What is Data Governance?
Data Governance is a framework of rules, processes and responsibilities for managing data as a strategic asset throughout its lifecycle – ensuring data quality, consistency and appropriate access across all systems.
Technical Definition: A framework of rules, processes and responsibilities for managing data as a strategic asset throughout its lifecycle – from creation to deletion. Technically this includes data definitions (what does a data point mean?), data quality standards, access rights and change management processes.
Why it matters: Without data governance, data quality degrades over time. Maintenance teams create ad-hoc data structures, naming conventions drift, and analytics platforms end up fed with incoherent, unusable data.
HubMind’s view: We establish data governance frameworks at the start of every digitalisation project. It is the invisible infrastructure that determines whether your data investment actually pays off over five years.
External link: DAMA International (Data Management Association).
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Example and trade-offs
If the same pump has three identities across operations, maintenance and finance, someone must decide which identity is authoritative, who may change it and how the change propagates. Data Governance makes those decisions visible. Too much central control slows delivery; too little creates local truths, weak traceability and expensive clean-up.
HubMind’s view
Our point: data governance is an organisation’s operational memory—not a policy folder. Why: quality is created in daily flows when the right person can change the right data and explain why. Recommendation: start with one critical information object, name an owner and steward, define quality measures, and build the rules into interfaces and workflows.
Related concepts: master data ownership, metadata, information modelling and API.
External reference: ISO 8000-1:2022 – data quality overview.
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