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Business Analytics: Text and Cases [Pehme köide]

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  • Formaat: Paperback / softback, 332 pages, kõrgus x laius: 241x184 mm, kaal: 600 g
  • Ilmumisaeg: 21-Jan-2020
  • Kirjastus: SAGE Publications India Pvt Ltd
  • ISBN-10: 9353287103
  • ISBN-13: 9789353287108
Teised raamatud teemal:
Business Analytics: Text and CasesSuurem pilt
  • Formaat: Paperback / softback, 332 pages, kõrgus x laius: 241x184 mm, kaal: 600 g
  • Ilmumisaeg: 21-Jan-2020
  • Kirjastus: SAGE Publications India Pvt Ltd
  • ISBN-10: 9353287103
  • ISBN-13: 9789353287108
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789353287108.html
Märksõnad:
This textbook is a comprehensive, step-by-step learning guide to each aspect of business analytics and its role and significance in real-life business decision-making. 

This textbook is a comprehensive, step-by-step learning guide to each aspect of business analytics and its role and significance in real-life business decision-making. 

Correct capture, analysis and interpretation of data can have an immense impact on business productivity. Therefore, business analytics has turned out to be a strategic need for sustainability and growth in this competitive world. Descriptive, predictive and prescriptive models and data mining techniques are increasingly being used to interpret large quantities of data for getting useful business insights. 

Business Analytics: Text and Cases deals with the end-to-end journey from planning the approach to a data-enriched decision-problem, to communicating the results derived from analytics models to clients. Using cases from all aspects of a business venture (finance, marketing, human resource and operations), the book helps students to develop the skill to evaluate a business case scenario, understand the business problems, identify the data sources and data availability, logically think through problem-solving, use analytics techniques and application software to solve the problem and be able to interpret the results.

Key Features:

•Case studies of three degrees of difficulty level to enhance better understanding of the concepts

•Application of software tools such as Microsoft Excel, R, SPSS, RapidMiner and Tableau to assist learning in building models and communicating results using analytics, data mining and data visualization

•End of book Appendix consisting of step-by-step solved comprehensive case studies that discuss the concepts of all the chapters

•Special emphasis on the need to develop skill for interpreting the outcome from the statistical results and presenting it in a form easily understood by the end user/client

