Data Catalog

In depth

The data catalog sits between a business glossary, that includes the vocabulary of the business,  and a data dictionary, that provides the technical schema. It maps plain‑language concepts to physical data assets and adds rich context.

A complete data catalog manages multiple kinds of metadata:

• Technical metadata: For example, schemas, tables, columns, data types, query stats, and freshness.
• Business metadata: For example, user-friendly names, descriptions, KPIs and metrics, and domains.
• Operational and social metadata: Such as, owners, stewards, popularity, usage, and ratings.
• Governance metadata: For example, sensitivity labels, access policies, retention rules, and compliance notes.

Common capabilities include search and browse, tagging and taxonomy, lineage and impact analysis, preview and profiling, and guided access requests. All this results in faster discovery, fewer one‑off Slack pings, and better trust in shared numbers.

Pro tip

Start small. Catalog one high‑value domain first, set clear ownership, and agree on naming patterns. Expand only after usage shows real demand.

Why Data Catalog matter

• Faster discovery: Users can quickly find the data they’re looking for.
• Shared understanding: Business terms and technical fields align.
• Better governance: Sensitive data is labelled and access is controlled.
• Higher trust: Freshness, lineage, and quality checks are visible.
• Lower support load: Fewer ad‑hoc requests to data teams.

Data Catalogs- In practice

Let’s look at two quick scenarios:

• SaaS churn analysis: Search the catalog for “churn,” land on a curated dataset with owner, refresh schedule, and linked metrics such as “Active Customers” and “Churn Rate”. Build your view with confidence.
• E-commerce returns: Browse the “Product” domain, open the “Returns” dataset, read the definition of “return_reason”, and see lineage back to the “order_events” table.

Here’s an example of a simple entry template:

Domain: Customer Success
Business term: Churn
Dataset or table: analytics.prod.customer_status_daily
Primary keys: customer_id, as_of_date
Important fields: status, churn_flag, plan_tier, region
Owner: Data Platform Team (owner@dataco.example)
Steward: Jane Smith (data steward)
Source systems: app_db, billing
Refresh schedule: hourly
Sensitivity: PII present (email), masked for general access
Quality checks: row count bounds, null checks on customer_id
Downstream metrics: Active Customers, Churn Rate, Net Revenue Retention

Data Catalogs and PowerMetrics

In PowerMetrics, you can:

• Organize your metrics. Use the metric catalog in PowerMetrics to present business‑friendly names, clear definitions, and tags for related assets.
• See data lineage. Reference the source dataset and steward in each metric description so users know where the data comes from and who to contact.
• Set metrics as approved for general use. Apply certification to signify trusted metrics.
• If you maintain a semantic layer such as dbt or Cube, reuse descriptions and tags inside PowerMetrics to keep context consistent.