Data Quality Management (DQM)
Data Quality Management (DQM) is the systematic approach to ensuring and improving data quality in companies. The article explains the most important concepts, processes and tools of DQM and their significance for successful data strategies. Learn how you can sustainably improve the quality of your company data through effective DQM.
The definition of data quality management (DQM)
Data Quality Management (DQM) describes the systematic approach for measuring, monitoring and improving data quality in organizations. It includes all processes, methods, and technologies that ensure that data is suitable for its intended use.
Distinction from related concepts and DQM
- Data Governance: DQM is a part of data governance. While data governance defines the overall framework for handling data, DQM focuses specifically on quality aspects.
- Master Data Management (MDM): MDM focuses on managing master data, while DQM covers all types of data.
- Data cleansing: Data cleansing is an operational process within DQM and relates specifically to the cleansing of data.
Key elements of data quality management (DQM)
1. Quality dimensions
- Accuracy: Compliance with reality
- Completeness: availability of all necessary information
- Consistency: Freedom from contradiction across different systems
- Timeliness: Validity of the data
- Relevance: Suitability for intended use
2. Basic processes
- Quality measurement: Definition and collection of quality metrics
- Quality monitoring: Continuous monitoring of data quality
- Quality improvement: Actions to address quality issues
3. Organizational aspects
- Roles and responsibilities: Data Stewards, Data Quality Managers
- Guidelines and standards: Quality standards, process guidelines
- Training and communication: Staff training
Technological basics
Basic technologies
- Profiling tools: Analyzing data structure and quality
- Validation systems: Check against defined rules
- Monitoring systems: Monitoring quality metrics
Advanced technologies
- Machine learning: Automatic detection of anomalies
- Rule-based systems: Automatic data validation
- ETL processes: Data integration and cleansing
Typical challenges
Organizational
- Lack of quality standards
- Unclear responsibilities
- Lack of quality awareness
Technically
- Heterogeneous system landscapes
- Complex data structures
- Scaling issues with large amounts of data
Success factors
- Strategic orientation: Alignment with corporate goals
- Management Support: Provision of resources
- Employee Engagement: Active involvement of all stakeholders
- Tool support: Use of appropriate technologies
- Measurability: Define clear quality metrics
Relevance for companies
Business Impact
- Improved decision quality
- Higher process efficiency
- Reduced error costs
- Increased customer satisfaction
Compliance
- Compliance with regulatory requirements
- Data protection compliance
- Auditability
Related terms
- Data Governance
- Master Data Management
- Data Lifecycle Management
- Information Quality Management
- Data Cleansing
- Data profiling
Conclusion
Data quality management is an essential part of modern data management. It combines organizational and technical aspects to systematically improve and ensure the quality of data. As part of data governance, DQM makes a significant contribution to creating value from data.
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