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Fundamentals of business analytics

By: Contributor(s): Publication details: New Delhi Wiley India Pvt. Ltd. 2016Edition: Ed.2Description: xxiii,403p.,CD-ROMISBN:
  • 9788126563791
Subject(s):
DDC classification:
  • 658.403 PRA
Contents:
CONTENTS Foreword v Preface vii Acknowledgments xi About the Authors xiii 1 Business View of Information Technology Applications 1-18 1.1 Business Enterprise Organization, Its Functions, and Core Business Processes 1.2 Baldrige Business Excellence Frameworks (Optional Reading) 1.3 Key Purpose of using IT in Business 1.4 The Connected World: Characteristics of Internet-Ready IT Applications 1.5 Enterprise Applications (ERP/CRM, etc.) and Bespoke IT Applications 1.6 Information Users and Their Requirements 2 Types of Digital Data 31-56 2.1 Introduction 2.2 Getting into “GoodLife” Database 2.3 Getting to Know Structured Data 2.4 Getting to Know Unstructured Data 2.5 Getting to Know Semi-Structured Data 2.6 Difference between Semi-Structured and Structured Data 3 Introduction to OLTP and OLAP 59-86 3.1 OLTP (On-Line Transaction Processing) 3.2 OLAP (On-Line Analytical Processing) 3.3 Different OLAP Architectures 3.4 OLTP and OLAP 3.5 Data Models for OLTP and OLAP 3.6 Role of OLAP Tools in the BI Architecture 3.7 Should OLAP be Performed Directly on Operational Databases? 3.8 A Peek into the OLAP Operations on Multidimensional Data 3.9 Leveraging ERP Data Using Analytics 4 Getting Started with Business Intelligence 87-116 4.1 Using Analytical Information for Decision Support 4.2 Information Sources Before Dawn of BI? 4.3 Definitions and Examples in Business Intelligence, Data Mining, Analytics, Machine Learning, Data Science 4.4 Looking at “Data” from Many Perspectives 4.5 Business Intelligence (BI) Defined 4.6 Why BI? How Can You Achieve Your Stated Objectives? 4.7 Some Important Questions About BI - Where, When and What 4.8 Evolution of BI and Role of DSS, EIS, MIS, and Digital Dashboards 4.9 Need for BI at Virtually all Levels 4.10 BI for Past, Present, and Future 4.11 The BI Value Chain 4.12 Introduction to Business Analytics 5 BI Definitions and Concepts 117-148 5.1 BI Component Framework 5.2 Who is BI for? 5.3 BI Users 5.4 Business Intelligence Applications 5.5 BI Roles and Responsibilities 5.6 Best Practices in BI/DW 5.7 The Complete BI Professional 5.8 Popular BI Tools 6 Basics of Data Integration 151-203 6.1 Need for Data Warehouse 6.2 Definition of Data Warehouse 6.3 What is a Data Mart? 6.4 What is then an ODS? 6.5 Ralph Kimball’s Approach vs. W.H. Inmon’s Approach 6.6 Goals of a Data Warehouse 6.7 What Constitutes a Data Warehouse? 6.8 Extract, Transform, Load 6.9 What is Data Integration? 6.10 Data Integration Technologies 6.11 Data Quality 6.12 Data Profiling 7 Multidimensional Data Modeling 205-255 7.1 Introduction 7.2 Data Modeling Basics 7.3 Types of Data Model 7.4 Data Modeling Techniques 7.5 Fact Table 7.6 Dimension Table 7.7 Typical Dimensional Models 7.8 Dimensional Modeling Life Cycle 8 Measures, Metrics, KPIs and Performance Management 257-270 8.1 Understanding Measures and Performance 8.2 Measurement System Terminology 8.3 Navigating a Business Enterprise, Role of Metrics and Metrics Supply Chain 8.4 “Fact-Based Decision Making” and KPIs 8.5 KPI Usage in Companies 8.6 Where do Business Metrics and KPIs Come From? 8.7 Connecting the Dots: Measures to Business Decisions and Beyond 9 Basics of Enterprise Reporting 273-307 9.1 Reporting Perspectives Common to All Levels of Enterprise 9.2 Report Standardization and Presentation Practices 9.3 Enterprise Reporting Characteristics in OLAP World 9.4 Balanced Scorecard 9.5 Dashboards 9.6 How Do You Create Dashboards? 9.7 Scorecards vs. Dashboards 9.8 The Buzz behind Analysis 10 Understanding Statistics 309-327 10.1 Role of Statistics in Analytics 10.2 Data, Data Description and Summarization 10.3 Statistical Tests 10.4 Understanding Hypothesis and t-Test 10.5 Correlation Analysis 10.6 Regression 10.7 ANOVA 10.8 The F-Test 10.9 Time Series Analysis 11 Application of Analytics 331-343 11.1 Application of Analytics 11.2 Analytics in Industries 11.3 Widely Used Application of Analytics 12 Data Mining Algorithms 347-367 12.1 Association Rule Mining 12.2 k-Means Clustering 12.3 Decision Tree 13 BI Road Ahead 369-384 13.1 Understanding BI and Mobility 13.2 BI and Cloud Computing 13.3 Business Intelligence for ERP Systems 13.4 Social CRM and BI Unsolved Exercises Glossary 385 Index 397
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Item type Current library Collection Call number Status Date due Barcode Item holds
