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IRM UK 2-Day Seminar & Workshop | Ten Steps to Data Quality
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Join us to learn the Ten Steps to Quality Data and Trusted Information™ – a practical approach to creating, improving, and managing the quality of information critical to running your business, satisfying customers, and achieving company goals. If you're working on real data quality-related projects that need real results, this is the seminar for you.

6/6/2017 to 6/7/2017
When: June 6 - 7, 2017
Tuesday and Wednesday
Where: etc.venues Marble Arch
Garfield House,
86 Edgware Rd,
London W2 2EA
United Kingdom
Presenter: Danette McGilvray

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Ten Steps to Data Quality


Use code AEA10 to receive 10% AEA member discount when registering!!

Register On-line:
6-7 June 2017, London

Simply put, information quality is providing the correct set of accurate information, at the correct time and place, to the correct people. However, ensuring quality information is far from simple. Whether you are just starting a project or are already in production, it is not unusual to find that data quality issues prevent organizations from realizing the full benefit of their investment in new business processes and systems.

Join us to learn the Ten Steps to Quality Data and Trusted Information™ – a practical approach to creating, improving, and managing the quality of information critical to running your business, satisfying customers, and achieving company goals. If you working on real data quality-related projects that need real results, this is the seminar for you.   What you learn here applies to all kinds of data and every type of organization – for-profit businesses of all sizes, education, government, healthcare, and nonprofit – because all depend on trusted information to succeed.

Key topics include:

  • The Ten Steps™ process
  • The Framework for Information Quality
  • The Information Life Cycle
  • Analyzing the information environment
  • Assessing data quality and business impact 
  • Conducting root cause analysis and implementing controls
  • Essential communication to meet information quality needs
  • Real-life application of the framework and methodology

Come with your particular needs in mind, learn how these topics apply to your situation and leave with realistic methods for improving information quality. Be prepared to participate as discussion, individual and group exercises, and applying what is learned to a course project are an integral part of the seminar.  Both foundational data quality concepts and practical application are included. 

This course is based on the extensive experience of the trainer/author/consultant and the book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information(Morgan Kaufmann Publishers, 2008) by Danette McGilvray.

Learning Objectives

  • Turn data quality challenges into actionable projects with clear objectives.
  • Connect data quality issues to business priorities.
  • Understand concepts that are fundamental to data quality management, such as the Framework for Information Quality, information life cycle, data quality dimensions, business impact techniques, root cause analysis techniques, etc.
  • Choose the appropriate steps/activities from the Ten Steps process to address business needs.
  • Apply many of the steps and techniques to a course project during the seminar.
  • Obtain templates and examples to use in the attendees’ own situations.

Course Outline

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.

Special Features

Executing Data Quality Projects 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.

Individual contributors and team members responsible for or interested in the quality of data in their business processes, systems, or databases, in roles such as:

  • Data analysts
  • Data quality analysts
  • Business analysts
  • Data designers/modelers
  • Data stewards (business and technical)
  • Application developers

This class has also proven helpful for:

  • Managers of the individual contributors
  • Project managers and leads of the team members listed above

These leaders need to understand what is involved in data quality as they are the ones who hire those responsible for the work, prioritize budgets and people’s time, and remove roadblocks to data quality work.

Speaker Biography

Danette McGilvray

Danette McGilvray is president and principal of Granite Falls Consulting, Inc., a firm that helps organizations increase their success by addressing the information quality and data governance aspects of their business efforts. Focusing on bottom-line results, Danette helps organizations enhance the value of their information assets by incorporating information quality management into the business. She also emphasizes communication and the human aspect of data quality and governance.

Danette is the author of Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (Morgan Kaufmann, 2008).An internationally respected expert, her Ten Steps™ approach to information quality has been embraced as a proven method for creating, improving, and managing information and data quality in the enterprise. Her trademarked approach, in which she has trained Fortune 500 clients and thousands of workshop attendees, applies to all types of data and all organizations. Her book is used as a textbook in university graduate programs. The Chinese translation was the first data quality book available in Chinese.

Danette helps clients solve specific data quality problems through data quality projects or incorporating data quality activities into other projects or methodologies. In addition to projects, Danette helps companies set up data quality and governance programs - formal on-going initiatives that address business needs by providing a foundation and services to sustain data quality. Her approach is outlined in her chapter on Data Quality Projects and Programs, in: S. Sadiq (ed.),Handbook of Data Quality Research and Practice (Springer-Verlag Berlin Heidelberg, 2013).

Danette is an invited speaker at conferences around theworld and received IAIDQ's Distinguished Member Award in recognition of her outstanding contributions to the field of information and data quality.

Seminar Fee
£1,245 + VAT (£249) = £1,494

Register On-line:
6-7 June 2017, London

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