Data Catalog
Discovery, ownership and technical metadata, and why catalogues go stale.
5 to work through
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intermediate
A company deploys a data catalogue. Six months later almost nobody uses it and most entries are stale. What went wrong, and what would make it work?
1 min answer -
intermediate
An organisation deploys a data catalogue and nobody uses it. What makes a catalogue useful rather than an inventory?
2 min answer -
intermediate
You bought a data catalogue eighteen months ago. It is populated and nobody uses it. What went wrong?
1 min answer -
advanced
A catalogue harvests column-level lineage by parsing the orchestrator's job definitions. A version upgrade changes one job format and the parser silently skips those jobs. The catalogue reports no error. What happens over the next three months?
3 min answer -
advanced
Salesforce's Force.com platform was described by Weissman and Bobrowski at SIGMOD 2009 as metadata-driven: many tenants' records share physical tables, with metadata rather than per-tenant schemas describing what each column holds. Your analytics estate sits on a platform of that shape. What breaks in a column-level data catalogue, and what replaces it?
2 min answer
3 terms in this topic
Data Catalogue
A searchable inventory of data assets with their schemas, owners, lineage, quality signals and usage.
metricLineage Coverage
The share of known data assets whose upstream and downstream edges were successfully derived on the latest harvest, which is the only signal that sep…
practiceMetadata Harvesting
Populating a catalogue automatically from the systems that hold the data, because anything requiring manual entry will be incomplete within a quarter.
Neighbouring topics
Data Governance & Semantics
General material on ownership, meaning, quality and control of data at enterprise scale.
Data Mesh
Domain ownership, data as a product, self-serve platform, and federated governance.
Data Products
A dataset with an owner, an interface, an SLO, and consumers who can rely on it.
Data Contracts
Producers committing to schema, semantics and freshness, and breaking builds when they do not.
Business Glossary
Agreeing what a term means before arguing about which number is right.
Semantic Layer
Metric definitions held once and served to every tool that asks.
Master Data Management
One authoritative record for a customer or product across systems that each have their own.
Reference Data
Code lists, hierarchies and currencies — small, shared, and quietly load-bearing.
Data Quality Dimensions
Completeness, accuracy, timeliness, consistency, validity and uniqueness as testable claims.
Data Observability
Freshness, volume, schema and distribution monitoring for pipelines that fail silently.
Data Stewardship
The operating model that makes ownership a role rather than a slide.
Data Access Models
Role, attribute and purpose-based access over analytical data, and how they compose.
Row & Column-Level Security
Restricting slices of a table rather than the whole table, and where it is enforced.
Tokenisation & Masking
Dynamic masking, deterministic tokens, and preserving joinability without exposure.
Retention & Purge
Deleting from an append-only estate, and proving the deletion happened.
Data Sharing & Clean Rooms
Collaborating on data neither party may hand over, with computation as the interface.
Sensitivity Labelling
Propagating a classification through joins and derived tables so controls follow the data.
BI Governance
Dashboard sprawl, certified reports, and the number the board is allowed to see.
Self-Service vs Governed
Letting analysts move fast without four teams reporting four different revenues.