The Next Frontier for Decision Sciences
by James R. Evans, Carl H. Lindner College of Business, University of Cincinnati, Decision Science Institute, March 2012
I'm going out on a limb here: business analytics is the
next supply chain management. About a decade ago, supply chain management
overtook total quality management as the buzzword among business practitioners
and academics. Today, business analytics is the hottest thing going, and seems
to be leaving SCM in the dust (although the application of analytics to SCM is
certainly growing!). A recent article at smartdatacollective.com noted:
The real trend this year is not the technology. It's about helping business people make better decisions, and actually change the way companies do business. Analytics has always been about transforming business, but the recent huge changes in analytic technology have created interesting new opportunities for business innovation. (Elliott, 2012)
The author also noted that analytics is the number one top technology priority for both CIOs and CFOs according to Gartner; organizations get $10.66 of value for every $1 invested in analytics; and growth forecasts faced are stronger-than-expected—Gartner announced an early estimate of more than a 10 percent growth in analytics during 2011, outpacing general IT growth. Many companies have recently established analytics departments; for instance, IBM reorganized its consulting business and established a new 4,000-person organization focusing on analytics (Liberatore & Luo, 2010). In 2011, the U.S. Bureau of Labor Statistics predicted a 24 percent increase in demand for professionals with analytics expertise.
reconfigured or are developing new degree programs at all levels in business
analytics, and many departments (like my own) have changed their names to
reflect the new terminology. For example, my department has expanded its former
MS in Quantitative Analysis to an MS in Business Analytics, with new courses in
data visualization, data mining, and managing business intelligence projects,
and is offering an undergraduate minor in business analytics that includes
spreadsheet analytics, data mining, and analysis along with traditional topics
and functional applications. Indeed, even new textbooks (yes, I'm guilty) are
being published to capitalize on this trend (Evans, forthcoming).
business case for analytics is strong. Various research studies have discovered
strong relationships between a company's performance in terms of profitability,
revenue, and shareholder return, and its use of analytics. Top performing
organizations (those that outperform their competitors) are three times more
likely to be sophisticated in their use of analytics than lower performers and
are more likely to state that their use of analytics differentiates them from
competitors (Davenport & Harris, 2007; Hopkins et al, 2010). However, research
has also suggested that organizations are overwhelmed by data and struggle to
understand how to use it to achieve business results, that most organizations
simply don't understand how to use analytics to improve their businesses. Thus,
understanding the capabilities and techniques of analytics is vital to managing
in today's business environment.
In many ways, the transition from traditional statistics and management science to business analytics reminds me much of the transition from TQM to Six Sigma. Here are the parallels I see:
•Six Sigma emerged as new "version" of TQM; business analytics is emerging as a new "version" of quantitative methods.
•Six Sigma tools had been around for 50 years or more; business analytic tools have been around for 50 years or more.
•Six Sigma grabbed the attention of senior executives in business; business analytics is doing the same.
•Six Sigma focuses on the bottom line; so does business analytics.
•Six Sigma made quality tools sexy to non-quality professionals; business analytics is making quantitative methods sexy to non-quantitative professionals.
We can probably go on. So what is business analytics? Is it really new and different or just a repackaging of the same old stuff? Business analytics has been defined as "a process of transforming data into actions through analysis and insights in the context of organizational decision making and problem solving" (Liberatore & Luo, 2010). My definition is that business analytics is "the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions" (Evans, forthcoming).
Business analytics is commonly viewed from three major perspectives: descriptive, predictive, and prescriptive.1 Most businesses start with descriptive analytics—the use of data to understand past and current business performance and make informed decisions. Descriptive analytics are the most commonly used and most well understood type of analytics. These techniques categorize, characterize, consolidate, and classify data to convert it into useful information for the purposes of understanding and analyzing business performance. Descriptive analytics summarize data into meaningful charts and reports, for example, about budgets, sales, revenues, or cost. They allow managers to obtain standard and customized reports, and drill down into the data and to make queries to understand the impact of an advertising campaign, for example, review business performance to find problems or areas of opportunity, and identify patterns and trends in data. Typical questions that descriptive analytics help answer are: How much did we sell in each region? What was our revenue and profit last quarter? How many and what types of complaints did we resolve? Which factory has the lowest productivity? Descriptive analytics also help companies to classify customers into different segments, which enable them to develop specific marketing campaigns and advertising strategies.
Predictive analytics analyze past performance in an effort to predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time. For example, a marketer might wish to predict the response of different customer segments to an advertising campaign, a commodities trader might wish to predict short-term movements in commodities prices, or a skiwear manufacturer might want to predict next season's demand for skiwear of a specific color and size. Predictive analytics can predict risk and finds relationships in data not readily apparent with traditional analyses. Using advanced techniques, predictive analytics can help to detect hidden patterns in large quantities of data to segment and group data into coherent sets in order to predict behavior and detect trends. For instance, a bank manager might want to identify the most profitable customers or predict the chances that a loan applicant will default, or alert a credit card customer to a potential fraudulent charge. Predictive analytics helps to answer questions such as: What will happen if demand falls by 10 percent or if supplier prices go up five percent? What do we expect to pay for fuel over the next several months? What is the risk of losing money in a new business venture?
