IBM InfoSphere QualityStage Essentials V11.5 - SPVC - 2M213G

Description

1. Data Quality Issues
Listing the common data quality contaminants
Describing data quality processes

2. QualityStage Overview
Describing QualityStage architecture
Describing QualityStage clients and their functions

3. Developing with QualityStage
Importing metadata
Building DataStage/QualityStage Jobs
Running jobs
Reviewing results

4. Investigate
Building Investigate jobs
Using Character Discrete, Concatenate, and Word Investigations to analyze data fields
Reviewing results

5. Standardize
Describing the Standardize stage
Identifying Rule Sets
Building jobs using the Standardize stage
Interpreting standardize results
Investigating unhandled data and patterns

6. Match
Building a QualityStage job to identify matching records
Applying multiple Match passes to increase efficiency
Interpreting and improving Match results

7. Survive
Building a QualityStage survive job that will consolidate matched records into a single master record

8. Two-Source Match
Building a QualityStage job to match data using a reference match

Aim

List the common data quality contaminants

Describe each of the following processes:

Investigation
Standardization
Match
Survivorship

Describe QualityStage architecture

Describe QualityStage clients and their functions

Import metadata

Build and run DataStage/QualityStage jobs, review results

Build Investigate jobs

Use Character Discrete, Concatenate, and Word Investigations to analyze data fields

Describe the Standardize stage

Identify Rule Sets

Build jobs using the Standardize stage

Interpret standardization results

Investigate unhandled data and patterns

Build a QualityStage job to identify matching records

Apply multiple Match passes to increase efficiency

Interpret and improve match results

Build a QualityStage Survive job that will consolidate matched records into a single master record

Build a single job to match data using a Two-Source match

Participant requirements

Participants should have:
Familiarity with the Windows operating system
Familiarity with a text editor
Helpful, but not required, would be some understanding of elementary statistics principles such as weighted averages and probability.

Participant

Data Analysts responsible for data quality using QualityStage
Data Quality Architects
Data Cleansing Developers