Practise vocabulary for data governance frameworks, governance committees, RACI for data, federated governance models, data mesh governance, data policies, and standards.
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A data governance framework defines:
Data governance frameworks (DAMA-DMBOK, IBM Data Governance Council model) define: who owns data, who can access it, how quality is measured, how policies are enforced. Without a framework, governance is ad-hoc and inconsistent — different teams apply different standards to the same data.
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A data governance committee typically does:
Data governance committees (also called Data Councils or Data Governance Boards) are the decision-making body for data policies. They handle escalations: "Which team owns the definition of 'customer'?", "Should we allow external access to this dataset?", "What is our retention policy for clickstream data?"
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A RACI matrix for data governance assigns:
RACI for data example: for "approving access to PII dataset" — R: data steward (does the review), A: data owner (accountable for the decision), C: legal (consulted on compliance), I: IT security (informed of the decision). RACI prevents "everyone assumes someone else is handling it."
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A "federated governance model" in a data mesh context means:
Federated governance balances central control with domain autonomy: the central platform team sets global policies (PII classification standards, retention policies, naming conventions), but each domain team applies and enforces them for their own data products. This avoids both the bottleneck of centralised governance and the chaos of no governance.
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A "data standard" in governance communication refers to:
Data standards prevent the chaos of inconsistency: if one team stores dates as DD/MM/YYYY and another as YYYY-MM-DD, joins fail silently. Standards must be documented, enforced (via linting, catalog validation), and socialised. Common standards: naming conventions, data type standards, timezone standards, classification taxonomies.