The problem
Most organisations have invested in a data catalogue, but the content inside it rarely matches the ambition. Tables and fields are listed, but descriptions are missing, owners are unclear, sensitivity tags are inconsistent, and lineage is partial. Data stewards are asked to fill the gaps manually, working through thousands of assets in spreadsheets and chasing colleagues for definitions that never quite arrive.
The result is a catalogue that technically exists but is not trusted. Analysts still ask the same questions in chat channels. Finance teams still rebuild the same definitions in their own models. Compliance still cannot evidence where personal or financial data lives. The catalogue becomes another system to maintain rather than a working control.
Why it matters
A weak catalogue has real consequences. Regulatory requests take longer because data location and ownership cannot be evidenced quickly. Reporting inconsistencies persist because teams interpret the same field differently. New joiners take longer to become productive because tribal knowledge is not written down. AI and analytics projects stall because the underlying data is not understood or trusted.
For leadership, this shows up as slow decisions, repeated rework and a growing gap between the data the business has and the data the business can actually use. For compliance and risk teams, it shows up as control gaps that are difficult to close at pace.
The opportunity
A data catalogue does not need to be enriched entirely by hand. With a combination of no-code automation, governed workflows and targeted AI, most of the heavy lifting can be done programmatically and then reviewed by the right people. Metadata can be pulled from source systems, draft descriptions can be generated from schema and sample data, sensitivity can be classified against policy, and owners can be proposed based on usage patterns.
The human role shifts from writing everything to reviewing, approving and refining. The catalogue becomes a living asset, maintained through a repeatable process rather than a one-off project.
Example workflow
1. Connect the source data
Connect to the data catalogue platform, source databases, warehouses, BI tools and identity systems. Pull metadata such as table names, column names, data types, row counts, last-updated timestamps, query usage and existing tags.
2. Standardise and prepare the data
Normalise object names, resolve aliases across systems, and build a single working register of assets that need enrichment. Flag which assets already have descriptions, owners, sensitivity tags and lineage, and which do not.
3. Apply business logic
For each gap, apply rules. Propose owners based on who queries or updates the asset most often. Propose domains based on schema, naming conventions and source system. Use AI to draft plain-English descriptions from column names, data types and sample values, grounded in existing glossary terms. Use AI to suggest sensitivity classifications against your data policy.
4. Run checks and controls
Validate that proposed owners are active employees in the correct function. Check that suggested classifications align with policy rules, for example that any field containing personal identifiers is tagged appropriately. Flag low-confidence suggestions for mandatory human review. Keep a full audit trail of what was proposed, by which model or rule, and on what evidence.
5. Produce outputs
Push proposed enrichments into the catalogue as draft entries. Generate review packs for data stewards and domain owners, grouped by domain so reviewers see related assets together rather than a random list.
6. Review exceptions
Stewards and owners review proposals through a simple approval interface. They can accept, edit or reject each suggestion. Rejections and edits feed back into the rules and prompts so the next run is more accurate.
7. Move to governed operation
Schedule the workflow to run on a regular cadence and whenever new assets are detected. Track coverage metrics such as percentage of assets with owners, descriptions, classifications and lineage. Report progress to the data governance forum so enrichment becomes a managed process rather than a periodic clean-up.
What good looks like
- Every catalogue asset has an owner, a description, a sensitivity classification and a domain.
- New assets are detected automatically and routed for enrichment within days, not quarters.
- AI-generated content is always reviewed by a named human before it is published.
- There is a clear audit trail showing who approved each definition and when.
- Coverage metrics are visible to leadership and tracked over time.
- The catalogue is the first place people look, not the last.
Benefits
For the business team
Data stewards spend their time reviewing and refining rather than writing from scratch. Analysts and finance teams find trusted definitions in one place, reducing rework and disagreement over numbers.
For leadership
Data governance becomes evidenced and measurable. Regulatory and audit requests can be answered with confidence. Investment in data platforms starts to deliver visible value because the underlying assets are understood.
For the wider business
New joiners onboard faster. Analytics and AI initiatives move more quickly because the data they depend on is documented, classified and owned. Risk of misuse of sensitive data is reduced.
Where to start
Pick one domain rather than the whole estate. Finance, customer or HR data are common starting points because ownership is reasonably clear and the value of good definitions is immediate. Run the enrichment workflow on that domain end to end, prove the model, then extend it to the next domain. A focused first version is far more valuable than an ambitious programme that never completes.
How 4th Revolution can help
4th Revolution is a finance-led, data-led specialist in no-code automation and embedded AI. We design workflows that are not just technically clever but genuinely governed, with clear ownership, controls and audit trails. For data catalogue enrichment, we combine practical metadata engineering with carefully scoped AI to draft descriptions, propose classifications and suggest owners, while keeping humans firmly in control of what is published.
Our goal is not to build a one-off enrichment exercise. It is to leave you with a repeatable, governed process that keeps your catalogue accurate as your data estate evolves.
Example outcome
Before: a catalogue with thousands of assets, fewer than a third with descriptions, inconsistent ownership and no reliable sensitivity tagging. Stewards working through backlog spreadsheets with little visible progress.
After: a governed enrichment workflow runs on a regular cadence. The majority of assets have proposed descriptions, owners and classifications waiting for review. Stewards focus on approving and refining rather than drafting. Coverage metrics improve steadily and are reported to the data governance forum. The catalogue becomes a trusted reference rather than a parallel system.