Metadata Management
Metadata management involves the administration, organization, and utilization of metadata to enhance data access and use.
Classification
- ComplexityMedium
- Impact areaBusiness
- Decision typeDesign
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Loss of Data Due to Erroneous Metadata
- Misunderstandings within the Team
- Lack of Compliance
- Conduct Regular Audits of Metadata
- Develop Clear Data Naming Conventions
- Encourage Collaboration Between Departments
I/O & resources
- Data Source Information
- Metadata Policies
- Access Logs
- Complete Data Catalog
- Unique Data Identifiers
- Growing Metadata Library
Description
Metadata management is a key process in data management that aims to systematically collect and maintain metadata. Effective metadata management improves the discoverability and understanding of data, thereby enhancing efficiency.
✔Benefits
- Enhanced Data Accessibility
- Increased Efficiency in Projects
- Better Decision Making
✖Limitations
- Dependency on Metadata Quality
- Difficulty in Implementation
- Costs for Training
Trade-offs
Metrics
- Data Access Rate
Measure how often data is accessed.
- Data Quality Score
Evaluate the quality of metadata.
- User Acceptance Rate
Measure how many users adopt the new processes.
Examples & implementations
Data Catalog of a Large Company
A multinational company implemented a data catalog to optimize access to data.
Use of Metadata in a BI Tool
A company uses metadata to improve analysis and reporting.
Integration of Metadata in Cloud Databases
Cloud providers integrate metadata management to improve data availability.
Implementation steps
Form a Metadata Management Team
Create Metadata Management Policies
Provide Training for Employees
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated Data Management Artifacts
- Undocumented Data Processes
- Lack of Software Updates
Known bottlenecks
Misuse examples
- Using Data Without Metadata
- Incorrect Data Association
- Releasing Data Without Authorization
Typical traps
- Managing Too Many Data Sources
- Creating Overly Complex Metadata Structures
- Insufficient Use of Metadata
Required skills
Architectural drivers
Constraints
- • Limited Budgeting
- • Technological Restrictions
- • Available Data Sources