John Thompson

Adjunct Professor
Lake Forest Graduate School of Management
September 27, 2022

Currently, I create and teach classes on AI/ML and advanced analytics at Lake Forest Graduate School of Management. I have been teaching classes in both the degree base program and the Executive Education based program. Classes have included students who work at - GrubHub, Abbott, State Farm, Allstate, and numerous other enterprise class organizations.

Adjunct Professor
Illinois Institute of Technology
September 30, 2008

I taught in the undergraduate Engineering School. The classes were focused on teaching Engineering students interdisciplinary collaboration and decision making. One semester, the team wrote business plans and in another semester the teams built early stage Virtual Reality prototypes

Advisory Board Member - Masters & Undergraduate Programs in Business Analytics
University of Texas - Austin
September 30, 2022

My role is to act as an advisor and consultant to the university administration, faculty, and staff about the Masters in Business Analytics program. We discuss curriculum, classes, engagement with companies and other ways to continually improve the business analytics program for the students, university and the companies that hire and interact with the students and the university.

Advisory Board Member - Masters in Data Science Program
Oklahoma State University
September 30, 2022

I am a member of the analytics advisory board. The mission of this board is to foster and support excellence in marketing analytics and data mining education at Oklahoma State University. The board serves as an advisory resource for the marketing analytics and data mining programs regarding such matters as mission, strategy, curriculum, external activities, student recruiting, student placement, internships, fundraising and new initiatives.

How I Got My Company – and Myself – Involved with the Mark Cuban Foundation AI Bootcamp
LinkedIn
October 22, 2021

When I was in college, there were sci fi movies and stories about artificial intelligence (AI), most of them focused on the scary future that was to come when the computers took over! Today, 37 years later, I am the Global Head of Artificial Intelligence and the Rapid Data Lab for CSL Behring. And now, over the next four weekends, I’ll be facilitating an AI bootcamp for the Mark Cuban Foundation for 20 high school students in the Philadelphia area.

Causal Artificial Intelligence: The Next Step in Effective Business AI
Amazon
October 03, 2023

In Causal Artificial Intelligence: The Next Step in Effective, Efficient, and Practical AI, a team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book’s discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings.

Useful for both data scientists and business-side professionals, the book offers:

Clear and compelling descriptions of the concept of causality and how it can benefit your organization
Detailed use cases and examples that vividly demonstrate the value of causality for solving business problems
Useful strategies for deciding when to use correlation-based approaches and when to use causal inference
An enlightening and easy-to-understand treatment of an essential business topic, Causal Artificial Intelligence is a must-read for data scientists, subject matter experts, and business leaders seeking to familiarize themselves with a rapidly growing area of AI application and research.

Data for All
Manning
May 23, 2023

Data for All, will be published in the Fall of 2022. Written for the general public, the book is an outline of how the world of data works today and how it will work in the near future. Described as a “Revolutionary Manifesto for Data”, the book instructs people in how to protect and manage their data and the data of their loved ones. Data for All communicates how to do this in applied and practical terms.

Building Analytics Teams: Harnessing analytics and artificial intelligence for business improvement
Packt Publishing
June 30, 2022

John is the author of the bestselling book – Building Analytics Teams: Leveraging analytics and artificial intelligence for business improvement. The book was published in June 2020 and outlines how to hire and manage high performance advanced analytics teams. The book outlines how to engage with executives and senior managers. How to select and undertake analytics projects that change and improve how a business operates.

Analytics: How to win with Intelligence
Technics
March 31, 2017

John is co-author of the bestselling book – Analytics: How to win with Intelligence, which debuted on Amazon as the #1 new book in Analytics in 2017. Analytics is a book that guides non-technical executives through the journey of creating an analytics function, funding initiatives, and driving change in business operations through data and applied analytical applications.

