Category: Data Governance
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Data Quality Is a Governance Outcome
Persistent data-quality problems often begin upstream. Strong governance connects ownership, definitions, controls, processes, thresholds, and accountability to produce trustworthy data.
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Artificial Intelligence, Cybersecurity, Data Governance, Enterprise Governance, Information Architecture
The Data Nobody Owns
Some enterprise data remains actively used long after its ownership and governance disappear. Orphaned data creates hidden operational, security, regulatory, and AI risks.
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Data Ownership Without Decision Rights Is an Illusion
Assigning a data owner does not create accountability if that person cannot make meaningful decisions. Real ownership requires authority, defined boundaries, risk acceptance, and evidence.
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The Data Accountability Gap
Enterprise data responsibilities are widely distributed, but accountability often is not. The Data Accountability Gap emerges when everyone uses the data yet no one clearly owns its meaning, risk, decisions, and consequences.
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Data Governance Is No Longer a Data Problem
Data governance has moved beyond databases, quality rules, and catalogs. As data increasingly drives AI and consequential business decisions, governance must establish accountability, authority, provenance, trust, and evidence across the enterprise.
