What BI Practitioners Can Learn From Operations Research

Growing interest in analytics and the trend toward automated decision making will lead the business intelligence crowd toward the mix of mathematical and statistical techniques used by operations researchers.

Seth Grimes, Contributor

May 5, 2008

7 Min Read

When Netherlands Railways, the Dutch passenger railway network, needed to revamp a timetable that was buckling under the pressure of a near doubling of service (from 8.0 billion passenger kilometers in 1970 to 15.4 billion in 2006), it turned to operations researchers for a fix. The improved timetable led to improved on-time arrivals, greater commuter satisfaction, and an estimated 40 million ($60 million) boost in annual profits.

What is "operations research" (OR) and how does it compare with business intelligence (BI)? OR is a set of mathematical and statistical techniques that are applied to complex management challenges. The techniques model and solve problems involving routing and transportation, communication networks, capacity planning, resource allocation and scheduling, and manufacturing. OR’s attention to “management science” complements the work of BI practitioners, who apply their own analytical techniques to explore finance, sales and marketing, and performance-management questions.

The good news is that these two complementary fields are converging on the common ground of advanced analytics, in part because the OR community is turning its attention to business visibility, and in part because of new decision-management initiatives in the BI world. As this article explains, a blending of BI and OR will likely bring improved decision-making, but don't bank on it happening in a hurry. The differing origins and perspectives of the BI and OR camps have created a gap that may take a while to bridge.

The Natural Fit Between BI and OR

BI is a true enterprise practice, highly visible even if not applied as widely as it could, and perhaps should, be. Vendors and practitioners are working on broadening use; recent years have seen BI extended to new information sources, analysis styles, delivery methods, and lines of business. They’re increasingly embracing data mining, predictive analytics, and initiatives that would reexamine BI practices and processes in order to systematize decision making — the latter a discipline that has emerged under the banner of enterprise decision management (EDM).

It would be natural for BI practitioners to embrace OR, which has long focused on automating decision making, surely the goal of those who talk about closed-loop BI. “OR starts with the decision and works back to figuring out what math and data will help with devising a better solution, while BI tends to start with the data and see what can be done with it," says James Taylor, co-author of Smart (Enough) Systems and one who believes that OR and BI are complementary but quite different. "OR folks tend to be focused on the nitty-gritty of day-to-day operations, and they use data from operational systems. BI tends to be focused on knowledge workers, data warehouses, and aggregation.”

It would be natural for the OR community to reach out to the BI world and its community of business-focused knowledge workers, who are increasingly looking to build out their analytical toolkits. “C-level decision makers are turning to analytics for help in the decision-making process,” writes Peter Horner, editor of Analytics, a new magazine published by the Institute for Operations Research and the Management Sciences (INFORMS). “When you see terms like operations research (OR), think analytics.” Many in the BI world, who are already supporting those executive decision makers, are saying close to the same things about BI and analytics.

Given the close kinship of BI and OR, one wonders why these two camps have long existed as separate communities?

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.


By Mary Crissey

Operations research (OR) is likely to deliver better, higher-confidence decision making if one or more of the following conditions applies:

1. You face complex decisions. Do you face more decision factors than you can handle manually? Do you have competing goals or difficulty weighing the pros and cons involved with multiple criteria? OR professionals can analyze complex situations and build intelligence into software systems.

2. You’re having problems with business processes. One or more of your processes are limping along and you aren’t sure exactly what to change. Many small, day-to-day decisions are guided by what’s typically worked well in the past, and you’d like to incorporate creative improvements. OR can simulate and test proposed changes to your processes before you implement costly revisions.

3. Your organization is not making the most of its data. Do you track information about your organization and have data that is begging to be used? OR specializes in working with unused or underused data, extracting the most valuable information and showing what additional data you could collect to increase the value further.

4. You’re troubled by risk. Do you want to limit or reduce risk? Assessing the risk of a new project or contract is often tricky. OR helps you quantify risk, which is critical to controlling it.

Mary Grace Crissey is an analytics marketing manager at SAS and a council officer of INFORMS. Write her at [email protected]

About the Author(s)

Seth Grimes


Seth Grimes is an analytics strategy consultant with Alta Plana and organizes the Sentiment Analysis Symposium. Follow him on Twitter at @sethgrimes

Never Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.

You May Also Like

More Insights