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Table of Contents
Intro
Foreword by Vanessa Stützle
Preface
Contents
1: Background and Drivers of the Data-Driven Organization
1.1 Business Intelligence Development
1.2 Drivers of the Data-Driven Organization
1.2.1 Change in the Technological Environment
1.2.2 Changed Decision Situation
1.2.3 Changing Competition and New Business Models
1.2.4 Changing Customer Behavior
1.2.5 Drivers Summary
References
2: Characteristics of the Data-Driven Organization
2.1 Derivation of the Data-Driven Organization
2.1.1 What Is Data?
2.1.2 What Is a Data-Driven Business?
2.2 What Do "Better" Choices Mean?
2.3 Maturity Levels of Data-Driven Companies
2.4 Properties of Data for the Data-Driven Organization
2.5 Types of Analyses
2.6 Advantages of a Data-Driven Company
References
3: Challenges and Barriers of the Data-Driven Organization
3.1 Empirical Studies on Challenges and Barriers
3.2 Summary of Findings and Evaluation
References
4: Process Model for Data Management
4.1 The Five Steps
4.2 Collect-Collect Data
4.2.1 What Is Data?
4.2.2 How Can We Differentiate Data?
4.2.3 Which Data from Which Sources Can Be Used?
4.2.4 More Data, More Knowledge?
4.2.5 How Do Data Silos Arise and How Do We Deal with Them?
4.2.6 What Criteria Are Relevant in the Choice of Technology?
4.2.7 What General Conditions Do We Have to Consider?
4.2.8 Guiding Questions for Collect
4.3 Understand-Understanding the Collected Data
4.3.1 Why Is Understanding Central?
4.3.2 What Conditions Do We Need to Be Able to Understand?
4.3.2.1 Technical Requirements
4.3.2.2 Analytical Requirements
4.3.3 What Must a Technical Preparation Look Like?
4.3.4 How Can We Tap into Data?
4.3.5 What Does Emotionalizing Data Mean?
4.3.6 How Can We Facilitate an Understanding?
4.3.6.1 Reference to a Comparable Size
4.3.6.2 Establishing a Time Reference
4.3.6.3 Reference to Known Objects
4.3.7 Guiding Questions for Understand
4.4 Decide-Decide on the Basis of the Collected Data
4.4.1 What Distinguishes a Data-Driven Decision from a Gut Decision?
4.4.2 What Types of Decisions Are Made in Companies?
4.4.3 What Are the Requirements for Making a Good Decision?
4.4.4 What Role Does the Time Factor Play in Decisions?
4.4.5 How Can We Visualize Data?
4.4.6 Data Versus Gut-Or Better in Combination?
4.4.7 Guiding Questions for Decide
4.5 Automate-Automation
4.5.1 Why Can't We Get Around Automation?
4.5.2 What Are the Technical Requirements for Automation?
4.5.3 What Added Value Does AI Create in the Context of Automation?
4.5.4 Is Automation Even More Than AI?
4.5.5 How Do We Manage to Transfer Our Findings into Processes in an Automated Way?
4.5.6 What Can Be the Causes of Resistance to the Data-Driven Organization?
4.5.7 Guiding Questions for Automate
4.6 Summary
Foreword by Vanessa Stützle
Preface
Contents
1: Background and Drivers of the Data-Driven Organization
1.1 Business Intelligence Development
1.2 Drivers of the Data-Driven Organization
1.2.1 Change in the Technological Environment
1.2.2 Changed Decision Situation
1.2.3 Changing Competition and New Business Models
1.2.4 Changing Customer Behavior
1.2.5 Drivers Summary
References
2: Characteristics of the Data-Driven Organization
2.1 Derivation of the Data-Driven Organization
2.1.1 What Is Data?
2.1.2 What Is a Data-Driven Business?
2.2 What Do "Better" Choices Mean?
2.3 Maturity Levels of Data-Driven Companies
2.4 Properties of Data for the Data-Driven Organization
2.5 Types of Analyses
2.6 Advantages of a Data-Driven Company
References
3: Challenges and Barriers of the Data-Driven Organization
3.1 Empirical Studies on Challenges and Barriers
3.2 Summary of Findings and Evaluation
References
4: Process Model for Data Management
4.1 The Five Steps
4.2 Collect-Collect Data
4.2.1 What Is Data?
4.2.2 How Can We Differentiate Data?
4.2.3 Which Data from Which Sources Can Be Used?
4.2.4 More Data, More Knowledge?
4.2.5 How Do Data Silos Arise and How Do We Deal with Them?
4.2.6 What Criteria Are Relevant in the Choice of Technology?
4.2.7 What General Conditions Do We Have to Consider?
4.2.8 Guiding Questions for Collect
4.3 Understand-Understanding the Collected Data
4.3.1 Why Is Understanding Central?
4.3.2 What Conditions Do We Need to Be Able to Understand?
4.3.2.1 Technical Requirements
4.3.2.2 Analytical Requirements
4.3.3 What Must a Technical Preparation Look Like?
4.3.4 How Can We Tap into Data?
4.3.5 What Does Emotionalizing Data Mean?
4.3.6 How Can We Facilitate an Understanding?
4.3.6.1 Reference to a Comparable Size
4.3.6.2 Establishing a Time Reference
4.3.6.3 Reference to Known Objects
4.3.7 Guiding Questions for Understand
4.4 Decide-Decide on the Basis of the Collected Data
4.4.1 What Distinguishes a Data-Driven Decision from a Gut Decision?
4.4.2 What Types of Decisions Are Made in Companies?
4.4.3 What Are the Requirements for Making a Good Decision?
4.4.4 What Role Does the Time Factor Play in Decisions?
4.4.5 How Can We Visualize Data?
4.4.6 Data Versus Gut-Or Better in Combination?
4.4.7 Guiding Questions for Decide
4.5 Automate-Automation
4.5.1 Why Can't We Get Around Automation?
4.5.2 What Are the Technical Requirements for Automation?
4.5.3 What Added Value Does AI Create in the Context of Automation?
4.5.4 Is Automation Even More Than AI?
4.5.5 How Do We Manage to Transfer Our Findings into Processes in an Automated Way?
4.5.6 What Can Be the Causes of Resistance to the Data-Driven Organization?
4.5.7 Guiding Questions for Automate
4.6 Summary