Category Archives: Big Data

The Consequences Of Obscurity

In a recent blog post, Polly Mitchell-Guthrie, when referring to an operations research project at UPS, wrote: “Does it really matter what we call it, if people value what was done and want to share the story? If it leads to the expansion of OR I don’t care if its called analytics.” In a 2011 blog post Professor Michael Trick went even further, stating: “The lines between operations research and business analytics are undoubtedly blurred and further blurring is an admirable goal.”

The desire for an association with the very popular, and wildly hyped terms, analytics and business analytics, is perhaps, understandable. Unfortunately, these terms are associated, not with the problem-centric paradigm of operations research, but with the data-centric world of IT/big data. I have been told by people who would know — an entrepreneur in the analytics space and a leader of a data science team — that when executives and IT leaders talk about analytics, they do not include operations research.

The terms analytics and business analytics are strongly associated with the word data: gathering it, cleaning it, mining it, analyzing it, presenting it, and attempting to gain insights from it. These activities in turn, are closely associated with disciplines such as statistics, data science, computer science, and information technology. As a result, the analytics universe is diverse, and much larger than the operations research community. (Interestingly, Professor Trick, in the above mentioned post, acknowledges that:  “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.”)

Blurring the distinction between operations research and analytics would obscure the distinctive approach, and unique capabilities, of operations research, creating a situation in which operations research no longer has a unique identity, and becomes lost in a larger data-centric universe that is characterized by extreme, data-focused publicity. Were this to occur, you should consider how the following questions would be answered:

  • Will students decide to spend years of their lives studying operations research? Will they even know that such a discipline exists?
  • Will universities continue to offer programs in operations research? Will they continue to require MBA students to take operations research courses? Will they continue to hire professors who specialize in operations research?
  • Will companies form new operations research groups, or maintain existing ones? Will IT leaders decide to add operations research analysts to their data science teams? Will jobs and consulting assignments exist for operations research analysts?

I am afraid that obscurity will not lead to the “expansion of OR”; it will lead OR into oblivion.

Big Data and Tulip Bulbs

In Holland, during the winter of 1636/1637, the price of tulip bulbs rose dramatically. By February, the price of certain bulbs was equal to the price of a large house. Then suddenly, the price of bulbs began to drop sharply; by May, they were practically worthless. Many people were wiped out financially, and the country was shaken.

In the United States, during the roaring 20’s, the stock market boomed. This caused great excitement, and people from all walks of life were ‘in the market’. In October 1929 the market crashed, and by July 1932, the market had lost 89% of its value. The collapse of the banking system and the great depression followed.

During the dot-com boom of the late 1990’s there was great excitement over the creation of ‘new economy’ companies based on the internet. New internet companies, some without profits or revenues, were funded and went public. The prices of these dot-com stocks soared. The NASDAQ index (which was heavily weighted with technology and dot-com stocks) peaked in March 2000; it then began to decline precipitously, and by October, 2002, it had lost 75% of its value. In the market crash of 2000-2002, $5 trillion of market value was lost.

As the real estate market boomed in the late 1990’s and early 2000’s, people bought houses that they couldn’t afford, banks progressively lowered their lending standards, and investors bought financial instruments they didn’t understand. It was widely believed that rising real estate prices and the pooling of mortgages in new financial instruments eliminated risk. Then in 2006, real estate prices began to decline; by 2008, millions of mortgages were in default, and the value of the new financial instruments declined precipitously. By the fall of 2008, the entire financial system was on the brink of collapse, and the worst economic downturn since the great depression ensued.

In all of these speculative bubbles, ‘irrational exuberance’ was present. People behaved as if they were under the influence of a ‘reality distortion field’. They were enthralled by a compelling narrative — people will come from all over europe to buy tulip bulbs, or the internet made traditional financial analysis irrelevant — which blinded them to the actual situation. Information which seemed to support the ‘narrative‘ was emphasized, and anything that conflicted with it was explained away or ignored.

