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Home > Training > Data Warehousing > Evaluating and Improving Data Quality Training

Evaluating and Improving Data Quality Training

Course#: WA2358

Participants learn the skills required to conduct projects in the three main areas of a data quality – profiling, assessment and cleaning and process improvement.  Data profiling skills focus on understanding the data available.  Assessment and cleansing expands upon traditional definitions and incorporates multiple perspectives of data quality.  The final section of the course has participants apply a proven process improvement approach to data with the intent of learning how to create ‘good’ data instead of cleaning ‘bad’ data.

Why Attend this Course?

  • A significant number of data quality projects are conducted with:
  • Very constrained definitions of data quality.
  • This course provides guidance on examining the many aspects of data quality from a business perspective.  It goes beyond thinking about domain values to timeliness, usefulness and even accessibility of the data.
  • A focus upon cleaning bad data. 
  • This course emphasizes a process improvement approach to data quality.  Data profiling and root cause analysis are taught as important techniques for improving the quality of data used in decision-making.

What Makes this Course Stand Apart?

  • The three-day program provides participants with ample time to learn and practice the techniques associated with data quality projects.  Rather than separate conceptual examples, the course employs a case study, based on a real-world project. 
  • Following a proven framework, participants will craft a data quality assessment plan and then execute the plan as part of a process improvement effort.
  • Participant materials include job aids that can be used as reference material upon return to their organizations.

What You Will Learn

Upon completion of this course you will be able to:

  • Carry out a data profiling effort to use as a baseline for data assessment using exploratory data analysis techniques.
  • Create a plan to assess and clean data.
  • Employ techniques to assess and clean data.
  • Create a business case for improving data quality, cost benefit analysis, impact and root causes.
  • Understand master data management techniques for effective data distribution throughout an organization.
  • Apply Quality Management concepts to data related problems.
  • Use root cause analysis to recommend high impact process improvements that will increase the quality of decision-making data.

Audience

  • Business Analyst (IT and non-IT)
  • Data Quality Analyst
  • Database Administrators
  • Project Leaders
  • Systems Analyst
  • Data Analyst

Prerequisites

The structure of the courses assumes no prior experience in statistics or data analytics. 

Duration

3 days

Outline of WA2358 Evaluating and Improving Data Quality Training

1. Overview

  • Data Quality – The Big Picture
  • Quality Management
  • Control vs. Assurance
  • Continuous Improvement
  • Data Quality Management
  • Processes (Overview level)
  • MDM., Assessment, Profiling, Cleansing, Audit Trails, Etc.
  • Interventions (Overview level)
  • Assessments, Process Improvements, etc.
  • Risk Analysis
  • Organizational Issues
  • Governance
  • Stewardship
  • Types of Data (unstructured, transactional, metadata, etc.)
  • Sources of Errors

2. Useful General Techniques

  • Information Flow
  • Data Models
  • Descriptive Statistics

3. Data Profiling

  • Purpose
  • Determining Data to Profile
  • Techniques (column, subject, attribute dependency, etc)

4. Data Quality Assessment

  • Defining Rules, Constraints and Relationships
  • Historical vs. In-process data
  • End-to-End Analysis
  • Issues with Data Integration

5. Data Cleansing Projects

  • Purpose
  • Techniques

6. Continuous Improvement

  • Lean Six Sigma Approach
  • Statistical Process Control

7. Master Data Management

  • Purpose
  • Types of Master Data
  • Industry Best practices
We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.
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