Prescriptive analytics uses optimization to identify the best alternatives to minimize or maximize some objective. Prescriptive analytics is used in many areas of business, including operations, marketing, and finance. For example, we may determine the best pricing and advertising strategy to maximize revenue, the optimal amount of cash to store in ATMs, or the best mix of investments in a retirement portfolio to manage risk. The mathematical and statistical techniques of predictive analytics can also be combined with optimization to make decisions that take into account the uncertainty in the data. Prescriptive analytics addresses questions like: How much should we produce to maximize profit? What is the best way of shipping goods from our factories to minimize costs? Should we change our plans if a natural disaster closes a supplier's factory and if so, by how much?
While the tools used in descriptive, predictive, and prescriptive analytics are different, many applications involve all three. Here is a typical example in retail operations.2 As you probably know from your shopping experiences, most department stores and fashion retailers clear their seasonal inventory by reducing prices. The key question they face is what prices should they set, and when should they set them to meet inventory goals and maximize revenue? For example, suppose that a store has 100 bathing suits of a certain style that go on sale April 1, and wants to sell all of them by the end of June. Over each week of the 12-week selling season, they can make a decision to discount the price. They face two decisions: when to reduce the price, and by how much? This results in 24 decisions to make. For a major national chain that may carry thousands of products, this can easily result in millions of decisions that store managers have to make! Descriptive analytics can be used to examine historical data for similar products, such as the number of units sold, price at each point of sale, starting and ending inventories, and special promotions, newspaper ads, direct marketing ads, and so on, to understand what the results of past decisions achieved. Predictive analytics can be used to predict sales based on pricing decisions. Finally, prescriptive analytics can be applied to find the best set of pricing decisions that maximize the total revenue.
Business analytics is a convergence of three key disciplines that have been taught and used for a long time: statistics, business intelligence and information systems, and modeling and optimization (traditionally, operations research and management science). Figure 1 shows my perspective of the relationships and synergies that are defining business analytics. While the core topics are traditional in nature, the uniqueness lies in their intersections. For example, data mining is focused on better understanding characteristics and patterns among variables in large databases using a variety of statistical and analytical tools. Many standard statistical tools, such as data summarization, PivotTables, correlation and regression analysis, and other techniques are used extensively in data mining. However, data mining also brings to the table more advanced statistical methods such as cluster analysis and logistic regression. Risk analysis relies on spreadsheet models and statistical analysis to examine the impacts of uncertainty in the estimates and their potential interaction with one another on the output variable of interest, and is often facilitated by Monte Carlo simulation. Spreadsheets and formal models allow one to evaluate "what-if" questions—how specific combinations of inputs that reflect key assumptions will affect model outputs. What-if analysis is facilitated by systematic approaches that manipulate databases and models, such as data tables, the Excel Scenario Manager, and goal seek tools, and parametric sensitivity analysis used by Excel add-ins such as Risk Solver Platform, which makes it easy to create data tables and tornado charts that provide useful what-if information.
Perhaps the most useful component of business analytics, which makes it truly unique, is the center of FigureÂ 1—visualization (BA635 Bonus Link: Visualization Broadens Business Intelligence's Appeal). Visualizing data and results of analyses provide a way of easily communicating data at all levels of a business, and can reveal surprising patterns and relationships. Software such as IBM's Cognos system exploits data visualization for query and reporting, data analysis, dashboard presentations, and scorecards linking strategy to operations. The Cincinnati Zoo, for example, has used this on an iPad to display hourly, daily, and monthly reports of attendance, food and retail location revenues and sales, and other metrics for prediction and marketing strategies. ARAMARK corporation developed visual "interactive simulators" to display the results of multivariate regression models on dials similar to those on an automobile dashboard, while allowing users to manipulate independent variables using simple sliders. UPS uses telematics to capture vehicle data and display them to help make decisions to improve efficiency and performance.3 IBM has predicted that data visualization will soon overtake historical trend analysis and standardized reporting as the analytic technique that provides the most value.
Figure 1. One perspective on business analytics.
As academics in business schools, we have been teaching these topics for over 40 years, albeit in a disjointed and compartmentalized fashion. Business analytics provides the framework to exploit the synergies between traditionally-diverse topics in a more practical, application-driven format. Perhaps the fields of quantitative methods, OR/MS, DSS, or whatever we've known for the past 40 years will gain the respect they deserve.
1.Adapted from Irv Lustig, Brenda Dietric, Christer Johnson, and Christopher Dziekan, "The Analytics Journey," Analytics, Nov/Dec 2010, Analytics-magazine.org and reprinted from Evans, Business Analytics: Methods, Models, and Decisions, Prentice-Hall, 2013.
2.Inspired by a presentation by Radhika Kulkarni, SAS Institute, "Data-Driven Decisions: Role of Operations Research in Business Analytics," INFORMS Conference on Business Analytics and Operations Research, April 10-12, 2011.
3.The ARAMARK and UPS examples were presented at the 2011 INFORMS Practice Conference in Chicago, April 2011.
Davenport, Thomas H., & Harris, Jeanne G. (2007). Competing on analytics. Boston: Harvard Business School Press, 46.
Elliott, Timo. (2012). 2012: The year analytics means business. Retrieved from smartdatacollective.com, February 10.
Evans, James R. (forthcoming). Business analytics: Methods, models, and decisions. Prentice-Hall.
Liberatore, Matthew J., & Luo, Wenhong. (2010). The analytics movement: Implications for operations research. Interfaces, 40(4), July-August, 313-324.
Hopkins, Michael S., LaValle, Steve, Balboni, Fred, Kruschwitz, Nina, & Shockley, Rebecca. (2010). 10 data points: Information and analytics at work. MIT Sloan Management Review, 52(1), Fall, 27-31