Quick answer
Data governance is the set of roles, rules and processes that decide who owns each data set, how it is defined, who can access it, how its quality is checked, and how long it is kept, so that data can be trusted and used safely.
What is data governance?
Data governance is the set of roles, rules and processes that decide how data is defined, who owns it, who can access it, how its quality is checked and how long it is kept. The goal is data people can trust and use without creating legal or security risk.
A useful test: if two people pull "active users" for last month and get different numbers, or nobody knows who added the signup_v2 event, governance is missing.
What it covers
| Area | The question it answers | Example for analytics data |
|---|---|---|
| Ownership | Who is responsible? | Each event has an owner and a developer |
| Definitions and quality | What does it mean, and is it correct? | A data dictionary and a tracking plan with required properties |
| Access | Who can see or change it? | Viewers read reports; only analysts edit definitions; exports are limited |
| Privacy and retention | Is personal data handled lawfully, and when is it deleted? | Personal fields are classified and masked; event rows are deleted after a set number of days |
| Documentation and change | How do changes happen? | New events go through proposed → developing → live |
Governance vs data management
Governance sets the rules and names the owners. Data management is the daily work that follows those rules: pipelines, storage, cleaning and backups. A useful shorthand: governance decides, management does.
A lightweight framework for analytics data
You do not need a data council to start. For a product or marketing team:
- Write a tracking plan. Every event, its properties, which are required, and who owns it.
- Name owners. One accountable person per area. A sample RACI:
| Task | Analytics lead | Developer | Marketing | Legal / DPO |
|---|---|---|---|---|
| Approve a new event | A | R | C | I |
| Mark a field as personal data | R | C | I | A |
| Change retention days | R | I | I | A |
| Grant export access | A | I | R | I |
(R = responsible, A = accountable, C = consulted, I = informed. Example only.)
- Classify personal data. Mark which fields directly or indirectly identify a person and keep them out of events where they are not needed.
- Set retention. Decide how long raw rows are kept; GDPR's storage limitation principle (Art. 5(1)(e)) asks for no longer than necessary.
- Review monthly. Unplanned events, missing required properties and stale definitions.
This guide is general information, not legal advice.
Governance controls in TapCub
- Tracking plan (All-platform analytics › Data › Tracking plan): each row has an owner, developer and status (proposed, developing, accepting, live, deprecated). Live verification shows hits in the last 24 hours and completeness of required properties.
- Event definitions: events that arrive without a definition show up as Unplanned; Blocked drops an event before it is stored.
- Property dictionary: classifies each key as a direct identifier, indirect identifier or non-personal, and masking applies to every role.
- Roles: owners, admins, analysts and viewers have different rights; for example, viewers cannot edit segments, and only owners and admins open the privacy center.
- Retention and requests: the privacy center sets retention days for event rows, autocapture, profiles and consent receipts, and handles data subject requests (access, export, restriction, erasure).
See also data observability.
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Solutions Engineer
Covers SDKs, API integrations, and data implementation, with an emphasis on validation, system interoperability, and troubleshooting.