Now we can again witness ‘irrational exuberance‘. This time surrounding ‘big data’. There is again a compelling narrative: data and algorithms will transform human life. Any claim, no matter how extravagant, is taken seriously:

While the claims for ‘big data’ are extreme, supporting evidence is sketchy or nonexistent. Few examples of successful ‘big data’ projects outside of internet marketing are given, and no theoretical basis is offered. A 2/2/15 article in InformationWeek reported on a survey of executives which found that only 27% of ‘big data’ projects were successful. This lack of success was attributed to faulty implementation; possible limitations to the technology were not considered.

Of course, companies offering ‘big data’ services are happy to promote the ‘narrative’ through marketing hype. And interestingly, one proponent of ‘big data’ said “it’s probably a bubble”, and another said “big data has certainly been hyped”. They both then continued to hype ‘big data’!

So, will it be different this time? Will the use of ‘big data’ grow without limit? Are you planning to take a job with a ‘big data’ startup company?

Should We Re-Brand Operations Research?

There are some in the operations research community who want to re-brand operations research. They would like to be called analytics professionals. The reasoning behind this appears to be the following:

Operations research is not that popular;

Analytics is very popular;

They would like to be popular, so;

They will call themselves analytics professionals, and then;

They will be popular.

Here, I will not dwell on the flawed premises, or faulty logic embodied in this reasoning. Instead, I will focus on the consequences of a successful re-branding. When considering these consequences, we should keep the following points in mind:

  • While those promoting analytics have trouble defining it, they are in agreement that it encompasses many different disciplines (see Confusion Over Analytics), such as statistics, computer science, data science, big data, business intelligence and operations research.
  • Operations research represents a tiny fraction of the IT/analytics universe.
  • The existence of generic analytics professionals would imply that there is no longer a meaningful distinction to be made between the ‘former’ disciplines of statistics, computer science and operations research.

To help you envision a post re-branding period, I offer two scenarios. In both, an IT executive is speaking to the leader of what was once an operations research group, but is now an analytics group after being re-branded. Remember, operations research no longer exists!

Scenario A

“Alice, I am assigning you and your team to be part of our data quality initiative.”

“But, Sir.”

“No buts Alice, big data is our priority — we must have high quality data!”

Six months later….

“Well done Alice. You and your team have reduced the error rate by 6%. I’m going to make this assignment to data quality permanent.

Scenario B

“Tom, I am assigning you and your team to our text analytics initiative.”

“But Sir.”

“No buts Tom, our competitors are all heavily involved in this area — we will not be left behind!”

Six months later….

“Tom, you and your team don’t seem to be up to the task — all of your projects are months behind schedule. I’m going to have to let you and your team go. Report to human resources and pickup your termination package.”


So, in one case those who have re-branded survive, and in the other case they do not. In both cases, the practice of operations research ends.

Will Operations Research Survive?

There have been some troubling signs: a 2010 article in OR/MS Today suggested that analytics would subsume operations research; a 2013 LinkedIn discussion asked “Will Big Data end Operations Research?”; and ominously, even INFORMS seems to be distancing itself from operations research.

How should we react to this? Should we:

  • Take early retirement, move to Vermont, and open a bed and breakfast?
  • Claim to be analytics professionals, and hope no one asks us about Hadoop or NoSQL?
  • Return to school to study data science?

No! None of the above will be necessary. To understand why, it is necessary to go back to first principles.

The original meaning of the name operational research (what operations research is called in Great Britain, where it was invented) was literally, scientific research on operations. The name was meant to distinguish scientific research on operations, from scientific research on the underlying technology of some product, e.g. radar. In the late 1930‘s the British Government funded scientific research directed towards creating radar equipment with sufficient range and precision to locate attacking aircraft. They also initiated an operations research study to determine the most effective way to deploy the radar stations, and integrate them into an effective air defense system.

This type of scientific research, and the scientific method upon which it is based, is a problem solving paradigm. Operations research is the application of this problem solving paradigm to the solution of operational and management problems.

During the summer of 1940, this paradigm arguably saved Great Britain from defeat. Today, as the Edelman Competition routinely demonstrates, this paradigm creates benefits so great, that they transform entire organizations. And, it is because of this paradigm that operations research can create value that can be created in no other way. This value — lower costs, higher profits, military advantage, more efficiency, better service — was needed in 1940, is in evidence all around us today, and will be in demand for as long as human civilization persists.

So, there is no cause for alarm. Just continue ‘Doing Good with Good OR’.