POWER STATION PROTECTIVE SYSTEMS:THE RISE OF ANALYTICS TOGETHER WITH OPERATIONS RESEARCH

THE RISE OF ANALYTICS TOGETHER WITH OPERATIONS RESEARCH

There has been great buzz throughout the business world in recent years about something called analytics (or business analytics) and the importance of incorporating analytics into managerial decision making. The primary impetus for this buzz was a series of articles and books by Thomas H. Davenport, a renowned thought-leader who has helped hundreds of companies worldwide to revitalize their business practices. He initially introduced the con- cept of analytics in the January 2006 issue of the Harvard Business Review with an article, “Competing on Analytics,” that now has been named as one of the ten must-read articles in that magazine’s 90-year history. This article soon was followed by two best-selling books entitled Competing on Analytics: The New Science of Winning and Analytics at Work: Smarter Decisions, Better Results. (See Selected References 2 and 3 at the end of the chap- ter for the citations.)

So what is analytics? The short (but oversimplified) answer is that it is basically oper- ations research by another name. However, there are some differences in their relative emphases. Furthermore, the strengths of the analytics approach are likely to be increas- ingly incorporated into the OR approach as time goes on, so it will be instructive to describe analytics a little further.

Analytics fully recognizes that we have entered into the era of big data where massive amounts of data now are commonly available to many businesses and organizations to help guide managerial decision making. The current data surge is coming from sophisticated computer tracking of shipments, sales, suppliers, and customers, as well as email, Web traffic, and social networks. As indicated by the following definition, a primary focus of analytics is on how to make the most effective use of all these data.

Analytics is the scientific process of transforming data into insight for making better decisions.

The application of analytics can be divided into three overlapping categories. One of these is descriptive analytics, which involves using innovative techniques to locate the rel- evant data and identify the interesting patterns in order to better describe and understand what is going on now. One important technique for doing this is called data mining (as described in Selected Reference 8). Some analytics professionals who specialize in descriptive analytics are called data scientists.

A second (and more advanced) category is predictive analytics, which involves using the data to predict what will happen in the future. Statistical forecasting methods, such as those described in Chap. 27 (on the book’s website), are prominently used here. Simulation (Chap. 20) also can be useful.

The final (and most advanced) category is prescriptive analytics, which involves using the data to prescribe what should be done in the future. The powerful optimization tech- niques of operations research described in many of the chapters of this book generally are what are used here.

Operations research analysts also often deal with all three of these categories, but not very much with the first one, somewhat more with the second one, and then heavily with the last one. Thus, OR can be thought of as focusing mainly on advanced analytics— predictive and prescriptive activities—whereas analytics professionals might get more involved than OR analysts with the entire business process, including what precedes the first category (identifying a need) and what follows the last category (implementation). Looking to the future, the two approaches should tend to merge over time. Because the name analytics (or business analytics) is more meaningful to most people than the term operations research, we might find that analytics may eventually replace operations research as the common name for this integrated discipline.

Although analytics was initially introduced as a key tool for mainly business organizations, it also can be a powerful tool in other contexts. As one example, analytics (together with OR) played a key role in the 2012 presidential campaign in the United States. The Obama campaign management hired a multi-disciplinary team of statisticians, predictive modelers, data-mining experts, mathematicians, software programmers, and OR analysts. It eventually built an entire analytics department five times as large as that of its 2008 campaign. With all this analytics input, the Obama team launched a full-scale and all- front campaign, leveraging massive amounts of data from various sources to directly micro-target potential voters and donors with tailored messages. The election had been expected to be a very close one, but the Obama “ground game” that had been propelled by descriptive and predictive analytics was given much of the credit for the clear-cut Obama win. Based on this experience, both political parties undoubtedly will make extensive use of analytics in the future in major political campaigns.

Another famous application of analytics is described in the book Moneyball (cited in Selected Reference 10) and a subsequent 2011 movie with the same name that is based on this book. They tell the true story of how the Oakland Athletics baseball team achieved great success, despite having one of the smallest budgets in the major leagues, by using various kinds of nontraditional data (referred to as sabermetrics) to better evaluate the potential of players available through a trade or the draft. Although these evaluations often flew in the face of conventional baseball wisdom, both descriptive analytics and predictive analytics were being used to identify overlooked players who could greatly help the team. After witnessing the impact of analytics, many major league baseball teams now have hired analytics professionals. Some other kinds of sports teams also are beginning to use analytics. (Selected References 4 and 5 have 17 articles describing the application of ana- lytics in various sports.)

These and numerous other success stories about the power of analytics and OR together should lead to their ever-increasing use in the future. Meanwhile, OR already has had a powerful impact, as described further in the next section.