Book CEPT Library Faculty of Management 658.403 PRA Available 019088
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CONTENTS
Foreword v
Preface vii
Acknowledgments xi
About the Authors xiii
1 Business View of Information Technology Applications 1-18
1.1 Business Enterprise Organization, Its Functions, and Core Business Processes
1.2 Baldrige Business Excellence Frameworks (Optional Reading)
1.3 Key Purpose of using IT in Business
1.4 The Connected World: Characteristics of Internet-Ready IT Applications
1.5 Enterprise Applications (ERP/CRM, etc.) and Bespoke IT Applications
1.6 Information Users and Their Requirements
2 Types of Digital Data 31-56
2.1 Introduction
2.2 Getting into “GoodLife” Database
2.3 Getting to Know Structured Data
2.4 Getting to Know Unstructured Data
2.5 Getting to Know Semi-Structured Data
2.6 Difference between Semi-Structured and Structured Data
3 Introduction to OLTP and OLAP 59-86
3.1 OLTP (On-Line Transaction Processing)
3.2 OLAP (On-Line Analytical Processing)
3.3 Different OLAP Architectures
3.4 OLTP and OLAP
3.5 Data Models for OLTP and OLAP
3.6 Role of OLAP Tools in the BI Architecture
3.7 Should OLAP be Performed Directly on Operational Databases?
3.8 A Peek into the OLAP Operations on Multidimensional Data
3.9 Leveraging ERP Data Using Analytics
4 Getting Started with Business Intelligence 87-116
4.1 Using Analytical Information for Decision Support
4.2 Information Sources Before Dawn of BI?
4.3 Definitions and Examples in Business Intelligence, Data Mining, Analytics, Machine Learning, Data Science
4.4 Looking at “Data” from Many Perspectives
4.5 Business Intelligence (BI) Defined
4.6 Why BI? How Can You Achieve Your Stated Objectives?
4.7 Some Important Questions About BI - Where, When and What
4.8 Evolution of BI and Role of DSS, EIS, MIS, and Digital Dashboards
4.9 Need for BI at Virtually all Levels
4.10 BI for Past, Present, and Future
4.11 The BI Value Chain
4.12 Introduction to Business Analytics
5 BI Definitions and Concepts 117-148
5.1 BI Component Framework
5.2 Who is BI for?
5.3 BI Users
5.4 Business Intelligence Applications
5.5 BI Roles and Responsibilities
5.6 Best Practices in BI/DW
5.7 The Complete BI Professional
5.8 Popular BI Tools
6 Basics of Data Integration 151-203
6.1 Need for Data Warehouse
6.2 Definition of Data Warehouse
6.3 What is a Data Mart?
6.4 What is then an ODS?
6.5 Ralph Kimball’s Approach vs. W.H. Inmon’s Approach
6.6 Goals of a Data Warehouse
6.7 What Constitutes a Data Warehouse?
6.8 Extract, Transform, Load
6.9 What is Data Integration?
6.10 Data Integration Technologies
6.11 Data Quality
6.12 Data Profiling
7 Multidimensional Data Modeling 205-255
7.1 Introduction
7.2 Data Modeling Basics
7.3 Types of Data Model
7.4 Data Modeling Techniques
7.5 Fact Table
7.6 Dimension Table
7.7 Typical Dimensional Models
7.8 Dimensional Modeling Life Cycle
8 Measures, Metrics, KPIs and Performance Management 257-270
8.1 Understanding Measures and Performance
8.2 Measurement System Terminology
8.3 Navigating a Business Enterprise, Role of Metrics and Metrics Supply Chain
8.4 “Fact-Based Decision Making” and KPIs
8.5 KPI Usage in Companies
8.6 Where do Business Metrics and KPIs Come From?
8.7 Connecting the Dots: Measures to Business Decisions and Beyond
9 Basics of Enterprise Reporting 273-307
9.1 Reporting Perspectives Common to All Levels of Enterprise
9.2 Report Standardization and Presentation Practices
9.3 Enterprise Reporting Characteristics in OLAP World
9.4 Balanced Scorecard
9.5 Dashboards
9.6 How Do You Create Dashboards?
9.7 Scorecards vs. Dashboards
9.8 The Buzz behind Analysis
10 Understanding Statistics 309-327
10.1 Role of Statistics in Analytics
10.2 Data, Data Description and Summarization
10.3 Statistical Tests
10.4 Understanding Hypothesis and t-Test
10.5 Correlation Analysis
10.6 Regression
10.7 ANOVA
10.8 The F-Test
10.9 Time Series Analysis
11 Application of Analytics 331-343
11.1 Application of Analytics
11.2 Analytics in Industries
11.3 Widely Used Application of Analytics
12 Data Mining Algorithms 347-367
12.1 Association Rule Mining
12.2 k-Means Clustering
12.3 Decision Tree
13 BI Road Ahead 369-384
13.1 Understanding BI and Mobility
13.2 BI and Cloud Computing
13.3 Business Intelligence for ERP Systems
13.4 Social CRM and BI
Unsolved Exercises
Glossary 385
Index 397

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