Glossary · The six pillars
Data governance
Data governance is whether someone owns each record and field, keeps it current and can show what changed: owners, lifecycle, freshness and audit trails.
52 active accounts owned by people who have left.
In plain terms
Governance asks who is responsible for this data, and is anyone looking after it? It carries 18% of the Truth Score™.
Most governance assessments read policy documents. We read the records. A policy that says every account has an owner is worth less than an export that shows how many active accounts are owned by people who have already left the company, which we find by comparing the CRM with HR records.
The pillar looks at ownership (does each record have a current, real owner), freshness (how many records haven’t been touched in years but still count as active), lifecycle (are statuses tracked, or is everything “active” forever) and audit trails (can you see who changed what, and when).
Governance is what keeps a good Truth Score™ from slipping back. A clean-up without owners decays; the same duplicates and stale records return within months. That is why governance fixes sit at the end of most remediation roadmaps: they hold the gains.
- See it in a real memoThe sample Truth Score ReadReal engine output on generated test data, not a client
- How we measure itThe method, including the capPillars, weights, bands and what is never guessed
- What happens to your exportsSecurity and data handlingExports only, NDA and data processing agreement first
Questions
Data governance, answered.
The questions people ask about data governance, answered in plain English.
Book the callIs data governance a compliance exercise?
Partly, but for AI it is practical: an agent acting on accounts owned by people who have left will send the wrong messages to the wrong customers. We measure governance in the records, not in the policy.
How do you find owners who have left?
By comparing the owner field in the CRM, or any system with owners, against HR and payroll records of leavers. It is one of the gaps that only shows up between systems.
Do you review our governance policies?
In an AI Investment Review we can read them alongside the records. The score itself is based on what the records show.
Related terms
Keep reading.
- AI readiness Data quality Data quality is how far data is fit for the job it is about to do: correct, filled in, consistent, owned, connected across systems and traceable to its source. Read the definition
- The six pillars Data lineage Data lineage is whether a number can be traced back to where it came from: which system, which records and which changes produced it. Read the definition
- Method Remediation roadmap A remediation roadmap is a prioritised list of data fixes, each with an owner, an effort estimate and a “done when” test, written to hand to your own team or any outside firm. Read the definition
- The Truth Score Truth Score™ The Truth Score™ is DataTruth’s score out of 100 for how far a company’s data can be trusted, built from six weighted pillars and placed in one of four bands, A to D. Read the definition
See how your data scores on governance. Measured, not self-reported.
Exports from two or more systems, matched between them, every red flag checked by hand. A signed memo with your Truth Score and a Leverability read, within 5 working days of complete exports.
Starts with a 30-minute call. Commits you to nothing.