The Data Catalog Dilemma

Context and problem statement

Data catalogs are typically acquired to solve a real discovery and trust problem: people cannot reliably find the right data, understand what it means, or decide whether it is fit for purpose. In many implementations, the tooling is delivered but the underlying metadata management practice is weak, so users encounter incomplete, outdated, or overly technical entries and stop using the catalog.

What a data catalog is (and what it is not)

A data catalog is a curated inventory of data assets and their metadata, designed to support discovery, understanding, and governance. In DAMA-DMBOK terms, it is an interface to Metadata Management capabilities, not a replacement for them. A catalog is not:

• A “single source of truth” for data values (that is the job of data stores and controlled pipelines)
• A substitute for data quality management, stewardship, or ownership
• A one-time documentation project (metadata must be continuously maintained)

Core concepts: metadata types and why “freshness” matters

Catalog value depends on multiple metadata types being present and reliable:

• Technical metadata: schemas, columns, data types, partitions, storage locations, job schedules
• Business metadata: business terms, definitions, KPI logic, domain context, policies
• Operational metadata: pipeline runs, freshness/SLAs, incidents, usage, access events
• Lineage metadata: upstream/downstream relationships across pipelines, transformations, BI layers
• Quality metadata: rule results, anomaly detection outputs, known issues, certifications When any of these are stale or inconsistent, users cannot evaluate fitness for use, and trust collapses. Treat “metadata freshness” as a first-class requirement (similar to data freshness): define what must be updated, how often, and how updates are validated.

Why data catalogs fail in practice

Common failure modes typically map to missing operating model elements rather than missing features:

• Stale or incomplete metadata: automation is not connected to the real execution path (ELT/ETL, orchestration, semantic layer), so updates lag behind reality
• Terminology mismatch: the catalog reflects system-centric names rather than business concepts, violating basic principles of shared vocabulary and stewardship
• Search that does not match user intent: search is not tuned with synonyms, business term mapping, popularity signals, and faceted filtering
• Disconnected workflows: the catalog is a separate destination instead of being embedded where work happens (SQL editor, BI tool, ticketing, CI/CD)
• Unclear ownership and accountability: no data owners/stewards, no RACI, and no lifecycle for definitions, certifications, and deprecations
• Governance perceived as friction: publishing and approval steps are heavy, so teams bypass the catalog

Framework alignment: how DAMA-DMBOK and TOGAF apply

From DAMA-DMBOK, successful catalogs are outcomes of coordinated practices across:

• Metadata Management (collection, integration, publishing, and control)
• Data Governance (decision rights, policies, stewardship, issue management)
• Data Quality Management (measurement, monitoring, remediation, communication)
• Data Architecture and Modeling (consistent structures, naming standards, and semantic clarity) From TOGAF’s architecture viewpoint, a catalog should be treated as part of the enterprise information landscape:
• Data assets (Data Architecture) must be described consistently and linked to processes and capabilities
• The catalog should support architectural governance (standards, exceptions, lifecycle status) and enable impact analysis through lineage

A practical operating model for a “living” catalog

Implement the catalog as a product with defined users, outcomes, and ongoing operations.

1) Define target users and primary use cases

Prioritize 3–5 use cases that matter to day-to-day work and instrument success:

• Find a trusted dataset for a report or analysis
• Understand a metric (definition, source, transformation, owner)
• Assess fitness for use (freshness, known issues, quality signals)
• Perform impact analysis before a schema/pipeline change
• Request access with policy-aware routing

2) Establish clear roles and decision rights (lightweight governance)

Create accountable ownership without slowing delivery:

• Data Owner: accountable for the data product/domain outcomes and access decisions
• Data Steward: responsible for definitions, context, and issue triage
• Data Custodian/Engineering: responsible for technical metadata, pipelines, and operational reliability
• Governance/Platform: sets standards, enables tooling, and monitors adoption Document a simple RACI for key actions: publishing, certification, deprecation, definition changes, and access policy updates.

