Overview

Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This course will step you through the QualityStage data cleansing process. You will transform an unstructured data source into a format suitable for loading into an existing data target. You will cleanse the source data by building a customer rule set that you create and use that rule set to standardize the data. You will next build a reference match to relate the cleansed source data to the existing target data.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

Audience

The intended audience for this course are:
• QualityStage programmers
• Data Analysts responsible for data quality using QualityStage
• Data Quality Architects
• Data Cleansing Developers
• Data Quality Developers needing to customize QualityStage rule sets

Prerequisites

Participants should have:
• Compled the QualityStage Essentials course, or have equivalent experience
• familiarity with Windows and a text editor
• familiarity with elementary statistics and probability concepts (desirable but not essential)

Objective

Please refer to the course overview

Course Outline

After completing this course,  you should be able to:
• Modify rule sets
• Build custom rule sets
• Standardize data using the custom rule set
• Perform a reference match using standardized data and a reference data set
• Use advanced techniques to refine a Two-source match