INFORMS Open Forum

  • 1.  Explaining our field to laypeople

    Posted 11-29-2022 15:29
    Edited by Beverly Wright 11-29-2022 15:38
    Hi all, INFORMS asked me to help create content to help explain basic concepts of the data science field to a non-INFORMS audience. Questions like: What is data science? What is machine learning? What is artificial intelligence? (And how do these things differ?) I thought it would be helpful to crowdsource some ideas on this. What are the right questions to answer? How do you explain these things to someone who has no background in them? Think on the level of an undergraduate or maybe someone planning to go to college soon, who is considering potential paths, but hasn't necessarily taken advanced math or statistics courses yet. I appreciate your input on how you would describe the data science field!


    Beverly Wright, PhD, CAP® (Certified Analytics Professional)

    Head - Data Science Solutions, Burtch Works

    Chair, Analytics Society, INFORMS

    Chair, Data Science & Analytics - FOR GOOD Initiative, Technology Association of Georgia

    Academic Director - Data Science, University of Georgia

    Podcast Host, TAG Data Talk

    C: 770 823 2396

  • 2.  RE: Explaining our field to laypeople

    Posted 11-30-2022 03:12
    Hi Beverly, I took a stab at this problem at "Artificial Intelligence (AI), Data Science, and Analytics" where I've used broad-brush categorizations to try to reduce the confusing overlaps and interplays.

    Rahul Saxena

  • 3.  RE: Explaining our field to laypeople

    Posted 11-30-2022 16:03
    Edited by Donna Ehrlich 12-01-2022 09:56
    Thank you for sharing this link, the explanations are excellent, I appreciate the way you organized the complexity into applicable demonstrations that link it to business. 


    Donna Ehrlich
    Associate Professor, Business Analytics
    Park University
    New Port Richey FL

  • 4.  RE: Explaining our field to laypeople

    Posted 12-01-2022 15:38

    Below I will share what I wrote for an undergraduate major in business analytics, having a little light hearted fun with it:

    Data is being collected everywhere-- my phone and my watch collect more data than I ever thought possible! So, what? why are all these devices collecting this data to tell me what to do; how many steps to take; and what to do next and when? What and why does all this data need to be collected and if it is collected, then what? The more data that is collected the better; really-- why?

    Well, that is what we are exploring in the Business Analytics Major…. as learners and practitioners the more relevant data we have, the more information we have; and with this we manage at a level that improves performance-anything from business management to overall wellbeing in a number of areas like healthcare, business management, computer science, athletics, and any industry that desires analyzing complex data for sound decision-making .

    Program overview:

    Business Analytics Major is offered through the business administration program. The Business Analytic Major is designed to prepare students for applying analytical and problem-solving thinking and skills in an organization. The purpose of the program is to develop students' knowledge base of the current analytics as well as problem-solving skills within the functional areas of an organization.

    In other words, this program is to assist in being able to take this data and explore through the lens of analytics how to convert it to real meaning, to save time, to enhance our lives, our organizations, and to be able to win at Fantasy Football, well at least maybe have a better chance.

    Maybe not exactly what you were looking for but thought I would share ...

    Donna Ehrlich
    Associate Professor, Business Analytics
    Park University
    New Port Richey FL

  • 5.  RE: Explaining our field to laypeople

    Posted 12-02-2022 16:17
    Thanks for getting this conversation started @Beverly Wright, and for your answers @Rahul Saxena and @Donna Ehrlich! I'll let those with more experience speak to Machine Learning & AI, but I can speak to Data Science and a couple other terms.

    To me, Data Science is a form of storytelling. We use information -- about the world around us, or from simulated worlds we create -- to describe and explain what's happening, how it's happening, and why it's happening. But at the end of the day, the goal is informing people through descriptions and explanations, which is why I call it storytelling.
    Another term I'll add is Management Science which uses data science to inform decisions that affect things we value. These could be decisions about manufacturing lines, the organizations we create, or even public policy. But those decisions don't happen in a vacuum. Management science is about how decisions affect what we value -- people, the environment, relationships, and knowledge, just like they can affect productivity and profits.

