What Is Data Analytics? The Four Types, with Examples
Data analytics turns raw data into answers. Learn the four types (descriptive, diagnostic, predictive, prescriptive) with simple business examples.
4 min read
Data governance is the set of roles, rules and processes that decide who is responsible for data, how it is defined, and how its quality, security and privacy are managed. Good governance means people can find the data they need, trust it, and use it legally. It is less about control and more about making data reliable enough to act on.
Why data governance matters
- Trust: everyone uses the same definition of “customer”, “revenue” or “active employee”.
- Quality: errors are caught and fixed at the source.
- Compliance: personal data is handled in line with the law, such as Qatar’s Personal Data Privacy Protection Law.
- Efficiency: less time spent reconciling reports.
- AI readiness: AI projects depend on clean, well-documented data.
Key roles
| Role | Responsibility |
|---|---|
| Data owner | A senior business leader accountable for a data domain (e.g. customer data) |
| Data steward | Day-to-day guardian of definitions and quality in that domain |
| Data custodian | Usually IT; manages storage, security and access technically |
| Data governance lead or council | Coordinates policies and resolves conflicts across domains |
| Data users | Follow the rules and report issues |
Core policies to have
- Data definitions: a business glossary for key terms and metrics.
- Data quality standards: what “good” means (complete, accurate, timely) and how it is measured.
- Access and security: who can see or change which data, and how access is approved.
- Privacy: how personal data is collected, used, retained and deleted.
- Retention: how long records are kept, and when they are archived or destroyed.
A simple 90-day starting plan
- Days 1–30: choose one important domain (often customer or product data), name an owner and a steward, and list the top data problems.
- Days 31–60: agree definitions for the 10–20 most-used terms, set basic quality checks, and document who can access what.
- Days 61–90: fix the most damaging quality issues at the source, publish the glossary, and report progress to leadership.
Then repeat for the next domain. Starting small and showing results works better than launching a large program on paper.
Signs your organization needs data governance
- Two departments report different numbers for the same metric.
- Nobody knows who can approve access to a dataset.
- Customer records are duplicated across systems.
- Staff export data to personal spreadsheets to “fix” it.
The six dimensions of data quality
| Dimension | Question | Example check |
|---|---|---|
| Accuracy | Does the data reflect reality? | Customer phone numbers verified at onboarding |
| Completeness | Are required fields filled? | Percentage of records missing an email |
| Consistency | Does it match across systems? | Same customer status in CRM and billing |
| Timeliness | Is it up to date? | Inventory updated daily, not weekly |
| Validity | Does it follow the rules and formats? | Dates and IDs in the correct format |
| Uniqueness | Is each item recorded once? | Duplicate customer records |
Pick the two or three dimensions that hurt most, measure them monthly, and publish the results to data owners.
Master data management in brief
Master data is the core shared data about customers, products, suppliers, employees and locations. Master data management (MDM) means keeping one trusted version of these records and sharing it across systems. You do not always need dedicated MDM software; for many organizations, clear ownership, matching rules and regular clean-ups of the main systems achieve most of the benefit.
Frequently asked questions
Is data governance an IT responsibility?
No. IT manages the technology, but business leaders must own the definitions and quality of their data.
Do we need special software?
Not to start. A shared glossary, clear roles and simple quality reports go a long way. Catalog and quality tools help as you scale.
How does governance relate to a data strategy?
Governance is one part of a data strategy: the strategy sets priorities, and governance makes sure the data behind them is reliable.
Prime Consulting helps organizations set up practical data governance. See our Data Analysis Solutions.