Data Classification
Data classification refers to the systematic categorization of data to enhance its use, security, and management.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeDesign
- Organizational maturityAdvanced
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Abuse of classification systems.
- Non-compliance with regulations.
- Data leaks due to misclassification.
- Document all classification steps.
- Regular training for staff.
- Clear communication of policies.
I/O & resources
- Existing datasets for classification.
- Classification policies and standards.
- Training materials for staff.
- Documentation of the classification.
- Reports on data security.
- Access logs.
Description
Data classification provides a structured approach for identifying, categorizing, and managing data within organizations. Effective classification can minimize risks and enhance data security. This process aids in finding relevant data more easily and complying with legal requirements.
✔Benefits
- Increased data security.
- Better compliance with regulations.
- More efficient data management.
✖Limitations
- Limited flexibility due to classification policies.
- Increased effort in data classification.
- Potential misclassifications.
Trade-offs
Metrics
- Number of Classified Data
Metric to assess the efficiency of the classification system.
- Access Requests per Category
The number of requests for access to classified data categories.
- Maintenance Costs per Year
Annual costs for maintaining and updating the classification system.
Examples & implementations
Data Classification in an Insurance Company
An insurance company implements a classification system to protect customer data and control access.
Data Classification in Retail
A retailer classifies sales data to optimize inventory.
Classification and Storage in the Cloud
An organization uses cloud services for data classification and storage.
Implementation steps
Train staff on data classification.
Implement classification policies.
Conduct regular reviews of the classifications.
⚠️ Technical debt & bottlenecks
Technical debt
- Old classification systems need to be replaced.
- Inadequate updates of security policies.
- Undocumented classification processes.
Known bottlenecks
Misuse examples
- Accessing classified data without authorization.
- Using incorrect classification policies.
- Non-compliance with data protection regulations.
Typical traps
- Lack of training in data security.
- Inadequate resources for implementation.
- Lack of clear responsibilities.
Required skills
Architectural drivers
Constraints
- • Legal requirements for data protection.
- • Technological limitations regarding data management.
- • Organizational policies on data security.