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Call Us: 01225 339705 Email: This email address is being protected from spambots. You need JavaScript enabled to view it. Address: Verhoef Training, 11 Kingsmead Square, Bath, BA1 2AB

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Audience

This course is intended for:

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

Dates (Bath):

12 Feb 2018

11 Jun 2018

3 Sep 2018

5 Nov 2018

Click for details & booking.

Also available on your site, please call for details.

Prerequisites

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.


Duration

4 days.


Objectives

This course teaches how to build QualityStage parallel jobs that investigate, standardize, match, and consolidate data records. Students will gain experience by building an application that combines customer data from three source systems into a single master customer record.

Course objectives include:

  • 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


Topics

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

QualityStage Overview
Describing QualityStage architecture
Describing QualityStage clients and their functions

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

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

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

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

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

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

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