Syllabus

This is an approximate syllabus and is likely to change.
Syllabus:

  1. Introduction
    1. What is Data Quality?
    2. Value Proposition: Impacts of Poor Data Quality
  2. Dimensions of Data Quality
    1. Data Quality of Data Models
    2. Data Quality of Data Values
    3. Data Quality of Presentation
    4. Other DQ issues
  3. Data Extraction and Transformation
    1. Legacy Data Migration
    2. ETL
    3. Data Transformation
  4. Data Quality Improvement
    1. The Data Quality Improvement Cycle
    2. Statistical Process Control
    3. Creating a Successful Data Quality Program
  5. Metadata, Data Quality, and ETL
    1. What is Metadata?
    2. Domains
    3. Mappings
    4. Reference Data Management
  6. Rules
    1. Data Quality Rules
    2. Data Transformation Rules
    3. Discovery of Data Quality rules
  7. Uses of Rules
    1. Message Transformation
    2. Data quality validation
    3. Data-Dependent GUI Generation
  8. Populating the Data Warehouse
    1. Using ETL to populate from Legacy systems
    2. Population from operational systems
    3. Rule-based Data Warehouse Quality Certification
  9. Data Cleansing Techniques
    1. Duplicate Elimination
    2. Data Standardization
    3. Record Linkage
    4. Approximate Matching and Searching
  10. Scalability Issues
    1. Rule Discovery
    2. Cleansing
    3. Rule application

=A9 2000, David Loshin