concept#Data#Analytics#Compliance
Data Lineage
Data lineage describes the origin and flow of data through systems and processes.
Data lineage enables transparency and traceability in data management systems.
Maturity
Established
Cognitive loadMedium
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityAdvanced
Technical context
Integrations
Reporting ToolsData Analysis PlatformsCompliance Management Systems
Principles & goals
Ensure TransparencyDocumentation of Data OriginCompliance with Regulations
Value stream stage
Discovery
Organizational level
Enterprise
Use cases & scenarios
Use cases
Scenarios
Compromises
Risks
- Incorrect Data Acquisitions
- Overload Due to Data Visualization
- Lack of Acceptance in the Team
Best practices
- Provide Regular Training
- Maintain Transparent Communication
- Proactive Problem Identification
I/O & resources
Inputs
- User Requirements
- Available Data Sources
- Compliance Guidelines
Outputs
- Updated Data Flow Diagrams
- Reports on Data Streams
- Transparent Data Processing Logs
Description
Data lineage enables transparency and traceability in data management systems. It plays a central role in areas such as data integration, compliance, and data quality.
✔Benefits
- Improved Data Quality
- Meeting Legal Requirements
- Optimization of Data Processes
✖Limitations
- High Effort for Implementation
- Complexity in Large Systems
- Dependencies Between Data
Trade-offs
Metrics
- Data Quality KPIs
Key indicators for measuring data quality.
- Compliance Rate
Percentage of compliant data statistics.
- Data Processing Time
Time required for processing data.
Examples & implementations
Data Lineage in an E-Commerce System
An e-commerce company uses data lineage to trace the origin of its product data.
Transparency in Healthcare Data Management
Healthcare organizations use data lineage to ensure the integrity of patient data.
Compliance Management in Financial Services
Financial service providers use data lineage to meet regulatory requirements.
Implementation steps
1
Capture Data Stocks
2
Define Processes for Data Analysis
3
Establish Data Management Policies
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated Data Sources
- Insufficient Data Integrity
- Poor Documentation Practices
Known bottlenecks
Data QualitySystem IntegrationResource Conflicts
Misuse examples
- Not Updating Data Flows
- Collecting Irrelevant Data
- Missing Review of Data Origins
Typical traps
- Overly Complicated Diagrams
- Lack of User Acceptance
- Unplanned Downtime
Required skills
Knowledge in Data ManagementAnalytical SkillsKnowledge of Regulatory Requirements
Architectural drivers
Integration with Existing SystemsUnderstanding Data ArchitectureCompliance with Security Standards
Constraints
- • Technological Constraints
- • Regulatory Requirements
- • Budget Constraints