Teaching and Using Analytics in Management Education

Teaching and Using Analytics in Management Education

Owen P. Hall Jr.
Copyright: © 2014 |Pages: 13
DOI: 10.4018/978-1-4666-5202-6.ch221
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Management education has entered a seminal period that is being driven by globalization, technology, and demographics. Calls for reform in both content delivery and outcomes continue to grow (Schoemaker, 2008; Kleiman, 2007). As a result, an increasing number of business school educators are now behind the movement to enact significant changes in the way management education, in general, and graduate management education, in particular, is delivered (Bruner, 2011; Danko, 2005). This trend is underscored by the rising number of online management programs among the top business schools (Grzeda, 2009). Additional trends include an increasing demand for fast track and limited residency programs, expanding peer school strategic alliances, and an escalating interest for program customization and flexibility. Naturally, as with any significant transition, there are a wide variety of challenges including risk mitigation, program consistency and faculty adoption.

Management education students, given the current business environment, need to develop technical analysis capabilities and problem solving skills to remain competitive in the globalized marketplace (Kao, 2011; Thomas, 2007). Analytics, which is receiving increased attention throughout industry and government, can be used to enhance management education in the following ways (Coghlan, 2010):

  • Provide a conceptual setting for improving student managerial decision-making expertise,

  • Assess student performance and identify appropriate additional learning resources, and

  • Offer business school administrators the capability to improve operational efficiency and effectiveness.

Analytics is the science of discovering and communicating meaningful patterns in data and developing actionable plans (Cooper, 2012). Organizations of all shades and hues are facing increased challenges associated with the growing availability of large and complex data bases. Many successful companies have already developed a fact-based and data informed decision-making culture based on the Analytics paradigm (Klatt, 2011). The dramatic growth of data collection throughout academia follows the trends in business over the last two decades. Unlike business, however, academia’s ability to process information in a timely way continues to lag. This is where the Analytics paradigm can also help. Generally speaking, Analytics can be divided into three broad categories: Descriptive, Predictive and Prescriptive. Descriptive Analytics is all about providing insights into what has already happened (e.g., student drop out rates). Predictive Analytics focuses on generating forecasts about the future (e.g., student enrollment trends and students at risk). Prescriptive Analytics builds on both descriptive and predictive Analytics to help identify solutions to specific problems and decision-making applications. As applied to academe this could include scholarship allocations and capacity sizing.

Key Terms in this Chapter

Action Analytics: To produce actionable plans that improves student learning performance and reduces costs

Predictive Analytics: A methodology for forecasting futures events and trends using a variety of technologies including statistics and artificial intelligence.

Business Intelligence (BI): A process for improving the decision-making process through enhanced data analysis.

Statistics: Generally divided into two broad categories: Descriptive and Inferential. Descriptive statistics involves data collect, process, classification and reporting. Inferential statistics focuses on drawing general conclusions from a sample regarding a population characteristic.

Analytics: The science of discovering and communicating meaningful patterns in data and developing actionable plans. The Analytics paradigm often favors data visualization methods in reporting results and recommendations. Typically, Analytics consists of three board categories: descriptive, predictive and prescriptive.

Visual Analytics: The process for analyzing large, disparate databases using visual interfaces.

Big Data: An extremely large database which generally defies standard methods of analysis.

Optimization: An analytical procedure for allocating scarce resources among competing alternatives in such a way as to achieve one or more goals.

Artificial Intelligence (AI): Is a computer-based analytical process that exhibits behavior and actions that are considered “intelligent” by human observers. AI attempts to mimic the human thought process including reasoning and optimization.

Academic Analytics: Application of the principles and tools of business Analytics to academia with a special emphasis on enhancing administrative performance.

Data Mining: A process for discovering patterns in large data bases using a variety of analytical techniques including artificial intelligence.

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