Course Title:
Data Quality Fundamentals
Geared To:
Everyone with a role in data management from program and project managers to designers and
developers of databases and data integration, conversion, and consolidation processes.
You Will Learn:
What data quality is and how it impacts the corporate bottom line.
What causes deterioration in data quality.
The key components and results of a comprehensive data quality program.
The roles and responsibilities in a data quality team.
Concepts and principles of data quality assessment and data cleansing.
Quality considerations for data integration.
Quality considerations for data conversion and consolidation.
Summary:
Data quality management is one of the greatest challenges of information technology. According
to the experts the cost of poor data quality can reach as high as 15% to 25% of operating profit.
Corporations are losing million of dollars due to inaccurate data. Yet the data quality profession
is still in its infancy. This course provides a high-level overview of data quality problems and
solutions. It starts with the description of causes of data quality problems and proceeds to outline
major components of a comprehensive data quality program.
Course Outline:
1. Introduction to Data Quality
What is data quality and how it impacts corporate bottom line?
What causes deterioration in data quality?
What are the key components of a comprehensive data quality program?
What are the products of the data quality program?
What are the roles and responsibilities in a data quality team?
2. Data Quality Assessment
What are the common mistakes in data quality assessment?
What are the steps of data quality assessment?
What are the roles and responsibilities in a data quality assessment team?
How to use data profiling in data quality assessment?
How to design and use data quality rules?
How to ensure comprehensive data quality assessment?
How to design aggregate data quality scorecard?
How to build data quality metadata warehouse?
How frequently should data quality be assessed?
3. Data Cleansing
What are the common mistakes in data cleansing?
What are the steps of data cleansing?
What are the roles and responsibilities in a data cleansing team?
How to define data cleansing objectives?
What are the sources and types of data corrections?
How to identify data correction rules?
How to build data correction decision tree?
How to keep audit trail of data corrections?
How to integrate results into data quality metadata warehouse?
4. Data Quality in Data Integration
What are the common mistakes in data integration?
What different data integration architectures exist?
How to identify and handle data errors in real-time interfaces?
How to identify and handle data errors in batch feeds?
How to identify unexpected changes in batch feeds?
How to build data quality monitors for different integration architectures?
How to build information integration hub?
How to integrate results into data quality metadata warehouse?
5. Data Quality in Data Conversion and Consolidation
What are the common mistakes in data conversion and consolidation?
What are the steps in data conversion and consolidation?
How to define target data specifications including data quality benchmarks?
What data conversion and consolidation strategies exist?
How to maintain data lineage throughout data migration?
How to integrate data migration metadata into data quality metadata warehouse?