3) Automate metadata capture from the execution path

Aim for “capture by default” so the catalog stays current with minimal manual effort:

• Ingest technical metadata from warehouses/lakes, BI tools, and query engines
• Ingest lineage from orchestrators and transformation tools (for analytics engineering, connect lineage to models and CI/CD)
• Ingest operational metadata (job status, run times, SLA/freshness indicators)
• Tie catalog entries to versioned definitions (e.g., business logic and documentation stored alongside code) and publish on deployment Manual entry should be reserved for high-value business context (definitions, examples, caveats), not for basic facts the system already knows.

4) Make business meaning discoverable via a semantic layer and glossary

To reduce jargon and ambiguity:

• Maintain a business glossary with approved terms, synonyms, and owners
• Link glossary terms to physical assets (tables, columns) and to semantic metrics (measures, dimensions)
• Prefer metric definitions that are executable and testable (semantic layer or modeled metric layer) rather than free-text descriptions This approach aligns with dimensional modeling goals (shared conformed definitions) while remaining compatible with modern ELT and data products.

5) Build trust signals that are objective and explainable

Avoid “trust me” labels; provide evidence:

• Freshness status vs. SLA
• Data quality test outcomes and trends
• Known issues/incidents linked to affected assets
• Lineage that shows where the data comes from and how it is transformed
• Usage signals (frequently queried, used in key dashboards), with appropriate privacy controls Introduce certification levels (e.g., draft, reviewed, certified) with clear criteria and an expiration/recertification mechanism.

6) Integrate the catalog into daily workflows

Adoption increases when the catalog appears at decision points:

• In SQL editors: inline definitions, owners, quality/freshness, and lineage for referenced tables/columns
• In BI tools: dataset and metric definitions surfaced where users build reports
• In collaboration tools: links, automated notifications (e.g., “this dataset is deprecated”), and Q&A routing to owners
• In change management: require catalog updates as part of pull requests/releases for breaking changes and deprecations Treat workflow integration as part of the Analytics Development Lifecycle (requirements → build → test → deploy → document → monitor), not an afterthought.

Documentation-first as a staged approach (when it works and when it doesn’t)

Starting with documentation under version control can be effective when:

• The environment is small to medium and teams already collaborate via code review
• Definitions and models change frequently and must be traceable
• You need to standardize naming, definitions, and ownership before buying or expanding tooling However, documentation-first has limits if you cannot automate discovery, lineage, and operational signals at scale. A pragmatic path is:
• Phase 1: versioned documentation + glossary + ownership
• Phase 2: automated harvesting (schemas, lineage, operational metadata) and publishing
• Phase 3: embedded experiences and governance workflows (access requests, certification, deprecation)

Best practices and common pitfalls

Best practices:

• Treat metadata as a managed asset with lifecycle, standards, and quality controls
• Prioritize automation for technical and operational metadata; reserve human effort for business context and stewardship
• Design for users: synonyms, examples, and “how to use” notes are often more valuable than exhaustive field descriptions
• Make trust measurable with freshness, quality results, and incident transparency
• Align catalog content to data products/domains and the semantic layer to reduce definition drift Common pitfalls:
• Relying on manual entry for core metadata (guarantees staleness)
• Publishing hundreds of assets without owners, SLAs, or clear lifecycle states
• Equating “has a description” with “fit for use” (missing quality and operational context)
• Implementing governance as heavy approvals rather than clear accountability plus automation

How to measure catalog effectiveness

Use metrics tied to the intended outcomes:

• Adoption: monthly active users, searches per user, click-through to assets
• Discovery success: search-to-open rate, time-to-first-trusted-dataset, repeated searches for the same term
• Trust signals: percentage of critical assets with owners, SLAs, lineage, and quality indicators
• Operational impact: reduction in duplicate datasets/metrics, fewer incidents caused by undocumented changes, faster impact analysis

Key takeaways

Data catalogs fail most often because metadata management is treated as a one-time population exercise instead of an ongoing practice. A successful catalog combines automated technical and operational metadata, stewarded business context, objective trust signals, and deep integration into analytics and engineering workflows. Implementing a clear operating model (roles, lifecycle, standards, and measurement) is as important as selecting the catalog tool.