Ten Steps to Quality Data

Two-Day Seminar
Thursday, June 14 and Friday, June 15

Seminar Hours
Thursday, June 14: 8:30AM - 4:30PM
Friday, June 15: 8:30AM - 4:00PM

Course Outline:

The Data and Information Quality Challenge
  • Information and data quality defined
  • Approaches to data quality in projects
  • 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 Goals/Strategy/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)
  • Information and Data Quality Improvement Cycle (Assess, Analyze, Action)
  • 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
  • The assessment results provide a basis for future steps, such as identifying root causes and needed improvements and data corrections
  • Attendees will get an overview of all the dimensions of data quality in the process.

Step 4 – Assess Business Impact

  • Using a variety of techniques, determine the impact of poor-quality data on the business
  • 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 continuously 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

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