On “Analytics” and related fields

I recently attended the INFORMS Conference on Business Analytics and Operations Research, aka “INFORMS Analytics 2011”, conference in Chicago. This deserves a little bit of an explanation. INFORMS is the professional organization for Operations Research (OR) and Management Science (MS), which are terms describing approaches to improving business efficiency by use of mathematical optimization and simulation tools. OR is perhaps best known for the technique of Linear Programming (read “Programming” as “Planning”), which is a method for optimizing a useful class of mathematical expressions under various constraints extremely efficiently. You can, for example, solve scheduling, assignment, transportation, factory layout, and similar problems with millions of variables in seconds. These techniques came out of large-scale government and especially military logistics and decision-making needs of the mid-20th century, and have now been applied extensively in many industries. Have you seen the UPS “We (heart) Logistics” ad? That’s OR.

OR is useful, but it’s not sexy, despite UPS’ best efforts. Interest in OR programs in universities (often specialties of Industrial Engineering departments) has been down in recent years, as has been attendance at INFORMS conferences. On the other hand, if you ignore the part about “optimization” and just see OR as “improving business efficiency by use of mathematical processes,” this makes no sense at all! Hasn’t Analytics been a buzzword for the past few years? (“analytics buzzword” gets 2.4 million results on Google.) Haven’t there been bestselling business books about mathematical tools being used in all sorts of industries? (That last link is about baseball.) Hasn’t the use of statistical and mathematical techniques in business been called “sexy” by Google’s Chief Economist? How could a field and an industry that at some level seems to be the very definition of what’s cool in business and technology right now be seen as a relic of McNamara’s vision of the world?

To answer that rhetorical question, I think it’s worth considering the many ways that organizations can use data about their operations to improve their effectiveness. SAS has a really useful hierarchy, which it calls the Eight levels of analytics.

  1. Standard Reports – pre-processed, regular summaries of historical data
  2. Ad Hoc Reports – the ability for analysts to ask new questions and get new answers
  3. Query Drilldown – the ability for non-technical users to slice and dice data to see results interactively
  4. Alerts – systems that detect atypical conditions and notify people
  5. Statistical Analysis – use of regressions and similar to find trends and correlations in historical data
  6. Forecasting – ability to extrapolate from historical data to estimate future business
  7. Predictive Analytics – advanced forecasting, using statistical and machine-learning tools and large data sets
  8. Optimization – balance competing goals to maximize results

I like this hierarchy because it distinguishes among a bunch of different disciplines and technologies that tend to run together. For example, what’s often called “Business Intelligence” is a set of tools for doing items #1-#4. No statistics per se are involved, just the ability to provide useful summaries of data to people who need in various ways. At its most statistically advanced, BI includes tools for data visualization that are informed by research, and at its most technologically advanced, BI includes sophisticated database and data management systems to keep everything running quickly and reliably. These are not small accomplishments, and this is a substantial and useful thing to be able to do.

But it’s not what “data scientists” in industry do, or at least, it’s not what makes them sexy and valuable. When you apply the tools of scientific inquiry, statistical analysis, and machine learning to data, you get the abilities in levels #5-#7. Real causality can be separated from random noise. Eclectic data sources, including unstructured documents, can be processed for valuable predictive features. Models can predict movie revenue or recommend movies you want to see or any number of other fascinating things. Great stuff. Not BI.

And not really OR either, unless you redefine OR. OR is definitely #8, the ability to build sophisticated mathematical models that can be used not just to predict the future, but to find a way to get to the future you want.

So why did I go to an INFORMS conference with the work Analytics in its title? This same conference in the past used to be called “The INFORMS Conference on OR Practice”. Why the change? This has been the topic of constant conversation recently, among the leaders of the society, as well as among the attendees of the conference. There are a number of possible answers, from jumping on a bandwagon, to trying to protect academic turf, to trying to let “data geeks” know that there’s a whole world of “advanced” analytics beyond “just” predictive modeling.

I think all of those are right, and justifiable, despite the pejorative slant. SAS’ hierarchy does define a useful progression among useful analytic skills. INFORMS recently hired consultants to help them figure out how to place themselves, and identified a similar set of overlapping distinctions:

  • Descriptive Analytics — Analysis and reporting of patterns in historical data
  • Predictive Analytics — Predicts future trends, finds complex relationships in data
  • Prescriptive Analytics — Determines better procedures and strategies, balances constraints

They also have been using “Advanced Analytics” for the Predictive and Prescriptive categories.

