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Information Systems Management in the Big Data Era Softcover reprint of the original 1st ed. 2014 [Pehme köide]

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  • Formaat: Paperback / softback, 293 pages, kõrgus x laius: 235x155 mm, kaal: 4745 g, 73 Illustrations, black and white; XVII, 293 p. 73 illus., 1 Paperback / softback
  • Sari: Advanced Information and Knowledge Processing
  • Ilmumisaeg: 14-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319355074
  • ISBN-13: 9783319355078
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Information Systems Management in the Big Data Era Softcover reprint of the original 1st ed. 2014Suurem pilt
  • Formaat: Paperback / softback, 293 pages, kõrgus x laius: 235x155 mm, kaal: 4745 g, 73 Illustrations, black and white; XVII, 293 p. 73 illus., 1 Paperback / softback
  • Sari: Advanced Information and Knowledge Processing
  • Ilmumisaeg: 14-Oct-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319355074
  • ISBN-13: 9783319355078
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319355078.html
Märksõnad:
This timely text/reference explores the business and technical issues involved in the management of information systems in the era of big data and beyond. Topics and features: presents review questions and discussion topics in each chapter for classroom group work and individual research assignments; discusses the potential use of a variety of big data tools and techniques in a business environment, explaining how these can fit within an information systems strategy; reviews existing theories and practices in information systems, and explores their continued relevance in the era of big data; describes the key technologies involved in information systems in general and big data in particular, placing these technologies in an historic context; suggests areas for further research in this fast moving domain; equips readers with an understanding of the important aspects of a data scientist’s job; provides hands-on experience to further assist in the understanding of the technologies involved.

Arvustused

It is about business, about realizing the value of information systems, and about governing our systems and people in a big data environment. Though designed as a text for a one-semester course on information systems (IS), it can serve as a resource for newly minted managers in business and government large-scale information technology (IT) sites as well. The authors continually provide the reader with interesting and thoughtful corporate experiences relevant to the topic at hand. (Robert M. Lynch, Computing Reviews, June, 2015)

