- Readthis 10-page chapterexplaining BA and BI.
- Write a reflection of ~450 words, answering the following:
- What are your key takeaways from the reading?
- Which ONE of the listed quantitative methods you are most familiar with?
- Share insights of when/where/how have you used that quantitative method.
- i.e. how did you become advanced/proficient at it?
- Which of the tools listed under the Business Intelligence circle in Figure 2.5 would you like to learn more about? Why?
- Do NOT structure your reflection as Q&A. Do NOT write in the first person.
BEP499-PDF-ENG.pdf BEP499 CHAPTER 2 Business Analytics and Business Intelligence From Business Analytics, Volume II: A Data-Driven Decision-Making Approach for Business By Amar Sahay (A Business Expert Press Book) Copyright © Business Expert Press, LLC, 2020. All rights reserved. Harvard Business Publishing distributes in digital form the individual chapters from a wide selection of books on business from publishers including Harvard Business Press and numerous other companies. To order copies or request permission to reproduce materials, call 1-800-545-7685 or go to http://www.hbsp.harvard.edu. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means – electronic, mechanical, photocopying, recording, or otherwise – without the permission of Harvard Business Publishing, which is an affiliate of Harvard Business School. This document is authorized for use only by BAH SAFIA in Business Intelligence - DAT-8564 - BOS1 at Hult International Business School, 2021. CHAPTER 2 Business Analytics and Business Intelligence Chapter Highlights • Business Analytics and Business Intelligence—Overview • Types of Business Analytics and !eir Objectives • Input to Business Analytics, Types of Business Analytics, and !eir Purpose • Business Intelligence and Business Analytics: Di"erences • Business Intelligence and Business Analytics: A Comparison • Summary Business Analytics and Business Intelligence—Overview !e terms analytics, business analytics (BA), and business intelligence (BI) are used interchangeably in the literature and are related to each other. Analytics is a more general term and is about analyzing the data using data visualization and statistical modeling to help companies make ef- fective business decisions. !e tools used in analytics, BA, and BI often overlap. !e overall analytics process includes descriptive analytics, in- volving processing and analyzing big data, applying statistical techniques (numerical methods of describing data, such as measures of central ten- dency, measures of variation, etc.), and statistical modeling to describe the data. Analytics also uses predictive analytics methods, such as regression, forecasting, data mining, and prescriptive analytics tools of management This document is authorized for use only by BAH SAFIA in Business Intelligence - DAT-8564 - BOS1 at Hult International Business School, 2021. 24 BUSINESS ANALYTICS, VOLUME II science and operations research. All these tools help businesses in making informed business decisions. !e analytics tools are also critical in auto- mating and optimizing business processes. !e types of analytics are divided into di"erent categories. Accord- ing to the Institute of Operations Research and Management Science (INFORMS)—(www.informs.org)—the #eld of analytics is divided into three broad categories: descriptive, predictive, and prescriptive. We dis- cussed each of the three categories along with the tools used in each one. !e tools used in analytics may overlap and the use of one or the other type of analytics depends on the applications. A #rm may use only the descriptive analytics tools or a combination of descriptive and predictive analytics depending upon the types of applications, analyses, and deci- sions they encounter. Types of Business Analytics and Their Objectives !e term business analytics (BA) involves modeling and analysis of busi- ness data. BA is a powerful and complex #eld that incorporates wide application areas including descriptive analytics including data visual- ization, statistical analysis and modeling; predictive analytics, text and speech analytics, web analytics, decision processes, prescriptive analytics including optimization models, simulation, and much more. Table 2.1 brie$y describes the objectives of each of the analytics. Table 2.1 Objective of each of the analytics Type of Analytics Objectives Descriptive Use graphical and numerical methods to describe the data. The tools of descriptive analytics are helpful in understand- ing the data, identifying the trend or pattern in the data, and making sense from the data contained in the databases of companies. Predictive Predictive analytics is the application of predictive models that are used to predict future trends. Prescriptive Prescriptive analytics is concerned with optimal allocation of resources in an organization using a number of operations research, management science, and simulation tools. This document is authorized for use only by BAH SAFIA in Business Intelligence - DAT-8564 - BOS1 at Hult International Business School, 2021. 