Data has become an indispensable success factor for every company. However, the road towards a data-driven organization is paved with numerous challenges. This book presents a process model for the path to a data-driven company and provides recommendations for the design of all relevant fields of action: Which structures need to be created? Which systems and processes have proven beneficial? How can the quality of the data be ensured and what requirements exist for a data-driven organization in the areas of governance and communication? And last but not least: How can employees be brought along on the journey and what implications does the data-driven organization have for our corporate culture? The book presents an orientation and action framework for the strategic and operational design of a data-driven organization and is valuable for managers who are involved in data management in companies and organizations.
Author(s): Jonas Rashedi
Series: Business Guides on the Go
Publisher: Springer
Year: 2022
Language: English
Pages: 131
City: Cham
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
References
5: Process Model for Implementing the Data-Driven Organization
5.1 Overview
5.2 Status Quo, Goals, and Data Strategy
5.2.1 Internal and External Analysis
5.2.1.1 Internal Analysis
5.2.1.2 External Analysis
5.2.1.3 Summary
5.2.2 Data Targets
5.2.3 Data Strategy
5.2.3.1 Definition
5.2.3.2 Delimitation
5.2.3.3 Development of the Data Strategy
5.2.3.4 Statement Areas of Strategies
5.2.3.5 Success Factors for the Development of the Data Strategy
5.2.4 Stakeholder Integration
5.3 Organization Model
5.4 Process Model
5.5 Example Projects
5.5.1 Self-Services and Real-Time Services at the Schwarz Group
5.5.2 Data & Analytics in B2B
5.5.3 Configuration of the Collaboration Between Data & Analytics and the Business Departments at a Fashion Company
5.5.4 Re-launching Data & Analytics at a Content Provider in the Sports Sector
5.6 Tools
5.7 Data Culture
5.8 Talent Management and Talent Strategy
5.9 Data Governance
References
6: Closing Words