    I'll end with this: I'm not sure there is a "right" question or a "right" answer to any of this. We're all humans learning more, shaped by our experiences and value. But asking questions that pique our curiosity and staying open to new answers sounds helpful to me!

    John Meluso
    Sloan VERSO Postdoctoral Fellow for Systems, Organizations, & Inclusion
    Vermont Complex Systems Center
    University of Vermont

  • 6.  RE: Explaining our field to laypeople

    Posted 12-02-2022 23:00
    I am sorry, I have no slick explanation of the difference between machine learning and AI.  Anyway, my interest is in developing universal paths to bring "ignorant" people to the desire to learn. I probably will regret contributing to activity (the human version of "don't feed a stray cat - it will soon adopt you) but here goes.
    1. If the audience is "ignorant" (no math, no statistics, no background) the introduction of this subject matter should be in terms of "problems you may have to face or wish to solve," under the cloud of the universal hatred for Math, but softened with a sprinkling of humor i.e. showing these are FUN topics. At the end of the day and irrespective how massive computers and complex algorithms you may play with, these domains rely on the human/analyst. Think like a drug dealer: Offer a small sample of the "good stuff" and get him hooked. 
      You were alive three days ago, you were alive yesterday, you are alive today. What does the data tell you? I will live forever! (welcome to Regression, you failed!) 
      1. What is the real answer? Have an open ended conversation; talk about the "behaviors" of the data science methods: training machine learning, other approaches, etc.) 
    2. Recommend books, articles that present interesting topics and twists in thinking 
      1. Variety: All these topics , plus optimization, etc. apply to all kinds of fields  (Freakonomics?),  i.e. you are not condemning yourself to a tenured life in the math department at the University of DeadEnds. 
      2. Means of abusing the subject matter (How to lie with statistics).  Remember that most learners will use their dictionary first to look up all the dirty words they know translated to the new language; the same applies here: you are teaching a new language.
      3. There is no "bad method" just bad practitioners (analysts) - At the end of the day, the quality of the output is dependent on the talent of the human 
    3. In my work (still working at age 71+) I have yet to encounter a young person who is not comfortable in the various domains and topics you mentioned; but they do have other challenges.
    4. In the classroom: They are being educated or trained by "experts in their field," an exposure that implies that the instructors and everything they present is absolutely TRUE. 
      1. Have they ever heard their professors include caveats in the application of the method being taught? 
      2. Did the "expert" utter the phrase "If you try the brilliant method XXX to solve the problem YYY, XXX will suck big time" 
      3. Did you ask the expert what were the weaknesses of the method being discussed? Did you get a good answer?
    5. Starting a new career/job: Their insecurity appears when they are asked to differentiate these items. 
      1. Which of these subject matters is/are applicable to this specific issue/problem/challenge? 
      2. Do you expect to have a successful outcome from the analysis you performed? ("The computer says..." is NOT a successful outcome)
      3. Have you considered the possibility that your effort will fail? Why? How? Why are you willing to risk your career if you fail?
    6. In real life: How comfortable are they with real life decisions?
      1. Do they have the courage to invoke an "old school method" rather than one of the pearls of data science? or vice-versa?
      2. Do they understand budget and schedule limitations? (do you have the time/money to train a machine, test an AI system, etc.)
      3. Have they read articles discussing the pathetic ROI of some "advanced" methods, and pondered about the root causes? Will they make the same mistakes? (The "I have a Ferrari but I still did not get the girl" syndrome)
    Besides providing "judgment" the human analyst is the essential "translator/story teller" - however cold and disciplined you wish to cim math or data science or engineering or physics to be, at the end the human has to explain it to other humans, take the kudos of a job well done and the mocking if he/she/they fails. In other words, it is humanistic. 