Executive Fellow, Woxsen University
Woxsen University
October 19, 2022

Founder
Marketing Sciences, LLC.
April 02, 2003

Marketing Sciences is a consulting firm focused on - Analytics, Artificial Intelligence, Data Monetization, Data Integration and Architecture, Internet of Things, and leveraging data for competitive advantage.

We help companies increase and improve - patient persistence and compliance, manufacturing yield, sales, marketing; to build compelling products, expand international operations profitably, and how to build new revenue streams from data.

Data for All with John K. Thompson
Practical AI
October 18, 2022

People are starting to wake up to the fact that they have control and ownership over their data, and governments are moving quickly to legislate these rights. John K. Thomson has written a new book on the topic that is a must read! We talk about the new book in this episode along with how practitioners should be thinking about data exchanges, privacy, trust, and synthetic data.

VIDEO | CSL Behring Global Head, AI and Rapid Data Lab: We Bring in Younger People
CDO Magazine
September 22, 2022

John Thompson, CSL Behring’s Global Head, AI, and Rapid Data Lab, speaks with Salema Rice, Global Managing Director, Applied Intelligence, Accenture (and CDO Magazine Editorial Board Member), about the challenges facing analytics leaders, the need for including younger minds on teams, and AI’s real-world application.

Thompson says that the biggest challenge for him as an analytics leader is getting the message across and understood by C-level executives. This is mainly because many C-suite executives did not start their careers with computers, analytics, or data, he adds.

He stresses that organizations must do whatever it takes to become entirely data-driven. Thompson reveals that CSL Behring has been working with multiple educational institutions to bring in interns as young as first-year students and other undergraduates to work with them on capstone projects, co-op projects, and internships.

According to Thompson, CSL Behring puts in a lot of effort to bring in younger people. He has been working with the Mark Cuban Foundation on AI boot camps, and is reaching out to participants as young as high school students. He hopes to get into elementary schools to get kids to understand the value of data, AI, and STEM.

Thompson goes on to share examples of AI use cases where technology helps make the world a better place. He points toward an application developed while he was at Dell with the University of Iowa Hospitals. The team built an AI environment that monitored patients in the operating room. It gave surgeons feedback about the heat, humidity, procedure, patient, and vitals. And, it predicted if a patient had a higher probability of developing post-surgical sepsis. Over the three years of using the system, it reduced the incidence of sepsis by 74%, saving many lives.

Analytics in Pharma - Patients, Productivity & Price
DATAcated
May 02, 2022

The DATAcated Conference is a free, virtual ‘data party’ hosted by Kate Strachnyi. This is the third DATAcated Conference – it has an industry focus and covers financial services, healthcare, energy, retail, sports, and food & beverage. In this interview, Kate and John talk about analytics in the Pharmaceutical industry.

The Future of Data
Lights on Data Show
April 04, 2022

John Thompson is a 2 x Best Selling Author, Keynote Speaker, and Innovator in AI, Data & Advanced Analytics. As a senior technology executive leading Data Science & Product teams is the best one out there to reveal to us what the future of data holds.

How to Build a Data Science Culture
The Artists of Data Science
June 04, 2021

In this interview, Harpreet Sahota and John talk about how to start, build, manage and grow a data science team and culture.

Empowering data scientists to find creative solutions
Alteryx
April 06, 2021

How can leaders encourage data scientists to experiment? Best selling author John K. Thompson joins us to share tips, including the importance of having a psychological understanding of what drives data scientists.

Building Analytics Teams
Ternary Data: Data Engineering Consulting
January 05, 2021

In this interview, Joe Reis and John talk about how to start, build, manage and grow a data science team and culture.

Building Successful Analytics Teams - with author John K. Thompson
Bernard Marr
September 04, 2020

Bernard Marr's Future of Business & Technology Podcast

In this conversation with author and technology executive John K. Thompson we will explore how to build successful analytics teams in companies. We will explore what makes a high-performing analytics team, the key mistakes to avoid and the important steps to create and grow a team that will successfully deliver your data and analytics projects.