List of Figures
xiii
List of Tables
xix
List of Abbreviations
xxi
Foreword xxiii
Suresh Divakar
Preface xxv
Acknowledgements xxvii
About the Authors xxix
1 Introduction to Business Analytics
1(11)
What Is Business Analytics?
5(1)
Business Analytics and BI
5(1)
Business Analytics and BPM
5(1)
Linking Strategy to Execution
6(1)
The End-to-End Value Chain
6(1)
Decision-making with Analytical Outcomes for Technical and Non-technical End Users
6(1)
Review Questions
7(1)
Case 1.1 A New Venture for an Untapped Market (Case Complexity: Easy)
7(1)
Case 1.2 Kirana Store Introduces a Reward Programme (Case Complexity: Medium)
8(2)
Case 1.3 Inventory Tracking Analysis by Jaishankar Tripathi (Case Complexity: Hard)
10(2)
2 Data Analytics for Business
12(20)
Planning for Analytics in Organizations
13(1)
Challenges of Setting Analytics Culture in Organizations
14(3)
Organizational Design for Impactful Analytics
17(6)
Data Analytics in HR, Marketing, Operations and Finance
23(2)
Review Questions
25(1)
Case 2.1 Measuring Customer Satisfaction of Service Experience (Case Complexity: Easy)
25(2)
Case 2.2 Business Analytics in an Oil Refinery (Case Complexity: Medium)
27(2)
Case 2.3 Bedding and Accessories Firm Embraces Business Analytics Culture (Case Complexity: Hard)
29(3)
3 Data Exploration in Business Analytics
32(14)
Understand Different Sources of Data
34(1)
Understand Different Types of Data
34(5)
Identify Relevant Data Points to Address Business Objectives
39(1)
What Is Available versus What Is Good to Have
40(1)
Untapped Data Sources
40(1)
Review Questions
41(1)
Case 3.1 Where Is the Data in the Cars? (Case Complexity: Easy)
41(2)
Case 3.2 Salary Packages of Graduating Students (Case Complexity: Medium)
43(1)
Case 3.3 What Went Wrong at Rozana Terminal? (Case Complexity: Hard)
44(2)
4 Mapping Chart for Analytics Outcome
46(16)
Planning the Analytics Road Map
47(2)
Problem Formulation: Identification and Simplification into Manageable Parts
49(1)
Designing the Research Matrix
50(4)
Building a Model: Connecting the Research Question to a Resolution Approach
54(4)
Conclusion
58(1)
Review Questions
58(1)
Case 4.1 Managing Change during a Technology Upgrade in a Garment
Manufacturing Company (Case Complexity: Easy)
58(1)
Case 4.2 Students Gear Up for Planning the Annual Management Symposium (Case Complexity: Medium)
59(1)
Case 4.3 Introducing Reverse Mentoring at Saujanya Bank (Case Complexity: Hard)
60(2)
5 Technology Infrastructure for Business Analytics
62(18)
Relating IT to BA
63(1)
Checks for an Organization's Technology Readiness for BA
64(5)
Role of Data Warehousing in BA
69(1)
Data Lakes versus Data Marts
70(1)
Cloud Computing and BA
70(1)
Internet of Things (IoT) Complements BA
71(1)
The Analytics-powered Organization
72(2)
Review Questions
74(1)
Case 5.1 Role of Data Science in Upstream Oil and Gas Companies in India (Case Complexity: Easy)
74(2)
Case 5.2 Identify IT Upgrade Need in a Pharmaceutical Company (Case Complexity: Medium)
76(1)
Case 5.3 Business Analytics Questionnaire for Fast Food Chain (Case Complexity: Hard)
77(3)
6 Analytical Methods for Parametric and Non-parametric Data
80(23)
Significance of Sampling in Business Research
81(2)
Confidence Interval and Hypothesis Testing
83(2)
Cross-tabulation
85(1)
Correlation
86(1)
Factor Analysis
86(3)
Regression (OLS) Models
89(3)
Multicollinearity
92(1)
Forecasting and Time Series Analysis
93(2)
Heteroscedasticity in Time Series Models
95(2)
Conclusion
97(1)
Review Questions
97(1)
Case 6.1 Should Vishwa Take the Loan? (Case Complexity: Easy)
97(2)
Case 6.2 Who Is the Star Sales Representative Performer for the Month at Laced Education? (Case Complexity: Medium)
99(2)
Case 6.3 What Drives Customer Satisfaction at a Gas Station? (Case Complexity: Hard)
101(2)
7 Analytical Methods for Complex Data
103(29)
Analytical Methods for Discrete Data: Logistic Regression Models
104(4)
Estimating Logit Models
108(1)
Evaluating the Performance of the Logit Model
109(1)
Other `Separation' Models
110(1)
Probit Models
110(1)
Choice-based `Conjoint' Methodology
111(13)
Conclusion
124(2)
Review Questions
126(1)
Case 7.1 Tranquils'.com Determines Leads (sales) from Its Website (Case Complexity: Easy)
127(1)
Case 7.2 Hotel Owner Decides to Measure Quality of Service (Case Complexity: Medium)
127(2)
Case 7.3 Budget Accommodation for College-going Students (Case Complexity: Hard)
129(3)
8 Data Mining Methods in Business Analytics
132(25)
What Is Data Mining?
133(1)
Data Mining and BA
134(1)
Data Mining and Machine Learning
134(1)
Data Mining Method: Cluster Analysis
135(4)
Data Mining Method: Association Technique
139(3)
Data Mining Method: Classification Decision Tree
142(1)
Data Mining Method: Classification Logistic Regression Analytics
143(4)
