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E-raamat: Speed, Data, and Ecosystems: Excelling in a Software-Driven World [Taylor & Francis e-raamat]

(Chalmers University of Technology, Gothenburg, Sweden)
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Speed, Data, and Ecosystems: Excelling in a Software-Driven WorldSuurem pilt
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Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781315270685

As software R&D investment increases, the benefits from short feedback cycles using technologies such as continuous deployment, experimentation-based development, and multidisciplinary teams require a fundamentally different strategy and process. This book will cover the three overall challenges that companies are grappling with: speed, data and ecosystems. Speed deals with shortening the cycle time in R&D. Data deals with increasing the use of and benefit from the massive amounts of data that companies collect. Ecosystems address the transition of companies from being internally focused to being ecosystem oriented by analyzing what the company is uniquely good at and where it adds value.

SECTION I Introduction
Chapter 1 Introduction
3(14)
1.1 Speed
8(1)
1.2 Data
9(1)
1.3 Ecosystems
9(2)
1.4 The Bapo Model
11(2)
1.5 Where All This Comes From
13(1)
1.6 For Whom This Book Was Written
14(1)
1.7 Reading Guide
15(2)
Chapter 2 Trends In Society, Business And Technology
17(14)
2.1 Trends And Drivers
19(1)
2.2 Products To Services
19(3)
2.3 Towards Customer-Driven Innovation
22(1)
2.4 Changing Nature Of Innovation
23(2)
2.5 Software Size
25(1)
2.6 Need For Speed
26(1)
2.7 Towards "Big Data" And "Smart Data"
27(1)
2.8 Platforms And Ecosystems
28(1)
2.9 Conclusion
28(3)
Chapter 3 Illustrating Our Story: Viganbe
31(12)
3.1 Viganbe: An Introduction
32(1)
3.2 Strategy
33(2)
3.3 Architecture And Technology
35(2)
3.4 Organizing
37(2)
3.5 Conclusion
39(4)
SECTION II Speed
Chapter 4 The Stairway To Heaven: Speed
43(28)
4.1 Dimension 1: Speed
45(2)
4.2 Traditional Development
47(4)
4.2.1 Definition
47(1)
4.2.2 Drivers For Adoption
48(1)
4.2.3 Feedback Loop
49(1)
4.2.4 Implications
49(1)
4.2.5 Remaining Concerns
50(1)
4.2.6 Example
50(1)
4.3 Agile Practices
51(5)
4.3.1 Definition
51(1)
4.3.2 Drivers For Adoption
52(1)
4.3.3 Feedback Loop
53(1)
4.3.4 Implications
53(1)
4.3.5 Remaining Concerns
54(1)
4.3.6 Example
55(1)
4.4 Continuous Integration
56(4)
4.4.1 Definition
56(1)
4.4.2 Drivers For Adoption
56(1)
4.4.3 Feedback Loop
57(1)
4.4.4 Implications
58(1)
4.4.5 Remaining Concerns
59(1)
4.4.6 Example
59(1)
4.5 Continuous Deployment
60(4)
4.5.1 Definition
60(1)
4.5.2 Drivers For Adoption
61(1)
4.5.3 Feedback Loop
62(1)
4.5.4 Implications
62(1)
4.5.5 Remaining Concerns
63(1)
4.5.6 Example
64(1)
4.6 R&D As An Innovation System
64(5)
4.6.1 Definition
64(2)
4.6.2 Drivers For Adoption
66(1)
4.6.3 Feedback Loop
67(1)
4.6.4 Implications
67(1)
4.6.5 Remaining Concerns
68(1)
4.6.6 Example
69(1)
4.7 Conclusion
69(2)
Chapter 5 Throughput And Responsiveness
71(16)
5.1 Large-Scale Software Development
73(1)
5.2 Throughput And Responsiveness
74(1)
5.3 Customer-Unique Versus Customer-First
75(1)
5.4 Customer-Specific Teams
76(2)
5.5 Feature Generalization
78(1)
5.6 Professional Services
79(2)
5.7 Bringing It All Together
81(2)
5.8 Experiences And Implications
83(1)
5.9 Conclusion
84(3)
Chapter 6 Managing Architecture
87(20)
6.1 Architecture Technical Debt
88(5)
6.2 Architecture Refactoring
93(4)
