This course is for data quality stewards who perform data management tasks, such as data quality improvements, data enrichment, and entity resolution.

The self-study version of this course includes structured course notes that provide a detailed overview, essential skills, and exercises, along with a software Virtual Lab to practice.

The e-learning includes:

• digital course notes for self-study
• Virtual Lab: 30 hours of hands-on software practice.

This course can help prepare you for the following certification exam(s): SAS Data Quality Steward for SAS 9.

Learn how to

• create and review data explorations
• create and review data profiles
• create data jobs for data improvement
• establish monitoring aspects for your data.

Who should attend
Data quality stewards

Formats available	Standard duration
Classroom:	3.0 days
Live Web Classroom:	6 half-day sessions	System Requirements
e-Learning:	21 hours/ 180 days	System Requirements

There are no prerequisites for this course.

This course addresses DataFlux Data Management Studio software.This course addresses DataFlux Data Management Studio 2.6 and DataFlux Data Management Server 2.6.

• providing an overview of the technology offerings for SAS Data Quality
• discussing the DataFlux Data Management Platform architecture

• navigating the DataFlux Data Management Studio interface
• creating a Data Managment Studio repository
• verifying the course QKB and reference sources
• working with data connections

• creating data collections
• designing data explorations
• creating data profiles
• designing data standardization schemes

• introduction to data jobs
• setting options for data jobs
• creating a simple data job

• identifying functionality that is available in the Quality grouping of nodes
• standardization, parsing, and casing
• identification analysis and right fielding

• understanding the data enrichment data sources
• working with address verification in a data job

• discussing the concept of match codes
• describing the process of generating match codes
• creating match codes
• clustering records
• adding survivorship to the entity resolution job

• defining business rules
• adding a business rule and an alert to a profile
• creating a historical visualization
• using a business rule in a data job
• data jobs with monitoring tasks

• multi-input/multi-output data jobs
• using data job references within a data job
• woriking with the Data Management server