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Business Analytics for Managers By Gert H. N. Laursen

Business Analytics for Managers by Gert H. N. Laursen

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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.

Business Analytics for Managers Summary

Business Analytics for Managers: Taking Business Intelligence Beyond Reporting by Gert H. N. Laursen

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.

About Gert H. N. Laursen

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.

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

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Business Analytics for Managers: Taking Business Intelligence Beyond Reporting by Gert H. N. Laursen
John Wiley & Sons Inc
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