Intelligent Enterprise

Same Discipline, Different Language

"Operations researchers don't interact with the IT community as much as they ought to," says Mary Crissey, an analytics marketing manager at SAS, a council officer of INFORMS, and, apparently, one of the few vendor executives with a foot in both the BI and OR camps.

"Academic mathematicians are not worried about what terms are buzzing about in the business world," Crissey says. "They talk to each other in their mathematical language of equations and theory without getting entangled in terminology such as BI. Pure Intelligence for business or public service organizations all boils down to data analysis; they just don't call it BI."

Taylor observes that, "most organizations that are good at OR " whether optimization, predictive analytics or data mining " tend to have these groups quite separate from the reporting/dashboard group that supports BI. Unless BI becomes both decision-centric — focusing on decisions to be made not on data to be stored and regurgitated — and more focused on operational decision making — the kinds of decisions that change transactional outcomes — then it is going to remain separate from OR."

Will a BI reorientation toward decision management, and increasing OR attention to business concerns, break down the barrier between the two communities?

OR techniques center on the search for optimal solutions to mathematical models of business operations. Perhaps BI practitioners, business analysts and executives view mathematical modeling and optimization as esoteric and inaccessible. Yet BI essentials — data transformations, OLAP, performance indicators, and visualizations — are built on the same technical foundation as OR (albeit with models focused on "business objects" captured in dimensional or normalized operational database schemas rather than as systems of equations).

"The owners of [business] problems aren't asking or looking for answers from the OR community," says Crissey. " In the past year or so, I have seen the gulf between 'number crunchers' and management decision makers narrow some. However, a language barrier continues to exist as an obstacle for many real-world implementations."

What's in the Mix?

There could be additional scope and style barriers that compound the differences in analytical complexity, language, and targets (front-office finance, sales, competitive intelligence, and marketing functions for BI vs. back-end manufacturing and logistics for OR). BI is perhaps more oriented toward narrowly scoped problems where OR models larger-scale systems. And BI is interactive, supporting but not directly linked to decision execution, where OR is automated and integrated.

SAS's Crissey has been thinking about ways to broaden acceptance of OR and, more generally, advanced analytics for several years. She points to an INFORMS campaign to "market the profession," under the "Science of Better" rubric, and she writes in an article published by SAS that "OR gives executives the power to make effective decisions and build productive systems based on rigorous mathematical models, consideration of all options, careful predictions of outcomes and estimates of risk, and state-of-the-art decision tools and time-tested algorithms."

BI practitioners stand to gain a great deal by adopting and adapting OR tools and techniques. The OR community's education and outreach initiatives are on track, but they'll have to embrace and build on BI practices already in place. The potential enterprise benefits are clear: improved organizational decision making, exploiting comprehensive operational models. When those models are in place, the BI-OR gulf will be bridged. Until then, the often-touted 360-degree views will remain only views and the promise of automated, systematized decision making and management will remain only promise.