Data catalog process flowchart (register to certify)
Data catalog process template for registering an asset, enriching metadata, assigning stewardship, validating user context, certifying status and maintaining the record.
What the data catalog process flowchart (register to certify) process is
A useful catalog entry is built through review, not produced by a connector alone. This template begins when an asset producer submits a dataset, report, model or interface for registration. Catalog operations capture a stable identifier and location, import available technical metadata, and confirm that the asset can actually be identified and accessed. Stewards then classify its domain, type and sensitivity, add a business definition, and link terms, owners and related assets. A real user tests whether the record can be found and interpreted before the data owner chooses an appropriate publication status.
Certification here is an internal, criteria-based trust signal, not an external assurance or a guarantee that the data fits every use. An asset that does not meet certification criteria can still be published as unverified with explicit limitations rather than hidden or overstated. The data lineage process at /templates/data-lineage-documentation-process documents source-to-consumption relationships that can enrich the catalog, while /templates/data-quality-management-process owns the rules and monitoring behind quality signals. Governance disputes about definitions or ownership belong in /templates/data-governance-process. This chart keeps the catalog's responsibility focused on registration, metadata quality, review, publication status, discovery and scheduled maintenance.
What this flowchart covers
In this template
- Asset registration with a stable identifier, location, contact and imported technical metadata
- Domain, type and sensitivity classification plus business definitions, terms, owners and related-asset links
- Stewardship assignment and checks for source, freshness, access path, discovery and user interpretation
- Owner review with separate certified, revise and publish-unverified outcomes based on stated criteria
- Searchable publication, user notification, ownership and freshness monitoring, and a route back through review after material change
When to use this template
- A catalog contains automatically harvested tables but users cannot tell what they mean, who owns them or whether they are current
- New reports, datasets or data products become widely used before their definitions, access paths and limitations are reviewed
- Certification labels are applied inconsistently and the organization needs visible criteria, ownership and review dates
- Catalog records become stale after source, schema, owner or intended-use changes because no maintenance route exists
How it works
Define registrable assets
List the asset types and minimum technical identifiers the catalog accepts, including how each asset is located and who can answer source questions. Decide which metadata can be imported and which context requires human stewardship.
Set metadata completeness rules
Specify mandatory definitions, domain, type, sensitivity, owner, steward, access route, freshness and related terms by asset type. Keep optional detail separate so incomplete essentials cannot hide behind a large volume of harvested fields.
Test with a real consumer task
Ask a representative user to find the asset, explain its meaning, locate its access route and identify a limitation. Feed failures back into the definition and links before owner review.
Define publication statuses
Write the evidence required for certification, who approves it, how long the status lasts and how unverified assets are labeled. Avoid implying that certification makes an asset suitable for uses that were never assessed.
Trigger maintenance from change
Identify source, schema, owner, access, quality and usage changes that reopen review. Assign review dates and use consumer feedback to find records that are technically current but no longer understandable.
Frequently asked questions
What are the steps in a data catalog process?
Register the asset and technical location, import available metadata, confirm accessibility, classify the asset, add business context and relationships, assign stewardship, verify source and access details, test discovery with a user, review limitations, choose a publication status, make the record searchable and monitor it for change.
What does certification mean in a data catalog?
It should mean the asset met the organization's stated review criteria for a defined scope and date. Those criteria may cover ownership, definitions, freshness, quality evidence, lineage or intended use. Certification should not be presented as a universal guarantee or external assurance.
Can an uncertified asset appear in the catalog?
Yes, if users benefit from discovering it and the status and limitations are clear. Publishing an asset as unverified can be safer than hiding it, because consumers can see the owner, context and cautions instead of finding an undocumented source elsewhere.
How are a data catalog and data lineage related?
The catalog is the discovery and context layer for assets, terms, owners and statuses. Lineage describes how data moves and changes from sources to consumption. A catalog can display lineage links, but those links still need a separate discovery, mapping and validation process.
Where this process fits
In most operations this process follows Data governance process flowchart (issue to closure) and hands off to Data quality management process flowchart.
It is one step in Data governance.
Step 2: Data governance process flowchart (issue to closure)
Data governance process template for scoping issues, assigning ownership, assessing cross-domain impact, recording decisions, implementing actions and closing with evidence.
Step 3: Data catalog process flowchart (register to certify) You are here
Data catalog process template for registering an asset, enriching metadata, assigning stewardship, validating user context, certifying status and maintaining the record.
Step 4: Data quality management process flowchart
Data quality management process template for prioritizing critical data, defining measurable rules, monitoring results and sustaining preventive improvements over time.