Ten Steps to Data Quality – Live Streaming only

Course Outline

The Data and Information Quality Challenge

• Information and data quality defined
• Why we care about data quality
• Data quality in action through programs, projects, and operational processes
• The Ten Steps™ methodology – key concepts plus the Ten Steps™ process

Key Concepts – A Necessary Foundation for Understanding Information Quality

• Framework for Information Quality (FIQ) – Components that impact information quality:
  • Business Needs – Goals, Strategies, Issues, Opportunities.
  • Information Life Cycle (POSMAD – Plan, Obtain, Store and Share, Maintain, Apply, Dispose)
  • Key Components that affect information quality (Data, Processes, People/Organizations, Technology)
  • Interaction between the Information Life Cycle and the Key Components
  • Location (Where) and Time (When and How Long)
  • Broad-Impact Components (RRISC – Requirements and Constraints, Responsibility, Improvement and Prevention, Structure and Meaning, Communication, Change)
• The relationship between Data Governance, Stewardship, and Data Quality

Step-by-Step:  The Ten Steps™ Process

• Each of the Ten Steps is covered in the seminar with instructions, techniques, examples, templates and best practices
• Data quality tools will also be discussed in the applicable steps
• Exercises and working on a course project with small teams give attendees the opportunity to practice what is learned

Step 1 – Determine Business Need and Approach

• Define and agree on the issue, the opportunity, or the goal to guide all work done throughout the project
• Refer to the business need throughout the other steps in order to keep the goal(s) at the forefront of all activities

Step 2 – Analyze Information Environment

• Gather, compile, an analyze information about the current situation and the information environment
• Document and verify the information life cycle, which provides a basis for future steps, ensures that relevant data are being assessed, and helps discover root causes
• Design the data capture and assessment plan

Step 3 – Assess Data Quality

• Evaluate data quality for the data quality dimensions applicable to the issue
• Results of assessments provide a basis for future steps, such as identifying root causes and determining needed improvements and data corrections
• Overview of all the dimensions of data quality and how to choose which dimensions will best support business needs

Step 4 – Assess Business Impact

• Determine the impact of poor-quality data on the business using a variety of qualitative and quantitative techniques.
• This step provides input to establish the business case for improvement, to gain support for information quality, and to determine appropriate investments in your information resource

Step 5 – Identify Root Causes

• Identify and prioritize the true causes of the data quality problems
• Develop specific recommendations for addressing the problems

Step 6 – Develop Improvement Plans

• Finalize specific recommendations for action
• Develop improvement plans based on the recommendations
• Establish ownership for implementation

Step 7 – Prevent Future Data Errors

• Implement solutions that address the root causes of the data quality problems

Step 8 – Correct Current Data Errors

• Implement steps to make appropriate data corrections

Step 9 – Implement Controls

• Monitor and verify the improvements that were implemented
• Maintain improved results by standardizing, documenting, and monitoring appropriate improvements

Step 10 – Communicate Actions and Results

• Document and communicate the outcome of quality tests, improvements made, and results of those improvements
• Communication is so important that it is part of every step

Special Features of the Course

This course is based on the book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ (Morgan Kaufmann Publishers, 2008) by Danette McGilvray.  The Ten Steps™ provides a structured framework to help attendees start their data quality work, yet is flexible enough to be applied to many different data quality situations. Attendees will receive a copy of the book along with extensive course material, templates, and examples they can put to use in their own organizations.  These are excellent references for the many data related projects and situations attendees could encounter in the future.

Attendees will benefit from Danette’s extensive experience as a consultant and practitioner. Delegates should be prepared to participate as this is a highly interactive course. Class discussion and exercises (both individual and with teams) are an integral part of the seminar.  Attendees have the opportunity to apply what is learned to a course project, chosen from their real data quality situations and concerns. This creates an environment where attendees can contribute their viewpoints and also learn from the instructor and each other’s experiences.