Month: May 2011

So What Correlates with Operations Research?

Google Labs has a new tool called Google Correlate. Google provided some early correlation results during the 2008 flu season when it showed that search count for certain terms (like “flu” presumably) could be used to estimate the prevalence of flu in an area.  This led to Google Flu Trends (it appears that currently only South Africa has many cases of the flu).

You can now play this game on your own data.  Have a time series over the last 9 years or so?  You can enter it into Google Correlate and see what search terms are correlated with the data.

Even easier is just entering a search term:  it will then return other correlated search terms.

If you are going to periodically write in something called “Michael Trick’s Operations Research Blog”, it is clear what to do next:  search on “Michael Trick” (it is required to egotistically search on your own name first, right?).  No dice:  I’m not popular enough to justify a search (sigh…).

But, of course, “operations research” works fine.  What  correlates with that phrase?  Turns out lots of interesting things:  “signal processing”, “information systems”, and … “molecular biology”?  What are the common features on these terms?  Well, they were relatively more common search terms in 2004-2005, relatively flat in the past three years, and have a strong seasonality, corresponding to the start of the academic year (“Hey, I signed up for Operations Research:  what the heck is that?”).  Whether it is operations research, signal processing or molecular biology, it appears lots of academic departments begin September with students frantically searching on their subjects.

We can try another term with some currency:  “business analytics”.  The result is somewhat surprising.  “Thank you email”?  “Vendor portal”?  “Zoes Kitchen”?  It seems hard to make much sense of this.  As we know, “business analytics” is a relatively new term and the search quantity is less than that of “operations research” which perhaps explains the spurious correlations:  there are so many terms that are searched as often as “business analytics” that the highest correlations come more or less randomly.

To data people like us (me, anyway), the ability to search correlations is endlessly fascinating.  Shift the operations research time series by 13 weeks and what do you get:  things like “portable mp3” and “retriever pictures”:  clearly our students are bored with our course and are surfing around for something more entertaining.  What does “management science” search correlate with?  “introduction” and “social research”.  Is there anything interesting to be learned by the differences in correlates between operations research and management science?  Nothing springs to mind, but there might be a thesis or two there.

I am not sure what any of this means, but it sure is a great way to spend an early summer afternoon!

 

 

Business Analytics and Operations Research: Tomato, To-mah-toe, Tractor!

There are few things in life more tedious than assigning boundaries to fundamentally ill-defined concepts.  Either terms are used to divide things that cannot be divided (“No, no, that is reddish-purple and clearly not purplish-red!”) or are used to combine groups while ignoring any differences (Republicans?  Democrats?  just “Washington insiders”).  Arguing over the terms is fundamentally unsatisfying:  it rarely affects the underlying phenomena.

So when INFORMS (Institute of Operations Research and the Management Sciences), an organization of which I am proud to have been President and equally proud to be Fellow, embarks on its periodic nomenclature debate, ennui overwhelms.  Not again!  The initial debate between Operations Research and Management Science resulted in two societies (ORSA and TIMS) for forty years before they combined to form INFORMS in 1995.  Decision Engineering, Management Engineering, Operations Engineering, Management Decision Making, Information Engineering, and countless other terms have been proposed at times, and some have even made toeholds in the form of academic department names or other usages.  None of this has fundamentally changed our field, except perhaps in confusing possible collaborators and scaring off prospective members (“Wow, if they don’t even know who they are then maybe I should check out a more with-it field!”). I decided long ago to just stick with “operations research” and make faces of disgust whenever anyone wanted to engage the issue of the name of the field.

Then, three years ago (only! check the google trends graph) the phrase “business analytics” came along, and it was a miracle!  Here was the phrase that really described what we were doing:  using past data to predict the future and make better business decisions based on those predictions.  That’s us!  And, due to books such as “Competing on Analytics”, the wider world actually were interested in us!  There were even popular books like “The Numerati” about us.  We were finally popular!

Except it wasn’t really about “us” in operations research.  We are part of the business analytics story, but we are not the whole story, and I don’t think we are a particularly big part of the story.  A tremendous amount of what goes by the name “business analytics” are things like dashboards, business rules, text mining, predictive analytics,OLAP, and lots of other things that many “operations research” people don’t see as part of the field.  IBM’s Watson is a great analytics story, but it is not fundamentally an operations research story.  People in these areas of business analytics don’t see themselves as doing operations research.  Many of them don’t even identify with business analytics but rather with data mining, business intelligence, or other labels.   All of this involves “using past data to help predict the future to make better decisions” but “operations research” doesn’t own that aspect of the world.  There are lots of people out there who see this as their mandate but haven’t even heard of operations research, and really don’t care about that field.

This is not surprising for those with an INFORMS-centric point of view.  INFORMS does not (and near as I can tell, ever has) represent even all of “operations research”.  According to the Bureau of Labor Statistics, there are more than 65,000 people with the job “operations research analyst”.  INFORMS membership of a bit more than 10,000  is a small fraction of all those involved in operations research.  INFORMS is not all of operations research:  it certainly is a small amount of business analytics.  How can INFORMS “own” business analytics when it doesn’t even own operations research?

Recognizing this divide does not mean erecting a wall between the areas (see the first paragraph on the mendacity of labels).  I think the “business analytics” world has a tremendous amount to learn from the “operations research” world and vice versa.  Here are few things the two groups should know (and are clearly known by some on both sides, though not to an ideal extent);  I welcome your additions to these lists:

What Business Analytics People should Learn from Operations Research

  1. Getting data and “understanding” it is not enough.
  2. Predicting the future does not imply making better decisions.
  3. Lots of decisions are interlinked in complicated ways.  Simple rules are often not enough to reconcile those linkages.
  4. Handling risk is more than knowing about the risk or even modeling the risk.  See “stochastic optimization”.
  5. Organizations have been competing on and changed by analytics for a long, long time now.   See the Edelman competition to start.
  6. Operations research is not exactly an obscure field.  Check the google trends of “operations research” versus “business analytics” (with OR in blue and BA in red).

