Sir, Let me know the difference between Operations Research and Business Analysis?
asked 05 Apr '11, 16:53
I believe that an excellent answer to this question is on page 61 of the October 2010 issue of ORMS Today Magazine. Unfortunately that article is not available for public access any more (not sure why; it was open for a while). A simple Google search reveals that it is still cached inside the Google servers. A link to the cached version is below. Take a look at the section entitled "Findings of the Study".
<< Moderator added snippet from link to avoid cache to be empty later on :
The study delivered a concise definition of analytics:
Analytics facilitates realization of business objectives through reporting of data to analyze trends, creating predictive models for forecasting and optimizing business processes for enhanced performance. Of course it can be argued that O.R. facilitates the same objectives and has pretty much the same definition. However, a key finding from the study is that analytics is seen as a core function of businesses that use it and spans many departments and functions within organizations and – in mature organizations – the entire business. O.R. on the other hand is seen as a toolbox of highly specialized techniques that are used only under special circumstances to solve only certain business problems. Analytics is seen as driving business value in addition to the academic integrity that O.R. affords. Perhaps the reason that INFORMS has been unable to gain much traction with practitioners, whether they consider themselves O.R. or analytics practitioners, is that analytics seems to speak the language of business while O.R. in most cases does not. The Science of Better campaign had many successes, of course, but was largely unsuccessful in getting business to widely embrace O.R. and speak our language.
The study helped our understanding of the field by uncovering three main categories of business analytics that are clearly hierarchical but sometimes overlap: descriptive, predictive and prescriptive analytics.
Most businesses start with descriptive analytics – the use of data to figure out what happened in the past. Descriptive analytics prepares and analyzes historical data and identifies patterns from samples for reporting of trends. Techniques such as data modeling, visualization and regression analysis largely reside in this space.
Predictive analytics uses data to find out what could happen in the future. Naturally it is a more refined and higher level usage of analytics. One can argue that some parts of O.R. reside in the predictive analytics category. Predictive analytics predicts future probabilities and trends and finds relationships in data not readily apparent with traditional analysis. Techniques such as data mining and predictive modeling reside in this space.
Prescriptive analytics uses data to prescribe the best course of action to increase the chances of realizing the best outcome. Prescriptive analytics evaluates and determines new ways to operate, targets business objectives and balances all constraints. Techniques such as optimization and simulation reside in this space. Most of us would probably agree that in fact most O.R. techniques reside in this space. Businesses, as they strive to become more analytically mature, have indicated a goal to move up the analytics hierarchy to optimize their business or operational processes. They see the prescriptive use of analytics as a differentiating factor for their business that will allow them to break away from the competition. Clearly, analytics leads to optimization where much of O.R. resides. But it is also clear that optimization is dependent on the analytics process.
The analytics process spans: project initiation – problem identification and process analysis; planning – requirements gathering and data needs/analysis; execution – data visualization, assessment analysis, predictions and trends and optimization/simulation; and finally, conclusions. Whether right or wrong, business largely sees O.R. residing only in the execution phase (and only in part of that phase.)
Further, there seems to be a stronger vertical industry alignment for analytics professionals than for O.R. professionals. For example, analytics professionals working in health care see themselves as health care professionals who happen to use analytics to help drive business decisions. Conversely, business seems to hold the view that operations research professionals, no matter the industry, tend to see themselves as operations researchers first and an industry professional second.
The study also made it clear that the needs of analytics professionals depend on the analytical maturity of the firm. Highly mature firms have a structured analytics team that is centralized and headed by an analytics expert. Less mature firms tend to have no clear structure – analysts are usually dispersed across the firm. Highly mature firms have well-defined recruitment processes; they know where to find analytics talent. Less mature firms have an ad hoc recruitment process and don’t really know where to find analytics talent. Mature firms have robust training programs for their analytics professionals. Less mature firms have very little or no training. Mature firms use techniques spanning the most basic to highly advanced. Less mature firms depend on basic data modeling and descriptive statistics.
Assuming you mean Analytics and not Analysis, here I shamelessly paste the answer by our dear Mr. OR-exchange-Admin himself from a talk in Denmark :
(sorry about the layout, copied from a pdf)
What's the difference?
Most definitions for both operations research and business analytics are deficient Operations Research:
Operations research (also referred to as decision science, or management science) is an interdisciplinary mathematical science that focuses on the effective use of technology by organizations. (wikipedia)
The term Operations Research (OR) describes the discipline that is focused on the application of information technology for informed decision-making. In other words, OR represents the study of optimal resource allocation. (Fortuitous Technology)
Mathematical or scientific analysis of a process or operation, used in making decisions. (answers.com
Business analytics (BA) refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. [wikipedia]
Business analytics (BA) is the practice of iterative, methodical exploration of an organization s data with emphasis on statistical analysis. (searchbusinessanalytics.com)
business analytics is the combination of skills, technologies, applications and processes used by organizations to gain insight in to their business based on data and statistics to drive business planning.
answered 05 Apr '11, 16:58
Bo Jensen ♦