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IRM UK | Ten Steps to Data Quality
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The Ten Steps to Data Quality course teaches a practical approach to creating, improving, and managing the quality of information critical to providing products and services, satisfying customers, and achieving goals for any type of organization. If you are working on real data quality-related issues that need real results, this is the course for you.

 Export to Your Calendar 10/24/2018 to 10/26/2018
When: 24 - 26 October 2018
Wednesday through Friday
Where: Map this event »
etc.venues Marble Arch
Garfield House,
86 Edgware Rd,
London W2 2EA
United Kingdom
Presenter: Danette McGilvray
Contact: +44 (0)20 8866 8366

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IRM UK

Ten Steps to Data Quality


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

Register On-line:
24 - 26 October 2018, London

Course Fee 
£1,595 + VAT (£319) = £1,914


Overview

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 investments in business processes and systems.

The Ten Steps to Data Quality course teaches a practical approach to creating, improving, and managing the quality of information critical to providing products and services, satisfying customers, and achieving goals for any type of organization. If you are working on real data quality-related issues that need real results, this is the course for you. What is learned 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.

Both concepts and practical application are included. Concepts provide a foundation for understanding data quality. Concepts are put into action through the Ten Steps™ process. Both are needed to apply the methodology appropriately to the many data quality related situations that attendees will face within their organizations. In addition to discussion and exercises (individual and as a group), attendees will practice what is learned by applying the steps and techniques to a course project of their choice.

Come with your particular needs in mind, be ready to participate, practice applying what is learned to your situation and leave with realistic methods for managing data quality.

Learning Objectives

After attending this course, delegates will be able to:

  • 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, (for example, the Framework for Information Quality, information life cycle, data quality dimensions, business impact techniques, root cause analysis)
  • Choose the appropriate steps/activities from the Ten Steps™ process to address business needs
  • See how other data management topics such as data governance, data modeling, metadata, business rules, master data, reference data, and data standards fit into the process for ensuring high quality data

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.

Audience

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

  • Data Analysts
  • Data Quality Analysts
  • Business Analysts
  • Data Designers/Modellers
  • Data Stewards
  • Application Developers

This class has also proven helpful for some managers and project managers as they also need to understand what is involved in data quality. They are the ones who hire and assign people’s time and are called upon to 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. With a focus on bottom-line results, Granite Falls helps organizations enhance the value of their information assets by connecting their strategy to practical steps for implementation. We also emphasize the importance of communication and human factors affecting the success of their business goals, issues, strategies, and opportunities.

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 any organization. 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.

A skilled facilitator, program and project manager, she has worked with people at all levels of the organization and from most functional areas, giving her a valuable perspective on organizational challenges based on real-life experience. Danette has consulted with and helped organizations with their data quality and governance efforts in industries as varied as biotech, pharma, insurance, banking, retail, automotive, financial services, direct selling, utilities, higher education, energy, and water management. Danette is a popular speaker and has taught her highly-rated courses in several countries.

Course Fee 
£1,595 + VAT (£319) = £1,914

Register On-line:
24 - 26 October 2018
, London

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