Data Governance Framework
A Data Governance Framework establishes guidelines and practices to enhance data management and usage within an organization.
Classification
- ComplexityMedium
- Impact areaOrganizational
- Decision typeOrganizational
- Organizational maturityAdvanced
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Lack of Employee Buy-in
- Lack of Resources
- Difficulties in Implementing Changes
- Regular training sessions for employees
- Involve all stakeholders
- Document all processes
I/O & resources
- Data Policy Documentation
- Resources for Training
- Technological Infrastructure
- Updated Policies
- Training Materials
- Data Quality Reports
Description
A Data Governance Framework is crucial for efficient management of data resources within an organization. It defines processes, standards, and responsibilities to ensure data quality and security. An effective framework contributes to better decision-making and compliance.
✔Benefits
- Improved Data Quality
- Better Decision Making
- Regulatory Compliance
✖Limitations
- High Implementation Costs
- Requires Ongoing Training
- Can Be Time-Consuming
Trade-offs
Metrics
- Data Quality Score
A measure of the accuracy and completeness of data.
- Time to Data Cleansing
The time required to clean data and ensure compliance with policies.
- Number of Trainings
The number of trainings conducted for data management.
Examples & implementations
Example from the Financial Sector
A financial institution implemented a framework to enhance data oversight and ensure compliance.
Health Data Management
A hospital utilized a governance framework to securely manage patient data.
Public Administration
An agency adopted a framework to enhance data integrity.
Implementation steps
Review existing data policies
Organize training for the employee team
Conduct regular audits for data quality
⚠️ Technical debt & bottlenecks
Technical debt
- Technical debts due to inadequate infrastructure
- Resistance due to outdated practices
- Lack of automation opportunities
Known bottlenecks
Misuse examples
- Ignoring data quality assurance processes
- Disregarding regulatory compliance
- Inadequate training of employees
Typical traps
- Overloading policies
- Too many changes at once
- Lack of feedback loops
Required skills
Architectural drivers
Constraints
- • Compliance with Internal Policies
- • Technological Limitations
- • Budget Constraints