I do like these definitions. But do I like the OR professional society trying to add Predictive Analytics to the scope of their domain, or at least of their Business-focused conference? I’m on the fence. It’s clearly valuable to link optimization to prediction, in business as well as other sorts of domains. (In fact, I have a recent Powerpoint slide that says “You can’t optimize what you can’t predict”!) And crosstalk among practitioners of these fields can be nothing but positive. I certainly have learned a lot about appropriate technologies from my membership in a variety of professional organizations.

But the whole scope of “analytics” is a lot of ground, and the underlying research and technology spans several very different fields. I’d be surprised if there were more than a dozen people at INFORMS with substantial expertise in text mining, for example. There almost needs to be a new business-focused advanced analytics conference, sponsored jointly by the professional societies of the machine learning, statistics, and OR fields, covering everything that businesses large and small do with data that is more mathematically sophisticated (though not necessarily more useful) than the material covered by the many business intelligence conferences and trade shows. Would that address the problem of advanced analytics better than trying to expand the definition of OR?

5 thoughts on “On “Analytics” and related fields

  1. Siah

    Hi Harlan,

    I am a PhD student in OR and I agree that OR is not a sexy profession. What can I say, the most fascinating OR product that I have ever seen is Walmart. I cannot even believe that one can design a supply chain model that makes a computer mouse on the other side of the globe, hauls it all the way through the ocean, carries it with a train to the middle of the country, uses a worker to place it on the shelf and sells it to me for $2, finally accepts returns and still manages to make a large revenue. I find this more interesting and more fascinating than mapreduce, mahoot and R put together. But it is not sexy I cannot win the heart of a girl without disowning my profession and its products.

    But I don’t think it should be sexy for me to enjoy it. Lets face it OR is old. We can do linear programs now but the state of the art in terms of optimization is conic programing!. Thats a nasty piece of math and even I hate to do it. nonlinear programming hasn’t advance much either (remember BFGS? thats still pretty much the best we can do) Most of OR’s stochastic systems geniuses are now working on the wall street and justifying their coin tosses with advanced math. and there are not many jobs for those who study logistics. Making contributions in OR is hard, very very hard. I have seen smart people who came to our program with high ambitions and left empty handed and frustrated. You need to be George Dantzig to make a meaningful contribution (For those who don’t know him, he is the inspiration behind good will hunting. But I don’t think he ever had success with girls as much as Matt Damon had ).

    As a result not many people decide to study OR. But on the other hand we do not need hordes of OR experts like the way we need computer experts. There is not much jobs for OR experts. And I think if we can produce IE or OR engineers in a couple of top schools we can completely satisfy the demand from the industry.

    As for myself, having a degree in OR have really differentiated me from the pool of those who know machine learning, or computer programming (I once got a degree in CS) I think it has helped me tremendously to find interesting projects that are related to CS but still have a flavor of systems, machines and people. I recommend it for everyone who thinks about processes, and math and would like to also get exposed to programming and machine learning and stats.

    By the way, I also believe that INFORMS new campaign to call it analytics is ridiculous. Many people in INFORMS are also involved with companies like IBM. I feel we see more and more of IBM’s campaign slangs popping up in our community and that is not just wrong. I won’t be surprised to see INFORMS changing “OR” to science of “Smart Planet” very soon 🙂

    Thanks for contributing to the community 🙂

  2. Michael Trick

    “Interest in OR programs in universities (often specialties of Industrial Engineering departments) has been down in recent years, as has been attendance at INFORMS conferences.”

    While I am not sure of the first part, the second part is absolutely false. Practically every year has more attendance at INFORMS than the previous year. At Pittsburgh in 2007, we had 3700 attendees (an all time record at the time) while Austin in 2010 had close to 5000.

    There has similar growth at the EURO (European OR) meetings. Conferences that used to be around 1000 people a decade ago now attract 2000 or more.

    There may be issues with the growth of the field, but conference registration is not one of them.

    As for the main thrust of the article, I’m going with “trying to let “data geeks” know that there’s a whole world of “advanced” analytics beyond “just” predictive modeling.”

    1. Harlan Post author

      Thanks for the reply and clarification. I don’t have complete information, but it is my understanding that conference registration numbers have been flat recently (with the exception of this year’s Analytics conference, obviously). Certainly that is the impression I got from presentations and discussions.

  3. Michael Trick

    I think you are confusing conference registration with INFORMS membership (or the presenters and discussers were similarly confused). See http://www.me.utexas.edu/news/2010/1210_informs.php for the 2010 number and compare with, say http://www.lionhrtpub.com/orms/orms-12-02/frinside.html for the number 8 years ago.

    It is a conundrum on why membership is flat or declining (down perhaps 20% in the last 8 years) while conference registration is up 40-50% in the same period. There is a “Bowling Alone” effect that affects all professional organizations (from the book by Putnam) but I think there is something more than that.

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