Data Quality Group

      Course Title:
         Ensuring Data Quality in Data Integration - Practical Skills

      Geared To:
         Data integration practitioners - those in the trenches who are responsible to design, develop, 
         maintain and operate data integration systems including data warehousing, master data 
         management, enterprise application integration, enterprise information integration, etc.

      You Will Learn:
         The data quality challenges that are inherent in data integration.
         The critical role of data quality monitoring in data integration.
         Specific techniques to monitor and manage quality for real-time data integration.
         Specific techniques to monitor and manage quality for batch data integration.
         The impacts of change on data quality and techniques to address those impacts.
         How an enterprise data integration hub can be applied to managing data quality.

      Summary:
             Corporate data universe consists of numerous databases connected by countless real-time and 
         batch data interfaces. The data continuously move about and change. The databases are endlessly 
         redesigned and upgraded, as are the programs responsible for the data integration. The typical 
         result of these dynamics is that information systems get better, while data quality deteriorates. 
         Without a comprehensive data quality monitoring program bad data spread like viruses. This course 
         discusses various practices that can be put in place to mitigate the problem and maintain high data 
         quality through data integration.

      Course Outline:
         1. Introduction to Data Integration
             What is data integration?
             How data integration causes deterioration in data quality?
             Why and when poor data quality snowballs?
             What are the common mistakes in data integration?
             What different data integration architectures exist?
             What are the components of a data quality monitoring program?

         2. Monitoring Data Quality in Real-Time Interfaces
             How to monitor data quality in real-time interfaces?
             What to do with data errors in real-time interfaces?
             How to monitor data quality in near real-time interfaces?
             How to use monitoring results for taking proactive data quality actions?
             How to integrate results into data quality metadata warehouse?

         3. Monitoring Data Quality in Batch Interfaces
             How to monitor data quality for different batch interface architectures?
             How to identify data errors in batch interfaces?
             When and why use staging area?
             How to compare data in batch interfaces with existing target data?
             What to do with data errors in batch interfaces?
             How to integrate results into data quality metadata warehouse?

         4. Monitoring Changes in Batch Interfaces
             How unexpected changes in batch interfaces impact data quality?
             How to identify unexpected changes in batch interfaces?
             What aggregate characteristics should be monitored?
             How to collect necessary aggregate statistics?
             How to use simple heuristic methods for change monitoring?
             How to use statistical models for change monitoring?
             How to integrate results into data quality metadata warehouse?

         5. Building Enterprise Data Integration Hub             
             What is data integration hub?
             When and why build data integration hub?
             What is the functionality of data integration hub?
             How to integrate data using data integration hub?
             How to validate data in data integration hub?
             How to track data lineage and data quality in data integration hub?
             How to integrate data integration hub with data quality metadata warehouse?
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