Who should use the Metadata Management workflow?
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Data
Practical execution plan for metadata management with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A living metadata system that stays accurate and relevant with minimal manual effort.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A living metadata system that stays accurate and relevant with minimal manual effort.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Atlan to a governance charter and glossary that serve as the single source of truth for metadata rules. Then, you pass the output to Atlan to a complete inventory of all data assets with technical and basic business metadata in a central catalog. Then, you pass the output to dbt Cloud (AI-Powered) to a visual lineage map showing end-to-end data flow and all inter-asset relationships. Then, you pass the output to Atlan to automated quality dashboards and alerts that keep metadata trustworthy and up-to-date. Then, you pass the output to Atlan to a user-friendly catalog where business users can self-serve data discovery with trust and context. Finally, Motion AI is used to a living metadata system that stays accurate and relevant with minimal manual effort.
Define Metadata Scope & Governance Rules
A governance charter and glossary that serve as the single source of truth for metadata rules.
Inventory & Extract Existing Metadata
A complete inventory of all data assets with technical and basic business metadata in a central catalog.
Enrich Metadata with Lineage & Relationships
A visual lineage map showing end-to-end data flow and all inter-asset relationships.
Implement Metadata Quality & Validation Rules
Automated quality dashboards and alerts that keep metadata trustworthy and up-to-date.
Publish Metadata & Enable Self-Service Discovery
A user-friendly catalog where business users can self-serve data discovery with trust and context.
Establish Ongoing Metadata Maintenance & Governance Cadence
A living metadata system that stays accurate and relevant with minimal manual effort.
Start by identifying the business context and data domains that require metadata management. Establish naming conventions, ownership, and stewardship roles. Document the governance rules (e.g., mandatory fields, allowed values, retention policies) that will guide all subsequent steps.
Why Atlan: Atlan provides data cataloging and governance capabilities needed to define metadata scope and rules, and can be paired with a spreadsheet for the initial glossary.
Scan all known data assets (databases, data lakes, file shares, BI reports) to automatically extract technical metadata: table schemas, column types, primary/foreign keys, row counts, and last update timestamps. For unstructured sources, extract file names, formats, and size. Manually supplement with any missing business context.
Why Atlan: Atlan offers data discovery and auto-scanning capabilities to inventory and extract existing metadata from various sources.
Map how data flows from source to consumption: trace ETL pipelines, SQL views, and report dependencies. Document column-level lineage so you know where each field originates and how it transforms. Also record relationships between tables (foreign keys) and between datasets (e.g., a report uses a specific view).
Why dbt Cloud (AI-Powered): dbt Cloud (AI-Powered) provides automated SQL generation and AI-generated documentation, which supports lineage tracking and relationship enrichment.
Define and automate checks to ensure metadata remains accurate and complete. For example, validate that every table has an owner, every column has a business term, and lineage is not broken. Set up scheduled scans to flag missing or stale metadata, and create alerts for violations.
Why Atlan: Atlan provides data catalog APIs and governance features that can be integrated with scheduling and monitoring tools for quality validation.
Make metadata accessible to end users through a searchable catalog interface. Provide business glossaries, data dictionaries, and lineage views. Set up role-based access so analysts can find and trust data without asking the data team. Optionally, expose metadata via API for integration into other tools.
Why Atlan: Atlan provides a data catalog UI for self-service discovery and can be exposed via API gateway for metadata publishing.
Set up a recurring process to review and update metadata as data sources change. Schedule monthly stewardship meetings to review quality reports, approve new glossary terms, and retire obsolete assets. Automate change detection (e.g., schema drift) to trigger metadata updates.
Why Motion AI: Motion AI provides automated project planning, task prioritization, and meeting scheduling to support ongoing maintenance cadence.
§ Before you start
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
§ Related
Track competitor moves and market shifts in real-time with automated intelligence gathering — so you always know what your rivals are doing.
Connect siloed business applications into a unified, AI-managed operational pipeline that eliminates manual handoffs between systems.
Analyze portfolios, backtest investment strategies, and receive AI-generated market signals — giving individual investors access to institutional-grade tools.