The Flintstones’ parable

Doug Samuelson

The group that had gathered to watch the Super Bowl moved on, as the game progressed, from their conversation about healthcare to the state of the OR/MS profession. Jim, who had talked about the threat of a flu outbreak at the Olympics in Rio, snorted scornfully at the fourth or fifth ad by a well-known company touting its new analytics software.

“Great-looking, entertaining ads,” he said, “but there was something really odd about them – did you notice? They’ve been developing and promoting this product for about five years, but every ad was about what they claim it can do – not about anything it has done! It’s the same thing the resume critique experts tell a lot of us about our resumes: Unless you’re brand new, talk about accomplishments, not duties or capabilities, preferably with a client praising the achievement. I’d assume these folks know that, so what I take from these ads is that they haven’t gotten it to do anything useful!”

“That might explain the big drop in their stock price over the last couple of years,” Fred remarked dryly.

“But it’s not just one company,” Jim added quickly. “They’re just one of the most prominent. It seems almost everyone’s jumping on the bandwagon of ‘analytics will save the world,’ but try getting any agreement about what analytics is and how to get it to work for you. And a lot of these computer-based ‘inference engines’ we hear about turn out to require an awful lot of pre-processing and pre-structuring, usually not automated or anything close to it, before they can accomplish anything. Like many O.R. techniques, they’re good for problems with set, well-defined categories and lots of good data, but not for the typical squishy, messy, ill-defined problems that decision-makers tend to care most about. I hope our profession, having jumped on that ‘analytics’ bandwagon too, doesn’t end up being dismissed as irrelevant and unhelpful in a few years, when the promise isn’t fulfilled.”

“Don’t we have some of those issues already?” Jane asked. “I know a lot of people, and academic departments for that matter, have wound up relabeling themselves as ‘systems engineers’ or ‘decision scientists’ or, now, ‘analytics professionals,’ because the name ‘operations research’ just didn’t mean all that much to decision-makers.”

“No kidding,” Jim concurred grimly. “As you know, I’m pretty active in the profession, so I get into some interesting discussions about the future of the profession. I’ve been arguing for years that if we keep trying to define O.R. more and more precisely as a particular set of methods and tools, we’ll end up, as Russ Ackoff predicted years ago, isolated in the basement, called in occasionally by the real decision analysts when they have a hard math problem to solve. But if what we offer is a uniquely productive way of looking at the world and solving problems that matter, then we have a great future. It’s our choice.

“And a lot of these companies, and business schools for that matter, don’t get it either,” Jim went on. “More and more emphasis on a specific set of techniques and methods, less and less on solving unfamiliar problems, and then wondering why smaller, more agile competitors are eating their lunches. Thirty years ago, the book ‘In Search of Excellence’ convinced managers they didn’t need quantitative support at all. Ten years ago, ‘Competing on Analytics’ convinced the next generation of high-level decision-makers that they did need quantitative methods, but didn’t explain how to tell who was doing them right. So, either way, lots of organizations talk a great innovation game, often claiming they’re analytics-based, but then keep on just doing what they’ve been doing.”

“Ugh,” Jane and Fred chorused. “Not encouraging.”

“No, it isn’t,” Jim agreed, “but that isn’t the worst of it. I’ve heard supposedly knowledgeable colleagues ‘explain’ the success of the big data and data science movements as, ‘What’s really going on there is that those people just don’t know enough about analytical techniques to appreciate what we do.’ Actually, I go to their meetings, and many of them have hard science doctorates and aren’t shy about mathematical techniques at all. And actually, a lot of these big data and data science people do need us, because setting up data to support decision-making requires some structuring and association tracking that brute force data storage and retrieval, even massively parallel with huge computing resources, just can’t do in any reasonable time. But, as with many other application areas, we won’t be appreciated if we don’t learn their language and explain things in their terms.

“So here we are in the modern version of The Flintstones,” Jim concluded.

“Too many organizations that used to be central to our profession are dinosaurs and don’t even realize it. And you know the dinosaurs end up extinct.”

“You do know that ‘The Flintstones’ was fiction, and dinosaurs never really coexisted with humans, don’t you?” Jane inquired, laughing.

“Sure,” Jim replied, “but here in real life we do have dinosaurs coexisting with their competitor species and having no idea of what’s about to happen to them. By the way, you don’t still own stock in any of these companies we were talking about, do you? Some have rebounded a bit lately, partly because of good ad campaigns and partly because of established reputations from past successes, and good marketers can always ‘sell the sizzle, not the steak,’ without providing a good piece of meat – for a while. But I’d bail out now, if I were you!”

Doug Samuelson ( is president and chief scientist of InfoLogix, Inc., in Annandale, Va.