per critical dataset: Data Owner (A) + Data Steward (R) + Data Engineer/Custodian (R, executes technical stuff) + Compliance/Legal (C for classification changes) + Consumers (I on schema changes).
Failure pattern without clear roles: 'the table owner is the team that created it' → team is reorganized → table orphaned → breaks 6 months later → nobody claims it → 2 days of Slack fights.
Grounded on https://www.dama.org/
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Privacy & Compliance (GDPR, CCPA, HIPAA)
The main data privacy regulations, what rights they grant to individuals, and what technical obligations they create for your systems.