GERT H.N. LAURSEN is head of customer intelligence at Maersk Line, the largest containerized shipping company in the world. He focuses on helping product-oriented organizations become more customer-centered through the use of various data sources, including data warehousing, questionnaires, and one-to-one interviews with customers, first-line staff, sales organizations, and other subject matter experts.
JESPER THORLUND is a business intelligence consultant and frequent speaker on business intelligence, business analytics, and microeconomics throughout Europe.
Foreword | p. ix |
Introduction | p. xi |
What Does BA Mean? Information Systems-Not Technical Solutions | p. xiv |
Purpose and Audience | p. xvi |
Organization of Chapters | p. xix |
Why the Term Business Analytics? | p. xx |
The Business Analytics Model | p. 1 |
Overview of the Business Analytics Model | p. 2 |
Deployment of the BA Model | p. 6 |
Conclusions | p. 12 |
Business Analytics at the Strategic Level | p. 17 |
Link Between Strategy and the Deployment of BA | p. 18 |
Strategy and BA: Four Scenarios | p. 19 |
Which Information Do We Prioritize? | p. 31 |
Summary | p. 40 |
Development and Deployment of Information at the Functional Level | p. 43 |
Case Study: A Trip to the summerhouse | p. 46 |
Establishing Business Processes with the Rockart Model | p. 55 |
Example: Establishing New Business Processes with the Rockart Model | p. 57 |
Optimizing Existing Business Processes | p. 65 |
Example: Deploying Performance Management to Optimize Existing Processes | p. 67 |
Which Process Should You Start with? | p. 72 |
A Catalogue of Ideas with KPIs for the Company's Different Functions | p. 90 |
Summary | p. 91 |
Business Analytics at the Analytical Level | p. 93 |
Data, Information, and Knowledge | p. 94 |
Analyst's Role in the BA Model | p. 95 |
Three Requirements the Analyst Must Meet | p. 98 |
Required Competencies for the Analyst | p. 101 |
Hypothesis-Driven Methods | p. 117 |
Data Mining with Target Variables | p. 120 |
Explorative Methods | p. 127 |
Business Requirements | p. 130 |
Summary | p. 134 |
Business Analytics at the Data Warehouse Level | p. 137 |
Why a Data Warehouse? | p. 137 |
Architecture and Processes in a Data Warehouse | p. 140 |
Tips and Techniques in Data Warehousing | p. 160 |
Summary | p. 168 |
The Company's Collection of Source Data | p. 169 |
What Are Source Systems, and What Can They Be Used for? | p. 170 |
Which Information Is Best to Use for Which Task? | p. 174 |
When There is More Than One Way to Get the Job Done | p. 177 |
When the Quality of Source Data Fails | p. 179 |
Summary | p. 180 |
Structuring of a Business Intelligence Competency Center | p. 183 |
What Is a Business Intelligence Competency Center? | p. 183 |
Why Set Up a Business Intelligence Competency Center? | p. 184 |
Tasks and Competencies | p. 185 |
Centralized or Decentralized Organization | p. 191 |
When Should a BICC Be Established? | p. 197 |
Summary | p. 200 |
Assessment and Prioritization of BA Projects | p. 201 |
Is it a Strategic Project or Not? | p. 201 |
Uncovering the Value Creation of the Project | p. 203 |
When Projects Run Over Several Years | p. 209 |
When the Uncertainty Is Too Big | p. 211 |
Projects as Part of the Bigger Picture | p. 214 |
Summary | p. 222 |
Business Analytics in the Future | p. 223 |
Index | p. 231 |
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