The intensified used of data based on analytical models to control digitalized operational business processes in an intelligent way is a game changer that continuously disrupts more and more markets. This book exemplifies this development and shows the latest tools and advances in this field Business Analytics for Managers offers real-world guidance for organizations looking to leverage their data into a competitive advantage. This new second edition covers the advances that have revolutionized the field since the first edition's release; big data and real-time digitalized decision making have become major components of any analytics strategy, and new technologies are allowing businesses to gain even more insight from the ever-increasing influx of data. New terms, theories, and technologies are explained and discussed in terms of practical benefit, and the emphasis on forward thinking over historical data describes how analytics can drive better business planning. Coverage includes data warehousing, big data, social media, security, cloud technologies, and future trends, with expert insight on the practical aspects of the current state of the field.
Analytics helps businesses move forward. Extensive use of statistical and quantitative analysis alongside explanatory and predictive modeling facilitates fact-based decision making, and evolving technologies continue to streamline every step of the process. This book provides an essential update, and describes how today's tools make business analytics more valuable than ever.
- Learn how Hadoop can upgrade your data processing and storage
- Discover the many uses for social media data in analysis and communication
- Get up to speed on the latest in cloud technologies, data security, and more
- Prepare for emerging technologies and the future of business analytics
Most businesses are caught in a massive, non-stop stream of data. It can become one of your most valuable assets, or a never-ending flood of missed opportunity. Technology moves fast, and keeping up with the cutting edge is crucial for wringing even more value from your data—Business Analytics for Managers brings you up to date, and shows you what analytics can do for you now.
Table of Contents:
Foreword xi
Introduction xiii
What is the Scope of Business Analytics? Information Systems—Not Technical Solutions xvii
Purpose and Audience xix
Organization of Chapters xxiii
Why the Term Business Analytics? xxiv
Chapter 1 The Business Analytics Model 1
Overview of the Business Analytics Model 2
Strategy Creation 4
Business Processes and Information Use 4
Types of Reporting and Analytical Processes 5
Data Warehouse 5
Data Sources: IT Operations and Development 5
Deployment of the Business Analytics Model 6
Case Study: How to Make an Information Strategy for a Radio Station 6
Summary 13
Chapter 2 Business Analytics at the Strategic Level 17
Link between Strategy and the Deployment of Business Analytics 19
Strategy and Business Analytics: Four Scenarios 20
Scenario 1: No Formal Link between Strategy and Business Analytics 22
Scenario 2: Business Analytics Supports Strategy at a Functional Level 24
Scenario 3: Dialogue between the Strategy and the Business Analytics Functions 28
Scenario 4: Information as a Strategic Resource 30
Which Information Do We Prioritize? 32
The Product and Innovation Perspective 34
Customer Relations Perspective 38
The Operational Excellence Perspective 42
Summary 44
Chapter 3 Development and Deployment of Information at the Functional Level 47
Case Study: A Trip to the Summerhouse 50
Specification of Requirements 51
Technical Support 52
Off We Go to the Summerhouse 53
Lead and Lag Information 54
More about Lead and Lag Information 57
Establishing Business Processes with the Rockart Model 59
Example: Establishing New Business Processes with the Rockart Model 61
Level 1: Identifying the Objectives 62
Level 2: Identifying an Operational Strategy 62
Level 3: Identifying the Critical Success Factors 64
Level 4: Identifying Lead and Lag Information 66
Optimizing Existing Business Processes 72
Example: Deploying Performance Management to Optimize Existing Processes 73
Concept of Performance Management 74
Which Process Should We Start With? 78
Customer Relationship Management Activities 80
Campaign Management 84
Product Development 85
Web Log Analyses 86
Pricing 89
Human Resource Development 91
Corporate Performance Management 93
Finance 94
Inventory Management 95
Supply Chain Management 95
Lean 97
A Catalogue of Ideas with Key Performance Indicators for the Company’s Different Functions 99
Summary 101
Chapter 4 Business Analytics at the Analytical Level 103
Data, Information, and Knowledge 106
Analyst’s Role in the Business Analytics Model 107
Three Requirements the Analyst Must Meet 109
Business Competencies 110
Tool Kit Must Be in Order (Method Competencies) 111
Technical Understanding (Data Competencies) 112
Required Competencies for the Analyst 113
Analytical Methods (Information Domains) 113
How to Select the Analytical Method 114
The Three Imperatives 116
Descriptive Statistical Methods, Lists, and Reports 122
Hypothesis-Driven Methods 129
Tests with Several Input Variables 130
Data Mining with Target Variables 133
Data Mining Algorithms 139
Explorative Methods 140
Data Reduction 141
Cluster Analysis 141
Cross-Sell Models 142
Up-Sell Models 143
Business Requirements 143
Definition of the Overall Problem 144
Definition of Delivery 144
Definition of Content 145
Summary 147
Chapter 5 Business Analytics at the Data Warehouse Level 149
Why a Data Warehouse? 151
Architecture and Processes in a Data Warehouse 154
Selection of Certain Columns To Be Loaded 156
Staging Area and Operational Data Stores 158
Causes and Effects of Poor Data Quality 159
The Data Warehouse: Functions, Components, and Examples 162
Alternative Ways of Storing Data 170
Business Analytics Portal: Functions and Examples 171
Tips and Techniques in Data Warehousing 175
Master Data Management 175
Service-Oriented Architecture 176
How Should Data Be Accessed? 177
Access to Business Analytics Portals 178
Access to Data Mart Areas 180
Access to Data Warehouse Areas 181
Access to Source Systems 182
Summary 183
Chapter 6 The Company’s Collection of Source Data 185
What are Source Systems, and What Can They Be Used For? 187
Which Information is Best to Use for Which Task? 192
When There is More Than One Way to Get the Job Done 194
When the Quality of Source Data Fails 197
Summary 198
Chapter 7 Structuring of a Business Analytics Competency Center 199
What is a Business Analytics Competency Center? 201
Why Set Up a Business Analytics Competency Center? 202
Tasks and Competencies 203
Establishing an Information Wheel 203
Creating Synergies between Information Wheels 205
Educating Users 207
Prioritizing New Business Analytics Initiatives 208
Competencies 208
Centralized or Decentralized Organization 208
Strategy and Performance 210
When the Analysts Report to the IT Department 213
When Should a Business Analytics Competency Center Be Established? 215
Applying the Analytical Factory Approach 217
Summary 219
Chapter 8 Assessment and Prioritization of Business Analytics Projects 221
Is It a Strategic Project or Not? 222
Uncovering the Value Creation of the Project 224
When Projects Run Over Several Years 230
When the Uncertainty is Too Big 232
The Descriptive Part of the Cost/Benefit Analysis for the Business Case 233
The Cost/Benefit Analysis Used for the Business Case 235
Projects as Part of the Bigger Picture 235
Case Study on How to Make an Information Strategy Roadmap 240
Summary 243
Chapter 9 Business Analytics in the Future 247
About the Authors 255
Index 257
About the Author :
GERT H. N. LAURSEN is a business consultant who builds analytical organizations around the world. He also builds disruptive business strategies for global market leaders and humanitarian organizations. He has an MBA in digital strategy, a master’s degree in marketing, and was named a global thought leader by IBM and SAS Institute.
JESPER THORLUND is a business intelligence consultant and frequent speaker on business intelligence, business analytics, and microeconomics throughout Europe.
