Data and Insight
Governance, quality and a single source of truth.
Start here · Essential guide
What centralized data management means in practice: warehouses, lakes and master data, what a shared view of data costs, and when a looser model fits better.
How one agreed set of numbers can end arguments over conflicting spreadsheets, and why central data is only as reliable as the inputs that feed it.
Duplicates, missing values, stale records and inconsistent formats: what each looks like in real data and the simple checks that help you catch them early.
General principles for deciding how long business records are kept, how a records schedule is built and maintained, and why legal advice matters for the details.
Who looks after data day to day, how data stewards differ from data owners, and how to set up stewardship roles without creating a bureaucracy.
What master data is, why duplicate customer and product records arise, and how a shared reference record is created and maintained, with realistic limits.
How centralized document management handles unstructured content: file storage, version control, permissions, search and retention, plus the costs and trade-offs.
What data governance covers: ownership, stewardship, shared definitions, access policies and privacy duties, with a balanced view of central versus local control.
How to get data to decision-makers quickly: who gets access, how fast, and how to avoid bottlenecks where every request waits on a central data team.
How central rules for accuracy, completeness, deduplication, validation and monitoring work, and with illustrations of how bad data spreads through shared systems.
Related topics