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El. knyga: Data-driven Organization: Using Data for the Success of Your Company

  • Formatas: PDF+DRM
  • Serija: Business Guides on the Go
  • Išleidimo metai: 11-Dec-2022
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783031206047
Kitos knygos pagal šią temą:
Data-driven Organization: Using Data for the Success of Your CompanyDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Serija: Business Guides on the Go
  • Išleidimo metai: 11-Dec-2022
  • Leidėjas: Springer International Publishing AG
  • Kalba: eng
  • ISBN-13: 9783031206047
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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.
1 Background and Drivers of the Data-Driven Organization
1(16)
1.1 Business Intelligence Development
1(4)
1.2 Drivers of the Data-Driven Organization
5(12)
1.2.1 Change in the Technological Environment
5(2)
1.2.2 Changed Decision Situation
7(3)
1.2.3 Changing Competition and New Business Models
10(2)
1.2.4 Changing Customer Behavior
12(1)
1.2.5 Drivers Summary
13(2)
References
15(2)
2 Characteristics of the Data-Driven Organization
17(16)
2.1 Derivation of the Data-Driven Organization
17(3)
2.1.1 What Is Data?
17(2)
2.1.2 What Is a Data-Driven Business?
19(1)
2.2 What Do "Better" Choices Mean?
20(2)
2.3 Maturity Levels of Data-Driven Companies
22(2)
2.4 Properties of Data for the Data-Driven Organization
24(2)
2.5 Types of Analyses
26(1)
2.6 Advantages of a Data-Driven Company
27(6)
References
31(2)
3 Challenges and Barriers of the Data-Driven Organization
33(6)
3.1 Empirical Studies on Challenges and Barriers
33(2)
3.2 Summary of Findings and Evaluation
35(4)
References
37(2)
4 Process Model for Data Management
39(36)
4.1 The Five Steps
39(1)
4.2 Collect--Collect Data
40(11)
4.2.1 What Is Data?
41(2)
4.2.2 How Can We Differentiate Data?
43(1)
4.2.3 Which Data from Which Sources Can Be Used?
44(1)
4.2.4 More Data, More Knowledge?
45(1)
4.2.5 How Do Data Silos Arise and How Do We Deal with Them?
46(2)
4.2.6 What Criteria Are Relevant in the Choice of Technology?
48(1)
4.2.7 What General Conditions Do We Have to Consider?
49(1)
4.2.8 Guiding Questions for Collect
50(1)
4.3 Understand--Understanding the Collected Data
51(7)
4.3.1 Why Is Understanding Central?
52(1)
4.3.2 What Conditions Do We Need to Be Able to Understand?
53(1)
4.3.3 What Must a Technical Preparation Look Like?
54(1)
4.3.4 How Can We Tap into Data?
55(1)
4.3.5 What Does Emotionalizing Data Mean?
56(1)
4.3.6 How Can We Facilitate an Understanding?
56(2)
4.3.7 Guiding Questions for Understand
58(1)
4.4 Decide--Decide, on the Basis of the Collected Data
58(8)
4.4.1 What Distinguishes a Data-Driven Decision from a Gut Decision?
59(1)
4.4.2 What Types of Decisions Are Made in Companies?
60(1)
4.4.3 What Are the Requirements for Making a Good Decision?
61(1)
4.4.4 What Role Does the Time Factor Play in Decisions?
62(1)
4.4.5 How Can We Visualize Data?
63(2)
4.4.6 Data Versus Gut--Or Better in Combination?
65(1)
4.4.7 Guiding Questions for Decide
66(1)
4.5 Automate--Automation
66(6)
4.5.1 Why Can't We Get Around Automation?
66(1)
4.5.2 What Are the Technical Requirements for Automation?
67(1)
4.5.3 What Added Value Does AI Create in the Context of Automation?
68(1)
4.5.4 Is Automation Even More Than AI?
69(1)
4.5.5 How Do We Manage to Transfer Our Findings into Processes in an Automated Way?
70(1)
4.5.6 What Can Be the Causes of Resistance to the Data-Driven Organization?
71(1)
4.5.7 Guiding Questions for Automate
72(1)
4.6 Summary
72(3)
References
72(3)
5 Process Model for Implementing the Data-Driven Organization
75(42)
5.1 Overview
75(2)
5.2 Status Quo, Goals, and Data Strategy
77(14)
5.2.1 Internal and External Analysis
77(6)
5.2.2 Data Targets
83(3)
5.2.3 Data Strategy
86(5)
5.2.4 Stakeholder Integration
91(1)
5.3 Organization Model
91(3)
5.4 Process Model
94(1)
5.5 Example Projects
95(7)
5.5.1 Self-Services and Real-Time Services at the Schwarz Group
95(3)
5.5.2 Data & Analytics in B2B
98(1)
5.5.3 Configuration of the Collaboration Between Data & Analytics and the Business Departments at a Fashion Company
99(1)
5.5.4 Re-launching Data & Analytics at a Content Provider in the Sports Sector
100(2)
5.6 Tools
102(1)
5.7 Data Culture
103(4)
5.8 Talent Management and Talent Strategy
107(4)
5.9 Data Governance
111(6)
References
115(2)
6 Closing Words
117
Jonas Rashedi is a speaker and consultant for various seminar providers and a regular contributor to summits, blogs and podcasts. He has been working in the field of data management for over 10 years, specializing in organization building and data analytics, and has developed techniques to help large and mid-sized companies manage data more efficiently and profitably. Through his work as a consultant, he has guided companies through various set-up and expansion phases, gaining expert knowledge in a wide range of tools and methodologies.  
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