Ten Steps to Quality Data. 3-Day Virtual Course
Speaker: Danette McGilvray
15-17 June 2022
30 November - 2 December 2022
All public courses are available as in-house training. Contact us for more information.
Overview
3-Day Virtual Course
Course Newly Updated!
Based on recently-released second edition of Executing Data Quality Projects:
Ten Steps to Quality Data and Trusted Information™
View Course Highlights
If you’re working on business issues where data is a component or have data quality-related issues that need real results, this is the course for you!
The quality of data and information and their impact is of increasing concern in our world today. It is not unusual to find that data quality issues and lack of basic data literacy prevent organizations from realizing the full benefit of their investments in business processes and systems.
Data quality is not just about data entry or having a data quality metrics dashboard – though they are part of it. The things you do to ensure high-quality data will also improve business processes, give you better functioning organizations, more skilled people, and make better use of the technology you have. Ultimately, with high-quality data and trusted information you can better support whatever is most important to your organization, protect its data assets, and manage risk.
By attending the virtual course, Ten Steps to Quality Data you’ll learn a practical approach to creating, improving, sustaining, and managing the quality of information. Book now to secure your place.
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 appropriate 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 applying the steps and techniques to a course project of their choice. Attendees will receive their own copy of Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, 2nd Edition.
All public courses are available as in-house training. Contact us for more information.
Learning Objectives
After attending the Ten Steps to Data Quality course, attendees will:
- Have the background needed to conduct their own data quality project using the Ten Steps methodology – a proven approach for creating, improving, sustaining, and managing data and information quality within any organization
- Understand how the Ten Steps methodology applies to three ways that data quality work gets done in most organizations (through programs, projects, and operational processes)
Through presentation, group and individual exercises, and a course project, attendees will learn how to:
- Turn data quality challenges into actionable projects with clear objectives
- Connect data quality issues with business priorities
- Use business impact techniques to show the value and impact of data quality
- Use data quality dimensions to assess the data that supports business needs and project objectives
- Use root cause analysis techniques to address the true causes of data quality issues
- Select the appropriate steps, activities, and techniques from the Ten Steps™ process to address business needs
- Incorporate data management topics such as data governance, data modeling, metadata, business rules, master data, reference data, and data standards into the process for ensuring high quality data
- Apply concepts such as the Framework for Information Quality and the information life cycle to data quality management
- Apply templates and examples to address their own data quality concerns
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 2nd 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.
Who It's For
Individual contributors and team members responsible for or interested in the quality of data in their business processes, systems or databases. This includes roles such as:
- Data Analysts
- Data Quality Analysts
- Business Analysts
- Data Designers/Modellers
- Data Stewards
- Business Process Modellers
- Application Developers
- Any data professional impacting the quality of data upon which their business depends
This class has also proven helpful for:
- Managers and project managers of individual contributors and team members.
- Who need to understand what is involved in addressing data quality because they hire resources, assign people’s time, provide support, and remove roadblocks to data quality work
- Users of data
- Whose work has been affected by poor data quality and want to find solutions for those problems, such as data scientists who have found themselves in a position of dealing with poor-quality data before they can start the “real” job for which they were hired
Speaker
Danette McGilvray
President and Principal
Granite Falls Consulting
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™ 2nd Ed. (2021, Elsevier/Academic Press). An internationally respected expert, her Ten Steps™ approach to information quality has been embraced as a proven method for creating, improving, sustaining, and managing information and data quality in any organization. Her book is used as a textbook in university graduate programs. The Chinese translation of the first edition 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 helped organizations 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.
With her holistic approach to data quality, Danette believes that data quality can save the world. She loves to work with organizations that want to increase their data literacy and build expertise in-house. She helps through consulting, training, one-on-one coaching, and executive workshops and presentations. Please contact her at danette@gfalls.com, connect with her on LinkedIn, and follow her on Twitter: Danette_McG.
IRM UK Public Course: Ten Steps to Data Quality
Testimonials
“It exceeded my expectations. Prior to he course, I was worried that it would be a densely packed course, heavy on theory. In reality the course was very accessible and the content was full of ideas I think can be applied in our area of work.”
Ian Marshall
Data Analyst, HMRC
“Really interesting course with a lot of useful and practical information. Good engagement throughout the course.”
Ben Cowley
Data Quality Lead, Yorkshire Building Society
“Very informative, lots of ideas and concepts to put into practice. Her knowledge on data is amazing. Keeping us focussed, on track and great at explaining.”
Maxine Williamson
Data Governance Manager, MOD
“Exceeded my expectations. We covered a huge amount, but it never felt overwhelming. I really liked the fact that the approach is flexible – the just enough principle is refreshing. Very clear and friendly. It was a great opportunity to learn from someone who clearly knows her stuff and has a lot of experience.”
Ed Bramall
Project Manager, Curo
“Course content very relevant to my role and will help me and my team to review and plan going forward. Danette is very knowledgeable about all aspects of data quality.”
Karen Radburn
Data Quality and Governance Manager, MOD
“Lots of tools and techniques discussed that I am sure I will use. Danette was very engaging and knowledgeable.”
Graham McKenna
Data Quality Lead, HMRC
“This concept of framework really matches up with our work environment. Danette was very clear explaining the material step by step.”
Ratih Anindita Wardhani
Data Analyst/Assistant Manager, Bank Indonesia
“Danette McGilvray was brilliant. I would definitely recommend this course to colleagues”
Graham Wall
Data Management Analyst, PageGroup
“Data and information challenges are well addressed, techniques will be helpful. Clear, excellent in execution and examples”
Simon Lebeau
Data Manager , Danone Research – France
“Danette McGilvray is very inspirational”
Radhia Ghanem
Data Quality Analyst , NHS PS, UK
“The course exceeded my expectations!”
Andrew Dickens
Data Manger , Land Registry, UK
“Fantastic course, very informative. Danette was an amazing lecturer.”
Roxanne Wells
Manager, Data Governance , FIS Limited – UK
“The course has helped me put into perspective and break down the areas of data quality that should fall under investigation in any project - the steps methodology ensures you have captured all the areas that affect data quality completely.”
Eirini Basta
Local Data Steward, Business Systems, HEINEKEN UK Limitedan
“Excellent from a management perspective. A good lecturer. Everything said was clear.”
Beibit Baktygaliyev
Application Support, Chevron
“Very easy to follow and apply to the work/challenges that I am going to face. Danette was great at using every day experiences and relating them to data quality. A great presenter who is open to new ideas irrespective of being in Data Quality for over 20 years.”
Naomi Thomas
Data Quality Analyst, Gocompare.com
Fees
- 3 days
- £1,195
- £1,195 +VAT = £1,434 (Price if you book by 18th May)
- 3 days
- £1,295
- £1,295 +VAT = £1,554 (Price you book after 18th May)
Group Booking Discounts
Delegates | |
---|---|
2-3 | 10% discount |
4-5 | 20% discount |
6 + | 25% discount |
Cancellation Policy:
Cancellations must be received in writing at least two weeks before the commencement of the seminar and will be subject to a 10% administration fee. It is regretted that cancellations received within two weeks of the seminar date will be liable for the full seminar fee. Substitutions can be made at any time.
Cancellation Liability:
In the unlikely event of cancellation of the seminar for any reason, IRM UK’s liability is limited to the return of the registration fee only. IRM UK will not reimburse delegates for any travel or hotel cancellation fees or penalties. It may be necessary, for reasons beyond the control of IRM UK, to change the content, timings, speakers, date and venue of the seminar.