Ten Steps to Quality Data. 3-Day Virtual Course.

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 Addressing Information Quality

• Framework for Information Quality (FIQ) – Components that impact information quality:
  • Business needs (customers, products, services, strategies, goals, 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 (RRISCCE – Requirements and constraints, Responsibility, Improvement and prevention, Structure and meaning, Communication, Change, Ethics)
• 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 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 Needs and Approach

• Identify and agree on business needs and data quality issues within scope of the project
• Reference them to guide work and keep at the forefront of all activities throughout the project.
• Determine project type and approach:
  • Focused data quality improvement project
  • Data quality activities in another project
  • Ad hoc use of data quality steps, activities, or techniques

Step 2 – Analyze Information Environment

• Learn about the information environment surrounding the business needs and data quality issues within scope
• Determine what is within scope of the project and the appropriate level of detail for each element of the information environment:
  • requirements and constraints
  • data and data specifications
  • processes
  • people and organizations
  • technology
  • the information life cycle
• This ensures that relevant data will be assessed for quality and provides input to future steps, such as when identifying root causes

Step 3 – Assess Data Quality

• Overview of various data quality dimensions, which are used to define, measure, improve, and manage the quality of data and information
• Learn what is needed to:
  • Select the data quality dimensions applicable to the business needs and data quality issues within scope
  • Design a suitable data capture and assessment plan
  • Make use of data quality assessment results: analyze individual assessments and synthesize with other results; make initial recommendations, document, and take action when the time is right

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 improvements, 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 and will avoid data errors from reoccurring

Step 8 – Correct Current Data Errors

• Make appropriate data corrections
• Ensure data corrections do not introduce new errors

Step 9 – Monitor Controls

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

Step 10 – Communicate, Manage, and Engage People Throughout

• Communication, engaging with people, and managing the project throughout are essential to the success of any data quality project
• These are so important that they should be included as part of every other step

Special Features of the Course

This course is based on the recently-released second edition of the book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ (2021, Elsevier/Academic Press) 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 new 2^nd edition along with extensive course materials, 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.