How to Build Effective Analytics Teams
Story by Data
September 01, 2020

Join the LinkedIn live session on how to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization. In today’ session you’ll hear from John Thompson, author of Building Analytics Teams. You’ll also have a chance to win a copy of the book! We are giving away 3 books at the end of the session for Best Questions Asked

Executive Advisory Board - AI, Data Science & Computer Science
Oakland University
September 30, 2022

I serve on the Executive Advisory Board (EAB) for the Computer Science department. I work with other members of the EAB, and the administration and faculty members at Oakland University to:
1. Review proposed curriculum additions, and revisions
2. Suggest improvements and changes in classes and offerings
3. Discuss areas of focus for classes and research to improve the relevancy to students and industry

The EAB is an external group that helps refine and improve the offerings and educational experience for students while they are at Oakland and to help in attracting students from a global population.

Board Of Advisors - Data Analytics Programs & Curriculum
Ferris State University
September 30, 2022

My involvement includes working with professors, administration, and students to ensure that the Analytics programs, including - Data Analytics, Business Data Analytics, Data Science and Analytics, and Advanced Studies in Data Analytics are constantly improving and tracking with the needs of employers, industries, governments and other organizations that want and need to employ data science, data engineering and analytics professionals.

Put Generative AI to Work
Packt
October 11, 2023

topics; Data Engineering, Data Quality, LLM security, LLM tooling, EinsteinGPT, Responsible AI, GenAI for Managers
Immerse yourself in power talks, panel discussions, fireside chats, and hands-on tech sessions that deliver cutting-edge insights on Generative AI and LLMs, directly from the industry's foremost minds.

Creativity vs. Practicality have data scientists lost the plot!
datazuum
September 29, 2022

Companies must begin to consider regular people as part of their data strategy. Data science teams must work with regular people every day to get a sense of their problems and opportunities, as well as their hopes and fears about data, before focusing on equipping people with the tools they need to formulate and solve their own problems.

Currently, when talking about Artificial Intelligence (AI) in the context of enterprise class, commercial companies, AI is more of an art than a science. Why is this the case?

AI is a part of Data Science. It says it right in the name, Data Science. The science element of Data Science and AI is a moving and evolving target; that is not a bad thing, it is just the state of the art today and a fact that we should understand a bit better.

The art of analytics, including Data Science and AI, is due to a number of factors, including:
1. AI is a relatively new discipline - the math, technology, and tools to build, manage and operate an AI enabled infrastructure are nascent and rapidly evolving.
2. The skilled professionals who can effectively and efficiently conceive, build, and maintain AI applications are in limited numbers and those professionals are trained in a number of different schools of thought and approaches to the craft.
3. The nascent approaches that are showing promise in research and academic labs need to evolve to work in commercial settings.
The amount of innovation that is being developed is promising and exciting, and provides a conceptual foundation for commercial software, robust models, and applications, but there is more work to be done before we can move from art to science.

Today, the effectiveness and efficiency in the art of Data Science and AI, is substantially dependent on the skill and ability of the data scientists or machine learning engineers involved. Specifically, it is reliant the ability of those professionals to effectively conceive, design, build and execute the feature engineering phase of their projects.

Much of the success of current feature engineering work comes down to mastery of statistics in general and creativity with using correlation in specific.

Let’s discuss how Correlation is used today in AI and Data Science projects.

What is Correlation and how do we use it today in Data Science/AI

Let’s define Correlation.

Cor·re·la·tion /?kôr??l?SH(?)n/ - a mutual relationship or connection between two or more things.

Data Scientists work with a broad set of features that are correlated to the variable or the measure that they seek understand, monitor, or predict. There may be thousands of possible variables that are correlates of the target measure, actually, in some cases, there may be millions, and possibly billions.

