Issue link: http://www.e-digitaleditions.com/i/124472
Analytical Definitions A 2011 study by the McKinsey Global Institute predicts that by 2018 the United States will face a shortage of more than 1.5 million managers, analysts, and other workers who are well-versed in the principles and use of analytics. As we design new courses and programs to meet this demand, we first must understand the three kinds of analytics: descriptive, predictive, and prescriptive. Each serves a different purpose, and each requires a different skill set and preparation. Descriptive analytics explores what has occurred. Reporting, online analytical processing (OLAP), dashboards, scorecards, and data visualization are all examples of descriptive analytics. For instance, I cover the development of dashboards and scorecards in my business intelligence course. Dashboards and scorecards display reports, charts, graphs from different sources, and data related to key performance indicators and benchmarks. I want students to know how to use these tools and how to link them to business strategy, metrics, development methodologies, strategic performance management, data infrastructure, software options, and interface design. Predictive analytics concentrates on what will occur in the future. The algorithms and methods for predictive analytics include regression analysis, factor analysis, and neural networks. The applications of this approach include demand forecasting, customer segmentation analysis, and fraud detection. Prescriptive analytics investigates what should occur. It is used to optimize system performance. Revenue management, which strives to optimize the revenue from perishable goods, such as hotel rooms and airline seats, is a good example. Through a combination of forecasting and mathematical programming, prices are dynamically set over time to optimize revenues. Predictive and prescriptive analytics are often referred to as According to the McKinsey Global Institute, by 2018, the United States will face a shortage of more than 1.5 million managers, analysts, and other workers who are well-versed in the principles and use of analytics. advanced analytics. Most organizations progress from descriptive to predictive to prescriptive analytics. First, organizations monitor what is taking place now in their businesses; next, they predict what will occur; and finally, they want to shape the future. Skill Sets and Mindsets What does the growing emphasis on analytics mean for business schools and our students? The answer depends on the roles our students will assume in organizations after they graduate. Generally, when it comes to analytics, they'll be one of three types of workers: business users, business analysts, or data scientists. Business Users—The majority of our graduates will be business users, who access analytics-related information and use descriptive analytics tools to create reports, conduct OLAP, interact with dashboards/ scorecards, and use data visualization. Students in this group need to have significant experiences in data gathering and analysis during their business educations. They need to understand how data is stored in relational databases and how to access and analyze data using a variety of data analysis tools (including Excel). These users can have degree concentrations in almost any business discipline and use these tools in a variety of contexts. Business Analysts—Members of this group access and analyze data, and then provide information based on that data to others in their organizations. Most business analysts are located in the functional areas of business, such as marketing, and do analytical work, such as designing marketing campaigns. Some in this group are members of centralized analytics teams that provide analytics support across the organization. Business analysts work with some combination of descriptive and advanced analytics. Business analysts are analytical and inquisitive. Some have business degrees in areas such as MIS, marketing, and finance; others have degrees in statistics, mathematics, or engineering. This group is better served with higher-level courses and concentrations. Data Scientists—These BizEd May/June 2013 51