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.
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?
The Agile ArchiveWhen it comes to managing data, donít look at backup and archiving systems as burdens and cost centers. A well-designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems.
2014 Analytics, BI, and Information Management SurveyITís tried for years to simplify data analytics and business intelligence efforts. Have visual analysis tools and Hadoop and NoSQL databases helped? Respondents to our 2014 InformationWeek Analytics, Business Intelligence, and Information Management Survey have a mixed outlook.