What Operations Research People should Learn from Business Analytics

  1. It is not just the volume of data that is important:  it is the velocity.  There is new data every day/hour/minute/second, making the traditional OR approach of “get data, model, implement” hopelessly old-fashioned.  Adapting in a sophisticated way to changing data is part of the implementation.
  2. Not everything is complicated.  Sometimes just getting great data and doing predictions followed by a simple decision model is enough to make better decisions.  Not everything requires an integer program, let alone a stochastic mixed integer nonlinear optimization.
  3. Models of data can involve more than means and variances, and even more than regression.
  4. One project that really changes a company is worth a dozen papers (or perhaps 100) in the professional literature.
  5. It is worthwhile for people to write about what is done in a way that real people can read it.

I believe strongly in both operations research and business analytics.  I have spent my career advancing “operations research” and have never shied from that name.  And I just led an effort to start an MBA-level track in business analytics track at the Tepper School.  This track includes operations research courses, but includes much more, including courses in data mining, probabilistic marketing models, information systems, and much more.

The lines between operations research and business analytics are undoubtedly blurred and further blurring is an admirable goal.  The more the two worlds understand each other, the more we can learn from each other.  INFORMS plays a tremendously important role in helping to blur the boundaries both by sharing the successes of the “operations research world” with the “business analytics” world, and by providing a conduit for information going the other way.  And this, more than “owning” business analytics, is what INFORMS and its members should be doing.

Ironically, this is part of the INFORMS Blog Challenge.

 

 

That’s got to be true… doesn’t it?

Back in 1996, Harvey Greenberg, longtime faculty member at the University of Colorado at Denver, began putting together a collection of myths and counterexamples in mathematical programming.  While generally I find mathematical programming to be quite intuitive, there turn out to be lots of things that I think must be true that are not.  Consider the following:

  1. The duality theorem applies to infinite LPs.
  2. Simulated annealing converges more slowly than steepest descent when there is a unique optimum.
  3. In linear programming, a degenerate basis implies there is a (weakly) redundant constraint.
  4. If f has continuous nth-order derivatives, local behavior of f can be approximated by Taylor’s series.
  5. The problem of finding integer x such that Ax = b, where A is an m by n integer matrix and b a length m integer vector, is NP-complete.

Amazingly none of these are true!  Reading through the myths and counterexamples reminds me of how much I “know” is really false.

The Myths and Counterexamples document is hosted by the INFORMS Computing Society as part of its Mathematical Programming Glossary, and Harvey periodically updates the Myths site (with the last update being in February 2010).  If you have shown that something that seems obvious is actually false, be sure to let Harvey know about it.  And the next time you are doing a proof and are tempted to make a claim because “it is well known that…” or “obviously…”, perhaps you should check out the site first.

Thanks to @fbahr and the folks at Reddit’s SYSOR for reminding me of the value of a project I have followed for about fifteen years so far!

Recently on OR-Exchange…

OR-Exchange is a question and answer site on operations research (and analytics).   The concept couldn’t be simpler.  People ask questions about operations research;  people answer questions about operations research.  Kinda like the usenet group sci.op-research without the spam.

I put together the site a couple of years ago when stack-exchange made it easy to put such sites on their server.  The idea was to mimic the popular mathoverflow site, but to specialize on operations research issues.  I had no idea of how well this would work, but it started off pretty active and continued to grow.

About a year ago, stackexchange decided on a different path, and they no longer wanted to host or support other groups.  Instead, groups of people could go through a process to become a true stackexchange site.  A number of groups have done that, and have done well with that path.  Unfortunately, our site was a little small for that direction.  Even now, with 381 “official users”, it would be the smallest of any stackexchange site (the current smallest is Jewish Life and Learning with 401 users).  The requirements to be a stackexchange site seemed insurmountable, so we needed another solution.

At this point, INFORMS stepped in and offered to sponsor the site.  After receiving confirmation that “sponsorship” did not mean “ownership” and that we could continue acting the way we were, we (i.e. me, along with a few of the very active participants) decided to move the site to INFORMS.  A big question was the software to use, since stackexchange software was no longer available.  Fortunately, there was an open source replacement from osqa.net, so it was just a matter of installing that….   Famous last words!  Installing the software and getting the current questions and answers from stackexchange was no easy feat.  Fortunately, David and Herman from INFORMS were up to the task, and were able to do the herculean task of getting things up and running smoothly.  The conversion happened on April 8, while I was sitting in a faculty meeting, doing the few minor things I needed to do, like pointing or-exchange.com from stackexchange to INFORMS (Here is some advice:when sitting in a faculty meeting, do not try to guess URLs;  godaddy and bigdaddy lead to radically different sorts of sites).  And things have worked great since then!

As I said, there are 381 registered users for the site, with about 40 being reasonably active.  But you don’t have to register to read the questions and answers, and there are about 300 unique visitors per day who do so, often due to hits at google.  This 300 is more than the background hits on this blog (when I post, hits spike up, but I run about 275 hits per day between posts).  There have been 316 questions asked, generating 1152 answers, along with at least that many comments.  At this point, there are eight moderators, though the moderation touch is extremely light.

Recently people have asked about

and much more!  It is a friendly group (except when it comes to answering homework problems!) so if you have a question in the area of operations research, broadly defined, don’t hesitate to check it out!  And thanks to INFORMS, and particularly Terry, David, and Herman, for the sponsorship and the outstanding technical support.