1.1 Business Drivers
Increasing data value
Decreasing poor data risks
Improving efficiency and productivity
Protecting organization's reputation
1.2 Goals & Principles
Goals
Developing a governed data approach
Defining standards and specifications for data quality
Defining and implementing processes to measure, monitor, and report on data quality levels
Data Quality Dimensions
Accuracy
Completeness
Consistency
the degree that data correctly represents ‘real-life’ entities.
whether all required data is present.
ensuring that data values are consistently represented within a data set and between data sets, and consistently associated across data sets.
Integrity
Reasonability
Timeliness
Uniqueness
Validity
making sure that data is up to date
Data Quality Activities
Define High Quality Data
Define a Data Quality Strategy
Identify Critical Data and Business Rules
Perform Initial Data Quality Assessment
(DQA)
Principles
Criticality
Lifecycle Management
Prevention
Root Cause Analysis
Governance
Standards Driven
Objective Measurement
Systematically Enforced
Common Causes of DQ Issues
Data Entry Process Issues
Data Entry Interface
List Entry - Drop-Down
Training Issues
Changes to Business Process
Data Processing Issues
incorrect assumptions about data sources
Stale Business Rules
Changed Data Structures
System Design Issues
Failure to enforce referential Integrity
Data Model Inaccuracies
Temporal Data Mismatches
Data Duplication
Week Master Data
Issues Caused by Fixing Issues
All changes should go through a governed change management process.