Business Analytics for Managers Taking Business Intelligence Beyond Reporting

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.

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.

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

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