6.3 The Role Of The Architect
97(2)
6.4 Art: An Organizational Model
99(3)
6.5 Experiences
102(2)
6.6 Conclusion
104(3)
Chapter 7 Continuous Integration
107(20)
7.1 Benefits Of Continuous Integration
108(1)
7.2 Testing Challenges
109(1)
7.3 Citim
110(11)
7.3.1 Civit
112(4)
7.3.2 Capturing Current State
116(1)
7.3.3 Envisioning Desired State
117(1)
7.3.4 Gap Analysis And Improvement Planning
118(1)
7.3.5 Post-Deployment Testing
119(2)
7.4 Process And Organization
121(1)
7.5 Experiences
122(1)
7.6 Conclusions
122(5)
SECTION III Data
Chapter 8 The Stairway To Heaven: Data
127(24)
8.1 Dimension 2: Data
128(5)
8.2 Ad Hoc Use Of Data
133(3)
8.2.1 Definition
133(1)
8.2.2 Drivers For Adoption
134(1)
8.2.3 Data-Driven Principles
134(1)
8.2.4 Implications
135(1)
8.2.5 Remaining Concerns
135(1)
8.2.6 Example
135(1)
8.3 Collection
136(3)
8.3.1 Definition
136(1)
8.3.2 Drivers For Adoption
137(1)
8.3.3 Data-Driven Principles
137(1)
8.3.4 Implications
138(1)
8.3.5 Remaining Concerns
138(1)
8.3.6 Example
139(1)
8.4 Automation
139(3)
8.4.1 Definition
139(1)
8.4.2 Drivers For Adoption
140(1)
8.4.3 Data-Driven Principles
141(1)
8.4.4 Implications
141(1)
8.4.5 Remaining Concerns
141(1)
8.4.6 Example
141(1)
8.5 Data Innovation
142(3)
8.5.1 Definition
142(1)
8.5.2 Drivers For Adoption
143(1)
8.5.3 Data-Driven Principles
143(1)
8.5.4 Implications
143(1)
8.5.5 Remaining Concerns
144(1)
8.5.6 Example
144(1)
8.6 Evidence-Based Company
145(3)
8.6.1 Definition
145(1)
8.6.2 Drivers For Adoption
146(1)
8.6.3 Data-Driven Principles
146(1)
8.6.4 Implications
147(1)
8.6.5 Remaining Concerns
147(1)
8.6.6 Example
147(1)
8.7 Conclusion
148(3)
Chapter 9 Feature Experimentation
151(22)
9.1 The Hypex Model
154(3)
9.2 Generate Feature Backlog
157(5)
9.2.1 Customers
158(1)
9.2.2 Business Strategy
159(1)
9.2.3 Bottom-Up Innovation
160(1)
9.2.4 Prioritizing The Feature Backlog
161(1)
9.3 Define Expected Behavior
162(3)
9.4 Implementation And Instrumentation
165(1)
9.5 Gap Analysis
166(2)
9.6 Develop And Test Hypotheses
168(1)
9.7 Iteration
169(1)
9.8 Example
170(1)
9.9 Conclusion
171(2)
Chapter 10 Evidence-Driven Development
173(24)
10.1 A Conceptual Framework
175(5)
10.2 The Qcd Method
180(1)
10.3 Requirements To Hypotheses
180(3)
10.4 Hypothesis Testing Techniques
183(3)
10.4.1 Concept Testing
185(1)
10.4.2 A/B Testing
186(1)
10.5 Scope Of Development
186(4)
10.5.1 New Product Development
187(1)
10.5.2 New Feature Development
188(1)
10.5.3 Feature Optimization
189(1)
10.6 Bringing It All Together: Qcd
190(1)
10.7 Example
191(2)
10.8 Conclusion
193(4)
SECTION IV Ecosystems
Chapter 11 The Stairway To Heaven: Ecosystems
197(30)
11.1 Software Ecosystems
199(4)
11.2 Towards Managing Complexity
203(3)
11.2.1 Complexity Problems During Evolution
205(1)
11.3 Three Layer Product Model
206(4)
11.3.1 Commoditized Functionality Layer
207(1)
11.3.2 Differentiating Functionality Layer
208(1)
11.3.3 Innovation And Experimentation Layer
208(1)
11.3.4 Productization And Commoditization Process
209(1)
11.3.5 Architecture Refactoring Process
210(1)
11.4 Validation
210(1)
11.5 Ecosystem Dimension
211(4)
11.6 Internally Focused
215(2)
11.6.1 Definition
215(1)
11.6.2 Drivers For Adoption
216(1)
11.6.3 Ecosystem Principles
216(1)
11.6.4 Implications
216(1)
11.6.5 Remaining Concerns
217(1)
11.7 Ad Hoc Ecosystem Engagement
217(2)
11.7.1 Definition
217(1)
11.7.2 Drivers For Adoption
218(1)
11.7.3 Ecosystem Principles
218(1)
11.7.4 Implications
218(1)
11.7.5 Remaining Concerns
