This course describes a comprehensive data quality driven approach to data conversion and consolidation—dC3 methodology.
This course is available at the TDWI World Conference in Orlando. For more information, and to register, visit www.tdwi.org/orlando2007.
Data Integration / Data Analysis & Design
Gian Di Loreto
CEO, Loreto Services and Technologies, LLC
Co-Founder, Data Quality Group LLC
You Will Learn
- The data quality challenges that are inherent in data conversion and consolidation
- A data quality approach to data conversion, consolidation, and cleansing (dC3)
- Discovery and analysis techniques for a thorough understanding of the source data
- Techniques to define and implement a quality-focused data conversion strategy
- Techniques to define and implement a quality-focused data consolidation strategy
- The what, why, and how of data cleansing
- Data conversion and consolidation practitioners—those in the trenches who are responsible to design, develop, maintain and operate data conversion and consolidation processes for
enterprise reporting, business analytics, compliance, ERP implementation, legacy system replacement, etc.
- Data quality practitioners—those in the trenches who are responsible to design, develop, maintain, and operate data cleansing processes and to perform data cleansing activities
Data conversion and consolidation is a major root cause of poor data quality. Numerous system implementations overrun schedule and budget or fail outright because quality of the converted data
proves inadequate. This typically is due to lack of analysis and understanding of the source data, as well as poorly defined target data quality specifications. The problem is especially acute
in data consolidations during corporate mergers and acquisitions, as well as implementations of data warehouses and operational data stores. This course describes a comprehensive data quality
driven approach to data conversion and consolidation—dC3 methodology.
1. Introduction to Data Conversion, Consolidation, and Cleansing (dC3)
2. Data Conversion Strategy
- What is data conversion and consolidation?
- Why data conversion and consolidation cause deterioration in data quality
- What are the common mistakes in data conversion and consolidation?
- What are the cornerstones of the dC3 methodology?
- What are the steps in a dC3 project?
- What are the roles and responsibilities in a dC3 team?
3. Data Consolidation Strategy
- How to analyze and profile data sources
- How to assess source data quality
- How to define target data specifications
- How to measure target data quality
- How to choose correct sources for all target data elements
- How to define target-to-source mappings
- How to ensure data quality throughout data conversion
- When to execute data cleansing
4. Data Cleansing Strategy
- What data consolidation strategies exist?
- How to choose a consolidation strategy that delivers the highest data quality
- How to build data staging area
- How to match data from various data sources
- How to consolidate overlapping, redundant, and conflicting data
- How to consolidate time-dependent and state-dependent data
- How to integrate new data with existing target data
5. Advanced Topics in Data Conversion and Consolidation
- What are the steps of data cleansing?
- How to organize and analyze error reports
- What are the sources and types of data corrections?
- How to design automated data correction rules
- How to cleanse state-dependent data
- How to cleanse time-dependent data
- How to decompose a dC3 project into simple steps
- How to build and use a dC3 decision tree
- How to maintain data lineage and audit trail throughout a dC3 project
- How to integrate dC3 metadata into a data quality metadata warehouse
- How to manage changes in data and requirements throughout a dC3 project
This course can be brought Onsite! Find out more!