The Data and Information Quality Challenge
• Information and data quality defined
• Approaches to data quality in projects
• Your data quality challenges
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
• The Ten Steps™ methodology – key concepts plus the Ten Steps™ process
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. The Ten Steps are the concepts in action.
• Data quality tools will also be discussed in the applicable steps.
• Exercises and working on a course project with a team give attendees the opportunity to practice what is learned.
Step 1 Determine Business Need and Approach
• “Connecting-the-dots” between the data quality issue and business needs
• Define and agree on the issue, the opportunity, or the goal to guide all work done throughout the project. (Refer to this step throughout the other steps in order to keep the goal at the forefront of all activities.)
Step 2 Analyze Information Environment
• Gather, compile, and 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.
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 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 the first step to the many human factors that impact data quality success and are vital to address. Communication is so important that it is part of every step.
Along with the seminar materials, delegates will receive a copy of the book “Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™” by Danette McGilvray. This is an excellent reference for future projects and situations encountered.