219(1)
11.8 Tactical Ecosystem Engagement
219(2)
11.8.1 Definition
219(1)
11.8.2 Drivers For Adoption
219(1)
11.8.3 Ecosystem Principles
220(1)
11.8.4 Implications
220(1)
11.8.5 Remaining Concerns
220(1)
11.9 Strategic Single Ecosystem
221(1)
11.9.1 Definition
221(1)
11.9.2 Drivers For Adoption
221(1)
11.9.3 Ecosystem Principles
222(1)
11.9.4 Implications
222(1)
11.9.5 Remaining Concerns
222(1)
11.10 Strategic Multi-Ecosystems
222(3)
11.10.1 Definition
222(1)
11.10.2 Drivers For Adoption
223(1)
11.10.3 Ecosystem Principles
223(1)
11.10.4 Implications
223(1)
11.10.5 Remaining Concerns
224(1)
11.11 Conclusion
225(2)
Chapter 12 Three Layer Ecosystem Strategy Model
227(26)
12.1 Three Ecosystems
228(1)
12.2 Innovation Ecosystem
228(5)
12.2.1 Drivers
228(2)
12.2.2 Characteristics
230(3)
12.3 Differentiation Ecosystem
233(3)
12.3.1 Drivers
234(1)
12.3.2 Characteristics
235(1)
12.4 Commoditization Ecosystem
236(3)
12.4.1 Drivers
236(1)
12.4.2 Characteristics
237(2)
12.5 Challenges In Ecosystem Engagement
239(3)
12.5.1 Innovation Ecosystem
239(2)
12.5.2 Differentiation Ecosystem
241(1)
12.5.3 Commoditization Ecosystem
242(1)
12.6 Ecosystem Strategies
242(6)
12.6.1 Innovation Strategies
243(3)
12.6.2 Differentiation Strategies
246(1)
12.6.3 Commoditization Strategies
247(1)
12.7 Telesm
248(3)
12.7.1 Innovation Strategy Selection
249(1)
12.7.2 Transition To Differentiation Ecosystem
249(1)
12.7.3 Differentiation Strategy Selection
249(1)
12.7.4 Transition To Commodity Ecosystem
250(1)
12.7.5 Commodity Strategy Selection
250(1)
12.8 Conclusion
251(2)
Chapter 13 Implications Of Software Ecosystems
253(30)
13.1 Industry Structures
254(5)
13.1.1 Vertically Integrated Firms
255(1)
13.1.2 System Integrators And Specialized Suppliers
256(1)
13.1.3 Supply Chains
257(1)
13.1.4 Closed Ecosystem
257(1)
13.1.5 Open Ecosystem
257(1)
13.1.6 When Industries Transition
258(1)
13.2 Esao Model
259(9)
13.2.1 The Esao Model
260(2)
13.2.2 Triggers And Responses
262(4)
13.2.3 Esao Innovation Strategies
266(2)
13.3 Observed Challenges
268(5)
13.3.1 Software Architecture
268(4)
13.3.2 R&D Organization
272(1)
13.4 Recommendations
273(5)
13.4.1 Customers First; Developers Second
273(1)
13.4.2 Platform In Every Transaction
274(1)
13.4.3 Proactively Incorporate New Functionality
275(1)
13.4.4 Communicate Clear, Multi-Year Road Maps
276(1)
13.4.5 Next Computing Platform Abstraction Layer
277(1)
13.5 Conclusion
278(5)
SECTION V Conclusion
Chapter 14 Conclusion
283(20)
14.1 Speed
284(4)
14.2 Data
288(3)
14.3 Ecosystems
291(3)
14.4 Maximizing Synergy
294(1)
14.5 How To Use The Stairway To Heaven
295(2)
14.6 The Future
297(6)
Index 303
In the spring of 2011, after 8 years in industry, Jan Bosch returned to academia as a professor of software engineering at Chalmers University of Technology in Gothenburg, Sweden. Earlier, he worked as VP Engineering Process and VP Open Innovation for Intuit in Mountain View, California. Prior to joining Intuit, he worked for several years at Nokia Research Center. Before that, he was a full professor of Software Engineering at the University of Groningen. His main research interests are in software architecture assessment, design and representation, software product lines, including variability management, organizational approaches and product family architecture design, design erosion, component-oriented software engineering, object-oriented frameworks and design patterns.
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