Data Catalog and Metadata Management

The Entity shall create a Data Catalog & Metadata Management policy that governs the creation, operation, maintenance, and dissemination of metadata across the Entity. The policy shall cover, at a minimum:

• Overview, purpose, scope, and objectives of the policy
• Policy applicability, validity, and review
• Policy guidelines in alignment to the National Data Management Policy and Standards
• Detailed policy statements covering, at a minimum, the following:
  • Metadata quality dimensions – refer to DQM.1.1.1.SS1
  • Critical metadata attributes (CMAs) identification
  • Developing Metadata Guidelines and Standards
  • Metadata Profiling and Quality Assessment
  • Metadata Quality Escalations and Issue Management
  • Metadata Quality Remediation Process
  • Metadata governance framework and stewardship
  • Metadata Integration and Interoperability
  • Metadata Cataloging and Classification
  • Metadata Versioning and Change Management
  • Metadata Security and Privacy
  • Performance management and monitoring
  • Periodic policy review and updates
  • Key policy performance indicators
• Roles and Responsibilities
• References

In alignment with the Entity’s data strategy, the Entity shall develop a data catalog & metadata management plan, that will be used and monitored to implement and activate data catalog and metadata management initiatives across the Entity. The plan shall include, at a minimum:

• List of activities and milestones. The plan should account for quick wins and longer-term strategic changes
• Owners and stakeholders
• Interdependencies, assumptions, and risks
• Implementation timeline and cost

The Entity shall design an Entity-wide Meta Model, ensuring the metamodel adheres to the existing data architecture standards and aligns with organizational goals. The design process shall include, at a minimum:

• Conduct stakeholder interviews to develop initial knowledge about the range of data held and metadata required and gather requirements for business, technical, and other identified types of metadata
• Integrate the metamodel within the broader context of the Entity’s data model
• Design multiple levels of metamodels (i.e.: high-level conceptual model, middle-level logical model, low-level physical model) - refer to Data Architecture & Modeling Domain
• Define and document the Meta-Entities and their Meta-Attributes to standardize the structure of data models (i.e.: tables, columns, data types, constraints)
• Include control activities (i.e.: job scheduling and monitoring, load statistical analysis, backup/ recovery/ archive/ pugging, query and report generation, etc.)
• Evaluate existing metadata sources and repositories, and integrate the metamodel with current metadata management tools, systems, and repositories
• Document the relationships and hierarchies within the metadata (i.e.: data flows mapping, parent-child relationships documentation, hierarchical structure establishment)
• Establish procedures for ongoing maintenance and updates (i.e.: updates schedule setup, maintenance workflows definition, reviews processes establishment)

Aligning with the Entity’s metamodel design, the Entity shall develop a Metadata Terms Standardization across the Entity. The standardization shall include, at a minimum:

• Generation and standardization of data elements (i.e.: common elements, data element creation protocols, uniformity in data element usage)
• Identification of basic attributes of data elements (i.e.: data type, length, format, relationships, usage guidelines)
• Rules and guidelines for the formulation of data definitions (i.e.: standards terminology, business processes and requirements)
• Naming and identification principles for data elements (i.e.: standardization of naming conventions, descriptive and meaningful of names, prevention of naming conflicts)
• Registration of data elements (i.e.: metadata registry, attributes documentation, easy access and searchability)
• Development of ontologies (i.e.: semantic models, business domain knowledge, data integration and interoperability)
• Creation of taxonomy (i.e.: data elements hierarchical classifications, data discovery and retrieval support, business and operational contexts alignment)

The Entity shall implement a data catalog tool that is required to act as an inventory of the Entity's data assets metadata. The tool shall include, at a minimum:

• Data Catalog automation (i.e.: metadata extraction from databases, metadata updates, metadata refreshes)
• Data Catalog notifications (i.e.: email notifications for metadata updates, alerts for quality issues, reminders for metadata reviews)
• Data Catalog audit trial (i.e.: metadata logging modifications, user actions recording, version history tracking)
• Data Catalog tool versioning (i.e.: version control for tool updates, legacy version maintenance for updates, previous versions rollback if needed)

The Entity shall establish key performance indicators (KPIs) to measure and report on the Entity's Data Catalog Adoption & Usage. KPIs shall include, at minimum:

• Number of registered Data Catalog users
• Number of active Data Catalog users
• Number of logins to Data Catalog
• Number of performed metadata queries
• Number of annotations (tags, comments) added to data assets
• Number of ratings added to data assets
• Number of assigned trust certificates to metadata