Summary & Glossary of Chapter 1.pdf

16 Chapter 1 Introduction S umma r y This introductory chapter began with a discussion of decision making. Decision making can be defined as the following process: (1) identify and define the problem, (2) determine the criteria that will be used to evaluate alternative solutions, (3) determine th...

16 Chapter 1 Introduction S umma r y This introductory chapter began with a discussion of decision making. Decision making can be defined as the following process: (1) identify and define the problem, (2) determine the criteria that will be used to evaluate alternative solutions, (3) determine the set of alternative solutions, (4) evaluate the alternatives, and (5) choose an alternative. Decisions may be strategic (high level, concerned with the overall direction of the business), tactical (midlevel, concerned with how to achieve the strategic goals of the business), or operational (day-to-day decisions that must be made to run the company). Uncertainty and an overwhelming number of alternatives are two key factors that make decision making difficult. Business analytics approaches can assist by identifying and mitigating uncertainty and by prescribing the best course of action from a very large number of alternatives. In short, business analytics can help us make better-informed decisions. There are three categories of analytics: descriptive, predictive, and prescriptive. ­Descriptive analytics describes what has happened and includes tools such as reports, data visualization, data dashboards, descriptive statistics, and some data-mining techniques. Predictive analytics consists of techniques that use past data to predict future events or ascertain the impact of one variable on another. These techniques include regression, data mining, forecasting, and simulation. Prescriptive analytics uses data to determine a course of action. This class of analytical techniques includes rule-based models, simulation, decision analysis, and optimization. Descriptive and predictive analytics can help us better understand the uncertainty and risk associated with our decision alternatives. Predictive and prescriptive analytics, also often referred to as advanced analytics, can help us make the best decision when facing a myriad of alternatives. Big data is a set of data that is too large or too complex to be handled by standard data-processing techniques or typical desktop software. The increasing prevalence of big data is leading to an increase in the use of analytics. The Internet, retail scanners, and cell phones are making huge amounts of data available to companies, and these companies want to better understand these data. Business analytics helps them understand these data and use them to make better decisions. We also discussed various application areas of analytics. Our discussion focused on financial analytics, human resource analytics, marketing analytics, health care analytics, supply chain analytics, analytics for government and nonprofit organizations, sports analytics, and web analytics. However, the use of analytics is rapidly spreading to other sectors, industries, and functional areas of organizations. We concluded this chapter with a discussion of legal and ethical issues in the use of data and analytics, a topic that should be of great importance to all practitioners and consumers of analytics. Each remaining chapter in this text will provide a real-world vignette in which business analytics is applied to a problem faced by a real organization. G l o ssa r y Artificial Intelligence (AI) The use of data and computers to make decisions that would have in the past required human intelligence. Advanced analytics Predictive and prescriptive analytics. Big data Any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software. Business analytics The scientific process of transforming data into insight for making better decisions. Data dashboard A collection of tables, charts, and maps to help management monitor selected aspects of the company’s performance. Data mining The use of analytical techniques for better understanding patterns and relationships that exist in large data sets. Data query A request for information with certain characteristics from a database. Copyright 2021 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. Glossary 17 Data scientists Analysts trained in both computer science and statistics who know how to effectively process and analyze massive amounts of data. Data security Protecting stored data from destructive forces or unauthorized users. Decision analysis A technique used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events. Descriptive analytics Analytical tools that describe what has happened. Hadoop An open-source programming environment that supports big data processing through distributed storage and distributed processing on clusters of computers. Internet of Things (IoT) The technology that allows data collected from sensors in all types of machines to be sent over the Internet to repositories where it can be stored and analyzed. MapReduce Programming model used within Hadoop that performs the two major steps for which it is named: the map step and the reduce step. The map step divides the data into manageable subsets and distributes it to the computers in the cluster for storing and processing. The reduce step collects answers from the nodes and combines them into an answer to the original problem. Operational decisions A decision concerned with how the organization is run from day to day. Optimization models A mathematical model that gives the best decision, subject to the situation’s constraints. Predictive analytics Techniques that use models constructed from past data to predict the future or to ascertain the impact of one variable on another. Prescriptive analytics Techniques that analyze input data and yield a best course of action. Rule-based model A prescriptive model that is based on a rule or set of rules. Simulation The use of probability and statistics to construct a computer model to study the impact of uncertainty on the decision at hand. Simulation optimization The use of probability and statistics to model uncertainty, combined with optimization techniques, to find good decisions in highly complex and highly uncertain settings. Strategic decision A decision that involves higher-level issues and that is concerned with the overall direction of the organization, defining the overall goals and aspirations for the organization’s future. Tactical decision A decision concerned with how the organization should achieve the goals and objectives set by its strategy. Utility theory The study of the total worth or relative desirability of a particular outcome that reflects the decision maker’s attitude toward a collection of factors such as profit, loss, and risk. Copyright 2021 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.