25 BUSINESS ANALYTICS AND BUSINESS INTELLIGENCE Input to Business Analytics, Types of Business Analytics, and Their Purpose !e $ow chart in Figure 2.1 shows the overall business analytics (BA) process. It shows the inputs to the process that mainly consist of business intelligence (BI) reports, business database, and cloud data repository. Figure 2.1 Input to the business analytics process, types of analytics, and description of tools in each type of analytics Figure 2.1 lists the purpose of each of the analytics—descriptive, pre- dictive, and prescriptive—and the problems they attempt to address are outlined below the top input row. For each type of BA, the analyses per- formed and a brief description of the tools are also presented. This document is authorized for use only by BAH SAFIA in Business Intelligence - DAT-8564 - BOS1 at Hult International Business School, 2021. 26 BUSINESS ANALYTICS, VOLUME II Tools of Each Type of Analytics and Their Objectives A summary of the tools used in each type of analytics and their objectives is listed in Tables 2.2, 2.3, and 2.4. !e tables also outline the questions each of the analytics tries to answer. !e three types of analytics are dependent and overlap in applications. !e tools of analytics sometimes are used in combination. Figure 2.2 shows the interdependence of the tools used in analytics. Table 2.2 Descriptive analytics, questions they attempt to answer, and their tools Analytics Attempts to Answer Tools Descriptive How can we understand the occurrence of certain business phenomenon or outcomes and explain: • Why did something happen? • Will it happen again? • What will happen if we make changes to some of the inputs? • What the data is telling us that we were not able to see before? • Using data, how can we visualize and explore what has been happen- ing and the possible rea- sons for the occurrence of certain phenomenon? • Concepts of data, types of data, data quality, and measurement scales for data. • Data visualization tools—graphs and charts along with some newly de- veloped graphical tools such as bullet graphs, tree maps, and data dash- boards. Dashboards are used to display the multiple views of the business data graphically. Big data visualization and analysis. • Descriptive statistics including the measures of central tendency, meas- ures of position, measures of variation, and measures of shape. • Relationship between two variables— the covariance and correlation coef!cient. • Other tools of descriptive analyt- ics are helpful in understanding the data, identifying the trend or pat- terns in the data, and making sense from the data contained in the data- bases of companies. The understand- ing of databases, data warehouse, web search and query, and big data applications. This document is authorized for use only by BAH SAFIA in Business Intelligence - DAT-8564 - BOS1 at Hult International Business School, 2021. 27 BUSINESS ANALYTICS AND BUSINESS INTELLIGENCE Table 2.3 Predictive analytics, questions they attempt to answer, and their tools Analytics Attempts to Answer Tools • How the trends and pat- terns identi!ed in the data can be used to pre- dict the future business outcome(s)? • How can we identify appropriate prediction models? • How the models can be used in making predic - tion about how things will turn out in the future—what will hap- pen in the future? • How can we predict the future trends of the key performance indica- tors using the past data and models and make predictions? Predictive • Regression models including: (a) simple regression models; (b) mul- tiple regression models; (c) nonlinear regression models, including the quadratic or second-order models, and polynomial regression models; (d) regression models with indicator or qualitative independent variables; and (e) regression models with interaction terms or interaction models. • Forecasting techniques. Widely used predictive models involve a class of time series analysis and forecasting mod- els. The commonly used forecasting models are regression-based models that use regression analysis to fore- cast future trend. Other time series forecasting models are simple moving average, moving average with trend, exponential smoothing, exponential smoothing with trend, and forecasting seasonal data. • Analysis of variance (ANOVA) and design of experiments techniques. • Data mining techniques—used to extract useful information from huge amounts of data known as knowledge discovery from database (KDD) using predictive data mining algorithms, software, and mathematical and statis- tical tools. • Prerequisite for predictive modeling: (a) probability and probability dis- tributions and their role in decision making, (b) sampling and inference procedures, (c) estimation and con!- dence intervals, (d) hypothesis testing/ inference procedures for one and two population parameters, and (e) chi- square and nonparametric tests. • Other tools of predictive analytics: ma- chine learning, arti!cial intelligence, neural networks, and deep learning (discussed later). This document is authorized for use only by BAH SAFIA in Business Intelligence - DAT-8564 - BOS1 at Hult International Business School, 2021. 28 BUSINESS ANALYTICS, VOLUME II Table 2.4 Prescriptive analytics, questions they attempt to answer, and their tool Analytics Attempts to Answer Tools Prescriptive • How can we optimally allocate resources in an organization? • How can the linear, nonlinear optimization, and simulation tools can be used for optimiz- ing business processes and optimal allocation of resources? A number of operations research and man- agement science tools • Operations management tools derived from management science