    I apologize if my focus is more on heresy, cynicism, humor etc. but I believe that we all traverse many mental states - naive and hungry for information (<6 years old), boredom (6 - 15), stupid parents (15 - 18), conqueror of the world (18 - 22), recalibration (22 - 30), sobriety (30-55), survival (55 - 70), resentment (70 - ?), and we all can use tools that will help us on our trek. Note that the recalibration of the parents matches the child pummeling them with demands - nursing, diapers, questions, possessiveness. Sobriety and survival phases of the parents match the boredom and stupid parent stages of the children. The take-away, I suggest, is that HUMOR is the single factor that will smooth the stress we endure in each of the stages. 
    Your students will be happy to spend time and energy in the Court of the Naked Emperor; not so much in the presence of the Emperor who has imaginary clothes.

      Alex Loewenthal
      Staff Systems Engineer
      Northrop Grumman Defense Systems
      Westlake Village CA

    1. 7.  RE: Explaining our field to laypeople

      Posted 12-03-2022 16:13
      As an O.R. by heart and, mostly, Data Scientist by practice what I've found is a great lack of understanding, or even ignorance, not of Data Science but of O.R.. There are maybe a million of resources online teaching Data Science but very very few teaching O.R. (disregarding the fact that O.R. is not part of the typical Data Science toolbox). The consequence of this, is that companies entering the field of analytics have placed all their efforts in hiring Data Scientists pretending that they have all the skills needed to solve the variety of problems presented to them when, in reality, as most likely everyone in this forum is aware of, there are certain problems where O.R. approaches are more appropriate or provide a particular edge. Moreover, I see a missconception among Data Scientists that all what O.R. is about is optimization and simulation, ignoring the foundational elements behind these two specific disciplines. These observations, I would say, apply across most of industry (with the exception, of course, of the few top players most of us easily recognize). So, I do think that at the individual level but especially as a professional organization in INFORMS we need to make a considerable effort to bring more visibility and better understanding of our field to the broader public.

      Sr Data Scientist
      Austin TX

    2. 8.  RE: Explaining our field to laypeople

      Posted 12-04-2022 22:22
      Aineth (and others)

      I agree that O.R. does not get the same respect as data science.   But OR and Data Science both are missing a crucial step in the beginning which to frame the problem and make sure we are solving the right problem.   We need to understand what decisions need to be made which leads us to what data needs to be considered.   What uncertainties need to be assesses perhaps using historical data, or simulation or possibly just assessed by a Subject Matter Expert (SME).   Developing the frame and sharing with the decision maker before a lot of data collection or modeling will result in a better decision faster.   There are a number of good Decision Quality (DQ) books.   One is "Decision Quality" by Carl Spetzler.  An older book is "Why Can't You Just Give Me The Number" by Pat Leach that discusses both the framing and quantitative methods of Decision Analysis.

      Decision Scientist Consultant
      Self Employed
      Fremont CA

    3. 9.  RE: Explaining our field to laypeople

      Posted 12-05-2022 23:35
      Well put, @BRIAN PUTT, about the need to understand and frame decisions. This step requires the breadth and depth of knowledge that makes our field a lifetime profession.

      Several weeks ago a good friend told me that he had paid good money to enroll in a "data science and business analytics" course that mainly taught him to use programming tools like Python, and nothing about decision-definition.

      The summary that @Donna Ehrlich shared is a great way to introduce our field. The next step (i.e., "this door opens into ...") can be a public "list of topics" that we work on. For instance, it would be great for INFORMS to publish the table of contents of the INFORMS Body of Knowledge as a starter list, and then for domain-communities to create domain-lists. For example, I started a list for Decision Analytics at

      I can imagine that the third step can be a public-domain exploration of analytics as a set of papers (nodes) where references are arcs. This will be quite bushy and I would think that OR/MS people would find it interesting.

