Data Governance, Classification & Records Retention
Knows what data you hold and when to delete it.
Overview
Retention is the compliance obligation nobody wants to own: keep records long enough to satisfy the regulator and short enough to satisfy the privacy regime, then prove you did both. That requires knowing what data exists, classifying it, applying a retention schedule, holding it against litigation when required, and defensibly disposing of it. Data catalogue and governance platforms handle the discovery and stewardship layer; records-management and privacy tools handle the schedule and disposition. Both matter, and organisations routinely buy one and assume it covers the other.
Becomes pressing once you hold regulated personal data across multiple systems and jurisdictions.
Advantages
- Defensible disposal shrinks both breach blast radius and discovery cost
- A catalogue with lineage answers "where did this personal data come from" without a project
- Retention schedules applied by policy beat schedules that exist only as a document
- Classification drives DLP, access control and privacy workflows from one decision
- Legal hold that actually suspends deletion is a hard requirement in litigation, and easy to get wrong
Trade-offs
- Catalogue projects are notorious for stalling — the tool lands, the stewardship never does
- Automated classification produces false positives that erode trust in the whole layer
- Retention rules conflict across jurisdictions and someone must adjudicate, not the software
- Expensive, and the value is deferred and indirect
- Coverage gaps in unstructured data and shadow SaaS undermine the whole exercise
Open-source option
OpenMetadata and DataHub are both credible self-hosted data catalogue and lineage platforms with active communities, and Apache Atlas remains the Hadoop-ecosystem incumbent. They cover discovery, cataloguing and stewardship well. None of them implements a records-retention schedule, legal hold, or defensible disposition — that side stays commercial or bespoke.