1. Data Quality Management
1.1. Main Steps
1.1.1. Data Definition
1.1.1.1. Definition
1.1.2. Data Quality Assessment
1.1.2.1. Criteria
1.1.2.1.1. Appropriateness
1.1.2.1.2. Completeness
1.1.2.1.3. Accuracy
1.1.3. Problem Resolution
1.1.3.1. Definition
1.1.4. Data Quality Monitoring
1.1.4.1. Definition
1.2. Data Quality Tools
1.2.1. Profiling
1.2.2. Parsing
1.2.3. Generalized Cleansing
1.2.4. Matching
1.2.5. Monitoring
1.2.6. Enrichment
1.3. Data Cleaning
1.3.1. Process Of Data Cleaning
1.3.1.1. Completeness checks
1.3.1.2. Reasonableness checks
1.3.1.3. Limit checks
1.3.1.4. Identify or remove outliers etc
1.3.2. Data Cleaning Framework
1.3.2.1. Define and determine error types
1.3.2.2. Search and identify error instances
1.3.2.3. Correct the errors
1.3.2.4. Document error instances and error types
1.3.3. Method
1.3.3.1. Correct
1.3.3.2. Filter
1.3.3.3. Detect and report
1.3.3.4. Prevent
2. Introduction
2.1. Definition
2.2. Dimension of data quality
2.2.1. Accurate
2.2.2. Complete
2.2.3. Legible
2.2.4. Relevant
2.2.5. Reliable
2.2.6. Timely
2.2.7. Valid
2.3. Data quality issues
2.3.1. Impacts of poor data quality
2.3.2. Causes of poor data quality
2.3.2.1. Manual data entry
2.3.2.2. Information obfuscation (not clear info)
2.3.2.3. After the Merger
2.3.3. Solutions
2.3.3.1. Monitoring
2.3.3.2. Real-time validation
2.3.3.3. Communication
3. Root Cause Analysis
3.1. Definition
3.2. Purposes
3.3. Steps to identify root cause
3.3.1. Data collection & prioritization
3.3.1.1. Pareto Analysis
3.3.2. Cause charting
3.3.2.1. Cause and Effect Diagram (Fishbone)
3.3.3. Root cause identification
3.3.4. Recommendation Generation and Implementation