Data Mining Method: Prediction---Linear Regression
147(2)
Data Mining Method: Text Analytics
149(4)
Real Analytics: Text and Web Analytics in Online Grocery
153(1)
Review Questions
154(1)
Case 8.1 Switching Cell Phones: A Gender- and Age-related Analysis at Panacea (Case Complexity: Easy)
154(1)
Case 8.2 Infant Mortality Rate versus Income (Case Complexity: Medium)
155(1)
Case 8.3 What Drives Popularity among School-going Kids? (Case Complexity: High)
156(1)
9 Interpreting the Statistical Outcomes
157(40)
Need for Developing Skill to Understand the Statistical Outcome
158(1)
Data Visualization Methods
159(33)
Review Questions
192(1)
Case 9.1 The Dream Employee (Case Complexity: Easy)
193(1)
Case 9.2 Bouncing the Word Cloud (Case Complexity: Medium)
194(1)
Case 9.3 Speedometer Gauge Measures Business Performance (Case Complexity: High)
195(2)
10 Documenting the Processes
197(5)
Need for Building a Systematic Documentation
197(1)
Documenting the Processes
198(1)
Steps Followed for Analysis
199(1)
Capturing Relevant Information Sources: Print and Digital
199(1)
Documenting Client Conversations
200(1)
Emphasizing Relevant Outcomes
200(1)
Conclusion
201(1)
Review Questions
201(1)
11 Building the Storyboard of Outcomes
202(1)
Analysing to Convincing Storytelling: Are They Different?
203(1)
Building Appropriate Dashboards
203(5)
Telling Relevant Stories from Analysis: What Does It Take?
208(3)
Communicating to the Client
211(4)
Review Questions
215(1)
Case 11.1 Creating the Two-wheeler Dashboard (Case Complexity: Low)
216(1)
Case 11.2 `About India' Dashboard with the Data on Maps (Case Complexity: Medium)
217(1)
Case 11.3 What Should Be Naina's Dashboard Design? (Case Complexity: High)
217
Appendix A Case---Trasha Beverages Goes the Business Analytics Way
1(32)
Appendix B Business Case---Using Analytics for Business Problem-solving
33(17)
Debt Collections in the Consumer Finance Industry
33(1)
The Business of Consumer Credit Collections: Setting the Context
34(1)
Managing Risk
35(1)
The BP to Be Addressed
36(3)
Designing the Test Pilot Operation
39(3)
Planned Analysis of the Output of the Pilot
42(2)
Interpretation of the Model Result
44(1)
Is the Experiment Successful? Response Rates in the Control Group
45(1)
Financial Implications of the Analysis
46(1)
Follow-up and Validation
47(1)
Implications on the Indian Financial Services Industry
47(1)
End Note
48(1)
References
49(1)
Appendix C Online Grocery Case
50(11)
Introduction
50(9)
Practical Implications
59(1)
Originality/Value
59(1)
References
60(1)
Appendix D Tips on Using Software Used in This Book
61(1)
Microsoft Excel
61(4)
R Software
65(3)
Rapid Miner Software
68(2)
SPSS Software
70(5)
Tableau Software
75
Index 1
Tanushri Banerjee is an Associate Professor of Information Systems at the Business School, Pandit Deendayal Petroleum University, Gandhinagar. She has 20 years of work experience divided between academia and industry. Her career is a mix of national and international job profiles, which has led her to blend industrial technological advancements into evolving academic curriculum. Prior to her current position, she was the associate director at Duke Corporate Education (India), a joint venture between Duke University and IIM Ahmedabad. Her responsibilities included steering the operations and systems infrastructure management initiatives for India along with the design and delivery of the Global Executive Program. Additionally, she has also held positions in the systems function at Abbott Laboratories Inc., Chicago, and at Torrent Labs at Ahmedabad. Tanushri is an MS in information sciences from the University of Illinois at Chicago and PhD in management from the Maharaja Sayajirao University of Baroda. Her current interest areas are business intelligence and data-driven decision making

Arindam Banerjee is a Professor of Marketing at IIM Ahmedabad. He has been associated with the institute for the past 18 years. Prior to his joining the institute, he was an analytics specialist in a management consulting firm in Chicago, IL. Before that he worked at AC Nielsen Corporation, United States, servicing the analytics requirement for Philip Morris Inc., where he gained market research experience. At IIM Ahmedabad, he teaches, consults and researches in marketing analytics and strategy. He has been involved in setting up the offshore analytics operations for a global bank in India and has been an analytics mentor for several other organizations. He is a PhD in marketing science from the State University of New York at Buffalo and a PGDM from IIM Lucknow. He was listed in the 10 Most Prominent Analytics Academicians in India, 2017 by Analytics India Magazine. He is also the author of Management Essentials: A Recipe for Business Success published by SAGE in 2013.