Professional data management is the foundation for the successful digital transformation of traditional companies. Unfortunately, many companies fail to implement data governance because they do not fully understand the complexity of the challenge (organizational structure, employee empowerment, change management, etc.) and therefore do not include all aspects in the planning and implementation of their data governance.
This book explains the driving role that a responsive data organization can play in a company's digital transformation. Using proven process models, the book takes readers from the basics, through planning and implementation, to regular operations and measuring the success of data governance. All the important decision points are highlighted, and the advantages and disadvantages are discussed in order to identify digitization potential, implement it in the company, and develop customized data governance. The book will serve as a useful guide for interested newcomers as well as for experienced managers.
Author(s): Lars Michael Bollweg
Series: Management for Professionals
Publisher: Springer
Year: 2022
Language: English
Pages: 166
City: Berlin
Foreword
Contents
About the Author
1: Introduction
Reference
Part I: Basics
2: What Is Data Governance?
2.1 Basics and Definition of Data Governance
2.2 Levels of Complexity
2.3 Data Lifecycle
2.4 Data Responsibility
2.5 Roles of Data Governance
2.6 Structures of Data Governance
Example
References
Part II: Design
3: Success Factors for the Implementation
3.1 Provide Resources
3.2 Identify Implementation Drivers
3.3 Develop Data Management Capabilities
Data Management Practices
3.4 Select the Organizational Structure
3.5 Create Added Value: Right Away
3.6 Communicate Intensively and Involve Stakeholders
3.7 Data-Centric Corporate Culture
References
Part III: Implement
4: Development of a Responsive Operating Model
4.1 Fundamentals of the Operating Model
4.2 Line Organization
4.3 Matrix Organization
4.4 Line or Matrix Organization
4.5 Procedure Model: Introduction of Data Governance
4.6 Procedure Model: Preparation for Regular Operation of Data Governance
References
Part IV: Run
5: Fundamentals of the Digital Transformation
5.1 Stages of Digital Value Creation
5.2 Fundamentals of Business Architecture
References
6: Data Governance as Driver of Value Stream Optimization and as Pacemaker for the Digital Transformation
6.1 Fundamentals of Process Documentation
6.2 Classic Value Stream Mapping
6.3 Data-Driven Value Stream Optimization
6.4 Application of Data-Driven Value Stream Optimization
6.5 The Digital Production Line
References
Part V: Control
7: Measuring the Success of Data Governance
7.1 Data Governance Maturity Model
7.2 Self-Assessment of the Current Development Status of the Data Governance Implementation
Bibliography
8: List of Principles
9: Bonus: Data Definition Template
10: Closing Words
Glossary
