Term
Data governance
The rules and responsibilities for data in a company: who owns which data, how it is defined, how its quality is checked, who can access it and how long it is kept. Without it, reports from different systems contradict each other and cannot be trusted.
Updated
What it means
Data governance is the set of rules, roles and processes that determine how a company handles its data. It answers who is responsible for which data, how data is defined, where its single source of truth is, who may change and read it and how long it is kept.
A typical output is a data dictionary with shared definitions: what counts as an “active customer”, “revenue” or an “order” and which system the figure comes from. Without it, sales, accounting and management each have their own numbers, and meetings revolve around which are right instead of what to do with them.
It also covers data quality, meaning rules for entering master data, checks for duplicates and missing fields and responsibility for fixing them, and protection: classification by sensitivity, access rights and GDPR compliance for personal data.
How it works in practice
For accounting, data governance is very practical. If the same supplier has three records with different company IDs, if cost centres are named inconsistently or if sales track discounts outside the system, reporting and business intelligence show wrong figures even though each entry is correct in itself.
A smaller company does not need elaborate structures. It is enough to appoint owners for the main data areas, for example sales for customers, purchasing for suppliers and accounting for accounting codes, write down the basic definitions and clean master data regularly. When introducing a new system or a data warehouse, this is a condition of success.
The rules are most useful during data migration from an old system, which reveals how many duplicates and nonsense entries have piled up over the years. We recommend using the migration to clean up and agreeing rules so the mess does not return in the new system.
What to watch out for
The most common mistake is treating data governance as an IT project. Technology helps, but definitions and responsibilities must be decided by people from the business. When IT writes the rules alone, nobody follows them, because nobody feels they own them.
The opposite mistake is bureaucracy: committees, approvals and documents that only slow down work with data. The goal is not rules for their own sake but figures management trusts and can decide on. Rules are best introduced step by step where data errors hurt most.
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