Data Quality Management (DQM)
Data Quality Management (DQM) is a systematic approach to ensuring the accuracy, completeness, and reliability of data.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeDesign
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Data update issues.
- Inaccuracy due to incorrect data input.
- Non-fulfillment of quality standards.
- Regularly review and assess data.
- Establish clear data standards.
- Train employees in data quality.
I/O & resources
- Identify data sources
- Involve stakeholders
- Establish quality criteria
- Quality-checked data
- Generated reports
- Improved data integrity
Description
Data Quality Management (DQM) involves strategies and methods for monitoring and improving data quality. The goal is to optimize decision-making processes and increase efficiency within organizations.
✔Benefits
- Enhanced decision quality.
- Increased process efficiency.
- Reduced risk of data errors.
✖Limitations
- High initial investments.
- Complex implementation.
- Resistance from employees against changes.
Trade-offs
Metrics
- Data Error Rate
The percentage of erroneous records in a dataset.
- Processing Time for Data Requests
The average time taken to process a data request.
- Customer Satisfaction with Data
The level of satisfaction of users with the quality of the data provided.
Examples & implementations
Data Quality Initiative at Company X
Company X conducted a comprehensive DQM initiative to improve the accuracy of its sales data.
Automated Data Cleansing at Company Y
Company Y implemented automated processes for data cleansing, resulting in significant efficiency gains.
Implementation of a DQM Tool at Company Z
Company Z introduced a new DQM tool that significantly improved data quality.
Implementation steps
Communicate the objectives of DQM.
Assess data quality before implementation.
Establish a continuous monitoring process.
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated data management systems.
- Lack of documentation of processes.
- Non-optimized data storage structures.
Known bottlenecks
Misuse examples
- Incorrect data validations.
- Not involving users in the DQM process.
- Not updating data regularly.
Typical traps
- Delays in implementation.
- Resistance to change.
- Ignoring legal requirements.
Required skills
Architectural drivers
Constraints
- • Regulations from data protection laws.
- • Availability of resources.
- • Technological limitations.