concept#Data#Governance#Compliance
Data Retention
Data retention refers to the strategies and measures for archiving data.
Data retention is crucial for compliance with legal requirements and for managing data throughout its lifecycle.
Maturity
Established
Cognitive loadMedium
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityAdvanced
Technical context
Integrations
Cloud storage servicesData management systemsMonitoring systems
Principles & goals
Ensure data integrityMaintain confidentialityEnsure accessibility
Value stream stage
Build
Organizational level
Enterprise
Use cases & scenarios
Use cases
Scenarios
Compromises
Risks
- Data loss
- Security breaches
- Non-compliance with regulations
Best practices
- Regular data review
- Data law training
- Secure storage
I/O & resources
Inputs
- User data
- Access rights
- Archiving policies
Outputs
- Stored Data
- Deleted Data
- Archived Data
Description
Data retention is crucial for compliance with legal requirements and for managing data throughout its lifecycle. It involves policies for storing, backing up, and deleting data to ensure legal compliance and access to historical information.
✔Benefits
- Compliance with legal requirements
- Improvement of data management
- Increased data security
✖Limitations
- Resource-intensive
- Data loss through mishandling
- Complexity during implementation
Trade-offs
Metrics
- Data loss rate
Proportion of lost data to total amount
- Compliance rate
How well are legal requirements being met?
- Access time
How quickly can data be retrieved?
Examples & implementations
Example 1
Example of data retention in practice.
Example 2
Further example of data management.
Example 3
Educational example for compliance.
Implementation steps
1
Classify data
2
Document policies
3
Train employees
⚠️ Technical debt & bottlenecks
Technical debt
- Unused data
- Outdated storage methods
- Lack of data understanding
Known bottlenecks
Slow data processingInsufficient infrastructureData availability issues
Misuse examples
- Data storage without regard to data protection
- Insufficient educational resources
- Non-compliant data practices
Typical traps
- Changes without documentation
- Mishandling of data
- Retentions without proper reason
Required skills
Data ManagementLegal knowledgeCompliance management
Architectural drivers
SecurityComplianceCost efficiency
Constraints
- • Data protection policies
- • Legal requirements
- • Budget constraints