Data Quality Group

      Course Title:
         Data Conversion, Consolidation, and Cleansing - Practical Skills

      Geared To:
         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.

      You Will Learn:
         The data quality challenges that are inherent in data conversion and consolidation.
         A methodological and  quality-focused approach to data conversion, consolidation, and cleansing (dC3).
         Discovery and analysis techniques to achieve thorough understanding of your source data.
         Techniques to define and implement a quality-focused data conversion strategy.
         Techniques to define and implement a quality-focused data consolidation strategy.
         Advanced topics of the dC3 approach including project planning, decision trees, data lineage tracking, 
           metadata management, and change management

      Summary:
             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 is typically 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.

      Course Outline:
         1. Introduction to Data Conversion, Consolidation, and Cleansing (dC3)
             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 dC3 methodology?
             What are the steps in a dC3 project?
             What are the roles and responsibilities in a dC3 team?

         2. Source Data Discovery and Analysis
             How to select and qualify data sources?
             How to handle non-relational "legacy" data?
             How to handle unstructured data?
             How to build data staging area?
             How to match data from various data sources?
             How to analyze and profile data sources?

         3. Data Conversion Strategy
             How to define target data specifications?
             How to define and measure target data quality?
             How to choose correct sources for all target data elements?
             How to define target-to-source mappings?
             How to define transformation and aggregation rules?
             How to ensure data quality throughout data conversion?

         4. Data Consolidation Strategy
             What data consolidation strategies exist?
             How to choose consolidation strategy that delivers highest data quality?
             How to consolidate basic indicative data?
             How to consolidate time-dependent data?
             How to consolidate state-dependent data?
             How to integrate new data with existing target data?

         5. Advanced Topics in Data Conversion and Consolidation
             How to decompose dC3 project into simple steps?
             How to build and use dC3 decision tree?
             When to execute source data quality assessment and data cleansing?
             How to maintain data lineage throughout dC3 project?
             How to integrate dC3 metadata into data quality metadata warehouse?
             How to manage changes in data and requirements throughout dC3 project?
XHTML | CSS