# Custom Attributes - Use Case Examples
This page provides practical examples for adding enrichment metadata to the Catalog. These custom attributes are intended to enrich discovery, search, and consumer guidance.
## Who this page is for
* Data governance stewards and business analysts who tag and enrich datasets for discoverability.
* Data catalog admins and platform owners implementing metadata fields and UX for consumers.
## Examples of attributes
Example application of custom attributes onto an asset.
| Attribute | Type | Purpose / Notes |
| ----------------------------------------------- | ------------------ | ------------------------------------------------------------------------------------------------------------------- |
| Legal Jurisdiction of Processing | multi-select enums | Helps consumers know where data is processed or stored; useful for search and filtering |
| Highlighted Business Contacts | list of user | Supplement primary owners — e.g., Subject Matter Experts, Business Contacts, Project Leads |
| Highlighted Business Contacts | list of users | Supplement primary owners — e.g., Subject Matter Experts, Business Contacts, Project Leads |
| Business Category / Domain | multi-select enums | High-level business grouping (e.g., Finance, Marketing, Supply Chain) to improve discovery and lineage grouping |
| Data Sensitivity Category (informational) | multi-select enums | Steward-curated sensitivity label purely for guidance (does not replace classification fields) |
| Regional Audience / Consumer Group | multi-select enums | Who the dataset is primarily intended for (e.g., EU Analytics Team, US Ops) to guide request routing and discovery |
| Masking / Pseudonymization Notes | plain text | Short note describing whether masking/pseudonymization is recommended or commonly applied (informational) |
| Allowed Processing Purposes (informational) | plain text | Describes common or recommended business uses to guide consumers; not an access control list |
| Third-party Processors Involved (informational) | plain text | Informational list of known external recipients — helps reviewers and consumers; link to vendor record if available |
| Retention Guidance Key | plain text | Human-friendly retention guidance pointer (links to policy; not an automated key unless you wire it to systems) |
| Gloss / Consumer Guidance | plain text | Short free-text guidance: intended uses, known caveats, common pitfalls, query examples for consumers |
## Use case 1 — New dataset onboarding (enrichment & discoverability)
Purpose: enrich a newly registered dataset so internal consumers can find and understand it quickly.
Steps for the steward:
1. Inspect the registration form or dataset record submitted by the engineering owner.
2. Add `Business Category / Domain` to group the dataset (e.g., Finance > Revenue).
3. Add `Highlighted Business Contacts` to point to subject matter experts or project leads beyond the primary owner.
4. Add `Legal Jurisdiction of Processing` as an informational tag to help consumers filter for region-specific data.
5. Populate `Gloss / Consumer Guidance` with a short note describing common queries, expected freshness, and any caveats.
Platform recommendations:
* Show the enrichment fields prominently in the dataset header (contacts, domain, jurisdiction, and one-line guidance).
* Use these tags to improve catalog search, filters, and suggested related datasets.
Expected outcome: internal consumers can quickly find the dataset, know who to contact, and see a short guidance snippet before requesting access.
## Use case 2 — Consumer discovery and quick screening
Purpose: help requestors and stewards quickly decide whether a dataset is worth requesting or exploring further.
Attributes used: `Gloss / Consumer Guidance`, `Business Category / Domain`, `Legal Jurisdiction of Processing`, `Highlighted Business Contacts`.
Steps for the steward:
1. Ensure the `Gloss / Consumer Guidance` contains a short summary and example queries so requestors can self-screen.
2. Use `Business Category / Domain` and `Legal Jurisdiction` to surface the dataset to relevant teams via filters and saved searches.
3. If the requestor still needs access, they can use the `Highlighted Business Contacts` to ask clarifying questions before submitting a formal request.
Platform recommendations:
* Show the one-line `Gloss` in search results and dataset previews to reduce unnecessary access requests.
Expected outcome: fewer low-value access requests; consumers make better-informed requests aligned to business context.
## Use case 3 — Tagging for retention guidance (informational)
Purpose: add lightweight retention guidance to datasets so consumers and stewards know typical retention expectations.
Attributes used: `Retention Guidance Key`, `Gloss / Consumer Guidance`.
Steps for the steward:
1. Add `Retention Guidance Key` with a short human-friendly pointer to the applicable policy (e.g., "See RET-PII-90 in Retention Playbook").
2. Add a note in `Gloss` describing whether retention is commonly short or long and who to contact for questions.
Platform recommendations:
* Use `Retention Guidance Key` in reports to help stewards prioritize formal retention audits.
Expected outcome: greater clarity about expected retention timelines without enforcing automated deletion unless integrated with policy systems.
## Use case 4 — Informational vendor notes for exports
Purpose: document known third-party processors and vendor notes to help reviewers and requestors prepare exports.
Attributes used: `Third-party Processors Involved (informational)`, `Masking / Pseudonymization Notes`, `Gloss / Consumer Guidance`.
Steps for the steward:
1. Populate `Third-party Processors Involved` with vendors commonly receiving the dataset and a short note if the vendor requires specific handling.
2. Add `Masking / Pseudonymization Notes` describing typical transformations previously applied or recommended.
3. Add an export-prep note in `Gloss` linking to vendor teams or vendor pages.
Expected outcome: reviewers and requestors have a quick reference about vendors and recommended transformations.
## Use case 5 — Data quality notes and consumer expectations
Purpose: record expected data quality and refresh cadence as enrichment so consumers understand limitations.
Attributes used: `Gloss / Consumer Guidance`, `Data Quality SLA / Refresh Cadence` (informational), `Business Category / Domain`.
Steps for the steward:
1. Add a short `Data Quality SLA / Refresh Cadence` note (e.g., "Refreshed nightly, expect up to 1% nulls in field X").
2. Capture typical known caveats in `Gloss` (e.g., late-arriving data, timezone issues).
3. Add `Business Category` to help route issues to the right teams.
Platform recommendations:
* Surface the `Gloss` and refresh cadence prominently so consumers see expectations before they use the data.
Expected outcome: consumers have clearer expectations and fewer surprise incidents; triage is faster because the right teams are pre-identified.
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