TDWI World Conference

Data Integration

Data Integration (DI) is fundamental to data warehousing and is a vital process for a rich and robust data resource to deliver BI solutions. Integration includes all of the activities necessary to acquire data from sources and to transform and cleanse the data. The body of knowledge includes concepts and skills for source data analysis and source qualification, data profiling, source/target mapping, data cleansing and transformation, and ETL development.

• S1: TDWI Data Warehousing Concepts and Principles: An Introduction to the Field of Data Warehousing
  
• M4: Kimball ETL Architecture Overview
  
• M7A: Implementing MDM for BI and Data Integration
  
• M7P: Change Management for MDM
  
• T8A: CIF—Coordinating Your BI, Data Warehousing, and Enterprise Information Initiatives
  
• T8P: Data Quality for Operational BI
  
• W4: Let's Stop Calling It Metadata: It's About Managing Information
  
• W5: Power, Politics, and Partnership: A Path to a More Agile Organization
  
• W8 Kimball Data Warehouse Lifecycle Overview
  
• TH2: TDWI Data Integration Techniques: ETL and Alternatives for Data Consolidation
  
• TH4: The BI Pathway Approach: Delivering BI for Business Value UPDATED
  
• TH8A: Data Requirements Analysis for Business Intelligence and MDM
  
• TH8P: Data Profiling for Data Quality Assessment, Reengineering, and Data Integration
  
• F1: TDWI Data Integration Testing: Ensuring Quality for ETL and Data ConsolidationUPDATED