Explore Data Catalog
Summarize
Summary of Explore Data Catalog
The Data Catalog is a centralized, self-service platform designed to help ServiceNow customers discover, evaluate, and access governed data assets across the enterprise. It addresses the common challenge of locating trustworthy, well-documented data scattered across multiple systems by providing a unified discovery and governance layer. This enables users to search for data assets, understand their lineage and quality, and request access to governed data efficiently.
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Key Features
- Search and Discovery: Users can find data assets using keyword search, faceted filters, and browsing by source system, domain, or collection. The search spans asset names, descriptions, tags, classifications, and business glossary terms, with results showing trust scores and quality indicators.
- Asset Details and Relationships: Detailed metadata for each asset is available, including schema, field descriptions, ownership, classifications, and lineage information.
- Business Glossary: Enables creation and maintenance of standardized business terms linked to catalog assets, promoting consistent use of data definitions across the organization.
- Metadata Collectors: Automated tools that harvest technical metadata from source systems, including schemas and lineage. These collectors run on scheduled or on-demand cycles to keep the catalog current as source systems change.
- User Roles:
- Connection Admin: Manages data source connections and metadata collectors.
- Data Steward: Enriches assets with business context, manages glossary terms, ownership, tags, classifications, and organizes assets.
- Catalog Viewer: Searches, reviews, and evaluates data assets for use in analytics, workflows, or AI.
Data Catalog Workflow
The Data Catalog lifecycle involves distinct phases that enable efficient data governance and consumption:
- Connect: Connection Admins establish external data connections and configure metadata collectors.
- Harvest: Metadata collectors automatically gather and update technical metadata and lineage.
- Enrich: Data Stewards add business context, assign ownership, and organize assets.
- Discover: Catalog Viewers search and evaluate assets based on metadata, lineage, and trust scores.
- Access: Users request access through governance workflows and consume data via tables, APIs, dashboards, or AI agents once approved.
Key Outcomes
- Efficient Data Discovery: Users can locate data assets across enterprise systems without manual coordination, improving productivity.
- Informed Decision-Making: Trust scores, quality indicators, and sample previews allow users to assess data reliability before access.
- Automated Metadata Management: Metadata collectors ensure the catalog remains accurate as source systems evolve.
- Consistent Business Vocabulary: The business glossary fosters standardized data definitions organization-wide.
- Improved Data Governance: Asset organization, classification, and stewardship assignments enhance accountability and control.
Next Steps
ServiceNow customers looking to implement or optimize the Data Catalog should explore configuring metadata collectors, methods to find and access data assets, and best practices for governing the catalog to maximize its value.
The Data Catalog is the self-service discovery layer for finding, evaluating, and accessing governed data assets.
The Data Catalog provides a centralized discovery and governance layer where users search for data assets, understand their lineage and quality, and request access to governed data across the enterprise.
Data Catalog overview
The Data Catalog addresses a common enterprise challenge. Data exists across dozens of systems, but finding trustworthy, well-documented assets requires manual coordination across teams. The Data Catalog solves this by providing a unified discovery layer. Metadata collectors automatically harvest technical metadata, Data Stewards add business context, and consumers evaluate trust scores and lineage before requesting access.
Search and discovery:
Find data assets through keyword search, faceted filtering, and browsing by source system, domain, or collection. Search looks across asset names, descriptions, tags, classifications, and business glossary terms. Results include trust
scores and quality indicators.
Asset details and relationships:
View comprehensive details for each data asset including schema, field descriptions, ownership, data classifications, and data relationships, including lineage.
Business glossary:
Create and maintain business glossary terms that define enterprise data vocabulary. Link glossary terms to catalog assets to provide business context. This promotes consistent use of data definitions across the organization.
Metadata collectors:
Automated scanners that connect to source systems, discover schemas, and build lineage relationships. They populate the Data Catalog with technical metadata. Collectors run on schedules or on demand to keep catalog metadata current as
source systems evolve.
Data Catalog users
| User | Description |
|---|---|
| Connection admin | Creates and manages connections to external systems and configures metadata collectors. Schedules collector runs and monitors collection execution and logs. |
| Data Steward | Enriches catalog assets with business context and creates and maintains business glossary terms. Links terms to assets, assigns ownership, manages tags and classifications, organizes assets into domains and collections, and tracks asset lifecycle status. |
| Catalog Viewer | Searches and browses the Data Catalog to discover data assets. Views asset details and lineage, evaluates trust scores and quality indicators, previews sample data, and identifies assets for use in analytics, workflows, or AI applications. |
Data Catalog workflow
This lifecycle shows the distinct phases of discovery, governance, and consumption in the Data Catalog:
- Connect: Connection Admins create connections to external data sources and configure metadata collectors. These harvest technical metadata including schemas, tables, columns, relationships, and lineage.
- Harvest: Metadata collectors run on schedules or on demand to discover assets and build lineage relationships. They populate the catalog with up-to-date technical metadata from connected source systems.
- Enrich: Data Stewards add business context by creating glossary terms, linking terms to assets, adding descriptions, assigning ownership, applying classifications, and organizing assets into domains and collections
- Discover: Catalog Viewers search and browse to find relevant data assets. They review metadata and lineage, evaluate trust scores, preview sample data, and identify assets that meet their requirements.
- Access: Users request access to discovered assets through governance workflows. After approval, they consume governed data through Data Fabric tables, APIs, analytics dashboards, or AI agents.
Data Catalog benefits
| Benefit | Feature | Users |
|---|---|---|
| Find data assets across enterprise systems without manual coordination | Search, browse, faceted filtering | All users |
| Understand data quality and trustworthiness before requesting access | Trust scores, quality indicators, sample data preview | Catalog Viewer |
| Automatically discover and catalog metadata as source systems evolve | Metadata collectors, scheduled harvesting | Connection admin |
| Provide business context and shared vocabulary for enterprise data | Business glossary terms, asset descriptions | Data Steward |
| Organize and classify assets for improved discoverability and governance | Domains, collections, tags, classifications | Data Steward |
| Establish accountability through ownership and stewardship assignments | Owner and steward assignment, lifecycle management | Data Steward |
What to explore next
To learn more about using the Data Catalog, see: