Continuous Data Quality Monitoring (CDQM)
Continuous Data Quality Monitoring ensures the ongoing monitoring and improvement of data quality within organizations.
Classification
- ComplexityMedium
- Impact areaOrganizational
- Decision typeTechnical
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Data Integrity Issues
- Dependence on Software Vendors
- Insufficient User Acceptance
- Provide regular training for users.
- Maintain transparent communication about progress.
- Make proactive adjustments based on feedback.
I/O & resources
- Access to Data Sources
- Monitoring Tools
- Data Quality Metrics
- Reports on Data Violations
- Quality Improvement Plans
- Monitoring Insights
Description
Continuous Data Quality Monitoring (CDQM) is a process for ensuring ongoing data quality. It enables organizations to quickly identify and rectify issues in their data, leading to better decision-making and increased efficiency.
✔Benefits
- Improvement of Data Quality
- Rapid Issue Resolution
- Better Decision-Making
✖Limitations
- High Initial Investments
- Dependence on Technologies
- Complex Implementation
Trade-offs
Metrics
- Error Rate
Number of errors per data point.
- Data Availability
Measurement of the time data is available.
- User Satisfaction
Degree of user satisfaction with data quality.
Examples & implementations
Data Quality Project at Company X
Company X implemented CDQM to enhance the accuracy of its customer data.
Automated Monitoring at Company Y
Company Y used CDQM for automatic real-time error detection.
Quality Improvement Initiative at Company Z
Company Z carried out a data quality improvement initiative using CDQM.
Implementation steps
Identify and assess data sources.
Configure monitoring tools.
Define data quality metrics.
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated Systems
- Lack of Documentation
- Unresolved Error Tickets
Known bottlenecks
Misuse examples
- Checking data quality only once a year.
- Automation without human oversight.
- Tracking too many metrics at once.
Typical traps
- Confusing data errors with user errors.
- Neglecting implementation.
- Unrealistic expectations of technology.
Required skills
Architectural drivers
Constraints
- • Budget Constraints
- • Regulatory Requirements
- • Technological Limitations