The art of the analytics process, and to be clear, there are very few people who are highly skilled at this part of the AI and Data Science procedure, is to determine which of those thousands, or millions, or maybe even billions, of variables will accurately, robustly, and reliably, predict the actions and behaviors of the target variable or variables in close proximity to the observed behavior in the real world.

In 37 years, I have worked with a small number of people who are world class in this area (less than 20). If AI and Data Science are to grow and be an integral part of every leading company, we cannot rely on a process that only a handful of experts can execute.

That is why we need to move from Correlation to Causation

Let’s define Causation.

Cau·sa·tion /kô?z?SH(?)n/ The relationship between cause and effect; causality.

Simply moving from correlation to causality does not change the AI and Data Science process into a repeatable, rigorous scientific process. It will take more than a change in focus or methodology to get us there.

We are at an inflection point of where the causal algebra is being proven in academic research labs today. Those academic efforts are being quickly followed by early-stage commercial companies that are building software and tools to leverage the research innovations so that companies can implement causal feature engineering.

Developing the appropriate and purpose-built: math, tools, and technologies is what will enable the changes needed to replace the current fallible feature engineering process with a science-based process. In doing so, a significantly wider population of analytics professionals can undertake the task of developing casual based analytic applications on a reliable, repeatable, and scalable basis.

Casual algebra is being built into tools appropriate for Data Scientist and Machine Learning Engineers. These tools and applications will improve upon and replace the current process of searching for correlates on a hit and miss basis with a measured, controlled process to look at each possible feature and combination of features to ensure that our causal features, models, and application are the best that they can possibly be.

This new and improved process will look at every feature, every combination and test them against the target and provide intelligence and transparency into the fit for the objectives of each project. The process will result in a set of features that we know to be the best available, not just the best we happened to find.

Why is Causality the next step in our Data Science/AI journey

The ability to move from the current highly variable process that is dependent on a small number of highly skilled professionals in severely limited numbers; to a scalable, repeatable process that is available to all analytics professionals that produces scientifically verifiable casual factors in relation to the phenomena that we want to understand, predict, and influence, now, that is a game changing development.

Moving from an environment where we think we have done the best job possible in finding the optimum features, to one where we know that we have the optimum features is a defining development that will have an impact in every indusryt and company that is leveraging Data Science and AI.

We will still need trained and skilled analytics professionals, there is no question, but with the coming advances into Causal AI, we will be able to bring additional people into the analytics process and still maintain a high level of confidence that we are building robust, reliable, flexible model and applications. We can grow the AI and Data Science fields at a much faster rate.

Causal AI not only provides the data science and AI communities with a verifiable scientific process, but it also brings us one step closer to Explainable AI (xAI).

xAI is the ability to explain what our AI models are doing, how they learn, and why they make the decisions that they make. This opens our ability to use our most powerful analytical techniques on all analytical problems across all industries.

Until we have xAI, we cannot use our most advanced techniques in regulated industries like pharmaceuticals, insurance, retail banking and more. Causal AI will accelerate the development of xAI.

The Future of Data

Location: Virtual    Fees: 35,000

Service Type: Service Offered

In this keynote address, we talk about impending changes in how we use, control, and monetize our data. In the next 2 to 5 years each individual will be able to manage and control their personal data.

During this discussion, you will learn about:

? The types of data you generate with every action, every day
? Where your data is stored, who controls it, and how much money they make from it
? How you can manage access and monetization of your own data
? Restricting data access to only companies and organizations you want to support
? The history of how we think about data, and why that is changing
? The new data ecosystem being built right now for your benefit

We discuss the true nature of data ownership, and how you can turn your data from resource that others benefit from into a financial asset for your benefit.

Up to now companies have had free rein to log every click, purchase, and “like” you make, and to earn money from your data. But across the globe new laws have been written, passed, and are coming into force that give you—the individual—the right to access, delete, and monetize your own data. This session outlines how you can use these new laws, regulations, and services to directly benefit from your data in new and lucrative ways.