      Rahul Saxena

    4. 10.  RE: Explaining our field to laypeople

      Posted 12-08-2022 10:28

      The questions raised in Beverly's post (or some modified versions of these same questions) are ones that I have heard many times throughout my career as a leader in OR and Analytics. They are not easy questions to answer and the question, "How do these things differ?" is even harder to explain in a few simple sentences. There is no perfect answer. I have answered them over the years along the following lines, based on my view of the different fields.

      I will first add one more term to the discussion about Data Science, Machine Learning, and Artificial Intelligence: Analytics.

      Analytics is a set of mathematical modeling methods and tools used to derive value and insight from data depending on the business problem at hand. They include different techniques that vary from the type of data that is being analyzed and the types of problems you wish to solve and are included in different disciplines and fields of study: Statistics, data mining, forecasting, optimization, simulation, quality control, multiple areas of operations research and others. These tools help you gain insight about the past and discover relationships (descriptive analytics); predict future outcomes and discover patterns based on available data (predictive analytics); and recommend optimal decisions (prescriptive analytics). As the volume and variety of data available continued to grow, several newer techniques continue to be added to this arsenal of tools to tackle different forms of data including text data, audio and video data, digital images, etc.

      While Analytics typically refers to the tools for data-driven decision making, Data Science includes a deeper knowledge of the collection and management and storage of data as well as the complete life-cycle of deriving insight from data to solve a variety of business problems and continuously monitoring the incoming and changing data to adjust the models as needed. One can also think of Data Science as the science of data-driven decision making using the appropriate set of analytical tools from the complete arsenal of tools available to the modeler.

      As data volumes continued to explode, computing power has also been growing at immense rates along with the business need for faster decisions for more complex problems requiring the automation of several of the analytical modeling tools used in data mining, predictive modeling, forecasting, text analytics, image recognition, etc. In simplest terms, Artificial Intelligence is the science of training machines or computers to perform human tasks automatically and at fast speeds. While there are multiple definitions of Artificial Intelligence, one that resonates with me is:

      Artificial Intelligence is the science of "automated data-driven decision making at scale" which requires several enabling technologies and a framework for implementation.  All the science and math that we do are just various arrows in our quiver, or tools in our toolbox: Forecasting, machine learning, optimization, predictive modeling techniques, natural language processing, image recognition, etc, etc. The distinguishing aspect of AI is the ability to do all the processing at super-fast speeds as well as tackle large volumes of data that was not possible before. This ability helps us make advances like automated decisioning for self-driving cars, classify thousands of images enabling quicker diagnoses of skin cancer and other diseases, create the best predictive models for hundreds of micro-segments and many other tasks that benefit from super-fast processing of large amounts of data. Note that even though we have heard the term artificial intelligence for several decades, AI has become a household word today because of the perfect trifecta: availability of massive amounts of data, advanced and innovative algorithms and tremendous advances in computing power and storage.

      Machine learning (ML) includes a class of powerful analytical modeling tools that typically "learn" from data to determine patterns, predict future trends, extrapolate to other members of similar populations, etc. Most ML methods can do this with multiple types of data including images, audio and video files, text data, etc. Quite often ML techniques are also used to augment the insight / value derived from other analytical modeling approaches. These tools are an essential part of the "magic" that makes artificial intelligence systems work!

      Do these definitions and descriptions seem to be very similar and are you wondering what is the difference? You will often see many of these terms used interchangeably. The reality is that there is a great deal of overlap in their definitions. I like to think of these four fields in the following way:  Analytics and Machine Learning refer to different types of tools that derive insight from data; Data Science uses these tools in addition to a framework of implementation to make and deploy data-driven decisions; Artificial Intelligence is highly scalable automated data-driven decision making that uses an arsenal of appropriate modeling tools and large volumes of data to mimic human behavior.

      For a good overview of these different areas and related examples, please see the following pages:

      Radhika Kulkarni
      Vice President, Advanced Analytics R&D (retd.)
      SAS Institute Inc. (retired)
      Durham NC