TDWI Data Quality Fundamentals Training in Finland

Managing data quality is among the most vexing of information management issues. Most organizations have persistent and long-standing data quality problems—troubles that grow and propagate with the challenges of data redundancy, purchased applications and databases, legacy databases, multiple data providers and consumers, missing documentation, and uncertainty in defining data quality.

Stepping up to data quality improvement isn’t easy. It demands an understanding of quality management principles and practices, and the ability to apply those practices to a complex and continuously changing data resource. Whether your goal is a broad enterprise-wide data quality program or a highly targeted data quality project, you must begin by understanding the practices and processes of data quality assessment and improvement. This course is designed specifically to provide that foundational knowledge.

There are no prerequisites for this course.

Data quality and data governance professionals; BI/DW managers, architects, designers, and developers; data stewards, data architects, and data administrators; information systems analysts, designers, and developers; anyone with a role in data quality or information systems testing

• Definitions and dimensions of quality
• How to create an actionable definition of data quality
• Typical causes of data quality problems
• Roles, responsibilities, and accountabilities in data quality management
• Roles, uses, and limits of data quality tools and technology
• Processes and techniques for data quality assessment and data quality improvement

Module One: Data Quality Concepts 

Defining Data Quality

• Common definitions of quality
• Applying quality definitions to data
• Data correctness and data integrity
• Actionable data quality

Dimensions of Data Quality

• Accuracy
• Completeness
• Consistency & dependency
• Precision & granularity
• Timeliness
• Structural integrity

Common Causes of DQ Problems

• Definition
• Design & modeling
• Data entry & data collection
• Conversion and consolidation
• Integration

Module Two: Data Quality Practices and Processes

Quality Management Practices

• Quality Assurance (QA) vs. Quality Control (QC)
• Quality economics
• Inspection and detection
• Correction and prevention

Quality Management and Data

• Business applications and operational data
• Integrated data and business information
• Data quality and defect propagation

Data Quality Organizations

• Governance
• Ownership
• Stewardship
• Custodianship
• Architecture
• Usage (access, update, and application)

Data Quality Processes

• Data profiling
• Data quality assessment
• Data cleansing
• Process improvement

Data Quality Tools and Technology

• Profiling
• Verification & standardization
• Matching & grouping
• De-duplication
• Data transformation

Module Three: Data Quality Assessment

Planning & Preparation

• Project planning
• Assessment team
• Assessment resources

Conducting the Assessment

• DQ Rule identification
• DQ Rule execution
• Analysis and tuning

Assessment Results

• Error catalog
• Data quality measures and metrics
• DQ scorecard

Applied Results

• Communication & expectations
• Root cause analysis
• Quality improvement
• Process improvement
• Data cleansing
• Data governance

Module Four: Data Quality Improvement

Procedural Data Quality

• Standardization
• Verification
• Classification
• Parsing
• Geo-coding Matching
• Grouping
• De-Duplication

Rule-Based Data Quality

• Five kinds of data correctness rules
• Six kinds of data integrity rules
• Four kinds of timeliness rules
• Applied DQ rules

IT Processes and Data Quality

• System architecture & standards
• Application and database development processes
• Conversion & migration processes
• Data warehousing & BI processes

Business Processes and Data Quality

• Defining Data
• Creating and updating data
• Access, analysis, and reporting

Module Five: Summary and Conclusion

• Summary of Key Points
• References & Resources