Data Stewardship
Data Stewardship refers to the responsibility for managing and maintaining data within an organization.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeDesign
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Lack of qualified personnel.
- Technological obsolescence of tools.
- Data misuse could cause legal issues.
- Conduct regular data reviews.
- Provide training on data processing.
- Maintain documentation of all processes.
I/O & resources
- Data Input Procedures
- Permission for Data Use
- Data Catalog
- Report on Data Quality Metrics
- API for Data Provisioning
- Documentation on Data Privacy Policies
Description
Data Stewardship includes principles and practices that ensure effective data management. It promotes data-driven decision making and supports maintaining data quality.
✔Benefits
- Enhanced data quality leads to better decisions.
- Improved data availability for employees.
- Increased compliance with legislative requirements.
✖Limitations
- High costs for data management tools.
- Need for regular training.
- Dependency on external service providers.
Trade-offs
Metrics
- Data Quality Index
Measures the quality of existing data.
- Compliance Rate
Measures compliance with regulations.
- User Satisfaction Index
Evaluates user satisfaction with the data resources.
Examples & implementations
Example of a Successful Data Management Project
A company significantly improved its data quality through targeted data management.
Implementation of a New Data Storage
A company implemented a new cluster for storing large amounts of data.
Training on Data Processing
Employees were trained in a continuing education course to enhance data processing standards.
Implementation steps
Assessment of the current data situation.
Development of strategies for improvement.
Implementation of data protection systems.
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated technology is being used.
- Lack of automated data checking processes.
- Insufficient resources for data management.
Known bottlenecks
Misuse examples
- Unauthorized data usage.
- Neglect of data privacy policies.
- Insufficient data checks.
Typical traps
- Ignoring data quality standards.
- Lack of communication between departments.
- Unrealistic expectations regarding data availability.
Required skills
Architectural drivers
Constraints
- • Technological infrastructure must be available.
- • Compliance with legal requirements.
- • Availability of specialized personnel.