1 Introducing Big Data
1(18)
1.1 What the Reader Will Learn
1(1)
1.2 Big Data: So What Is All the Fuss About?
1(5)
1.2.1 Defining "Big Data"
2(1)
1.2.2 Big Data: Behind the Hype
3(3)
1.2.3 Google and a Case of the Flu
6(1)
1.3 Big Data: The Backlash Begins
6(5)
1.3.1 Big Data Catches a Cold
6(2)
1.3.2 It's My Data -- So What's in It for Me?
8(1)
1.3.3 Bucking the Backlash -- The Hype Cycle
9(2)
1.4 A Model for Big Data
11(5)
1.4.1 Strategy
12(1)
1.4.2 Structure
13(1)
1.4.3 Style
13(1)
1.4.4 Staff
13(1)
1.4.5 Statistical Thinking
14(1)
1.4.6 Synthesis
14(1)
1.4.7 Systems
15(1)
1.4.8 Sources
15(1)
1.4.9 Security
16(1)
1.5 Summary
16(1)
1.6 Review Questions
16(1)
1.7 Group Work/Research Activity
17(2)
1.7.1 Discussion Topic 1
17(1)
1.7.2 Discussion Topic 2
17(1)
References
17(2)
2 Strategy
19(34)
2.1 What the Reader Will Learn
19(1)
2.2 Introduction
19(1)
2.3 What is Strategy?
20(2)
2.4 Strategy and `Big Data'
22(4)
2.5 Strategic Analysis
26(17)
2.5.1 Analysing the Business Environment
26(6)
2.5.2 Strategic Capability -- The Value Chain
32(6)
2.5.3 The SWOT Analysis'
38(5)
2.6 Strategic Choice
43(8)
2.6.1 Introduction
43(1)
2.6.2 Type, Direction and Criteria of Strategic Development
44(3)
2.6.3 Aligning Business and IT/IS Strategy
47(4)
2.7 Summary
51(1)
2.8 Review Questions
51(1)
2.9 Group Work Research Activities
51(2)
2.9.1 Discussion Topic 1
51(1)
2.9.2 Discussion Topic 2
51(1)
References
52(1)
3 Structure
53(28)
3.1 What the Reader Will Learn
53(1)
3.2 Introduction
53(2)
3.3 What Is `Structure'?
55(1)
3.3.1 What Do We Mean by `Structure'?
55(1)
3.4 Formal Structures
56(13)
3.4.1 The "Organisational Chart": What Does It Tell Us?
56(5)
3.4.2 Structure, Systems and Processes
61(4)
3.4.3 Formal Structure: What Does This Mean for Big Data?
65(1)
3.4.4 Information Politics
66(3)
3.5 Organisational Culture: The Informal Structure
69(9)
3.5.1 What Do We Mean by `Culture'?
69(1)
3.5.2 Culture and Leadership
69(2)
3.5.3 The "Cultural Web"
71(7)
3.6 Summary
78(1)
3.7 Review Questions
78(1)
3.8 Group Work/Research Activity
78(3)
3.8.1 Discussion Topic 1
78(1)
3.8.2 Discussion Topic 2
79(1)
References
79(2)
4 Style
81(22)
4.1 What the Reader Will Learn
81(1)
4.2 Introduction
81(1)
4.3 Management in the Big Data Era
82(8)
4.3.1 Management or Leadership?
82(1)
4.3.2 What Is `Management'?
83(3)
4.3.3 Styles of Management
86(1)
4.3.4 Sources of Managerial Power
87(3)
4.4 The Challenges of Big Data (the Four Ds)
90(9)
4.4.1 Data Literacy
90(2)
4.4.2 Domain Knowledge
92(1)
4.4.3 Decision-Making
93(3)
4.4.4 Data Scientists
96(2)
4.4.5 The Leadership Imperative
98(1)
4.5 Summary
99(1)
4.6 Review Questions
100(1)
4.7 Group Work/Research Activity
100(3)
4.7.1 Discussion Topic 1
100(1)
4.7.2 Discussion Topic 2
100(1)
References
100(3)
5 Staff
103(22)
5.1 What the Reader Will Learn
103(1)
5.2 Introduction
103(1)
5.3 Data Scientists: The Myth of the `Super Quant'
104(7)
5.3.1 What's in a Name?
104(1)
5.3.2 Data "Science" and Data "Scientists"
105(5)
5.3.3 We've Been Here Before
110(1)
5.4 It Takes a Team
111(6)
5.4.1 What Do We Mean by "a Team"?
111(2)
5.4.2 Building High-Performance Teams
113(4)
5.5 Team Building as an Organisational Competency
117(4)
5.6 Summary
121(1)
5.7 Review Questions
121(1)
5.8 Group Work/Research Activity
122(3)
5.8.1 Discussion Topic 1
122(1)
5.8.2 Discussion Topic 2
122(1)
References
122(3)
6 Statistical Thinking
125(22)
6.1 What the Reader Will Learn
125(1)
6.2 Introduction: Statistics Without Mathematics
125(1)
6.3 Does "Big Data" Mean "Big Knowledge"?
126(3)
6.3.1 The DIKW Hierarchy
127(1)
6.3.2 The Agent-in-the-World
128(1)
6.4 Statistical Thinking -- Introducing System 1 and System 2
129(4)
6.4.1 Short Circuiting Rationality
132(1)
6.5 Causality, Correlation and Conclusions
133(2)
6.6 Randomness, Uncertainty and the Search for Meaning
135(3)
6.6.1 Sampling, Probability and the Law of Small Numbers
137(1)
6.7 Biases, Heuristics and Their Implications for Judgement
138(6)
6.7.1 Non-heuristic Biases
142(2)
6.8 Summary
144(1)
6.9 Review Questions
144(1)
6.10 Group Work Research Activities
145(2)
6.10.1 Discussion Topic 1 -- The Linda Problem
145(1)
6.10.2 Discussion Topic 2 -- The Birthday Paradox
145(1)
References
146(1)
7 Synthesis
147(22)
7.1 What the Reader Will Learn
147(1)
7.2 From Strategy to Successful Information Systems
147(4)
7.2.1 The Role of the Chief Information Officer (CIO)
148(1)
7.2.2 Management of IS Projects
149(2)
7.3 Creating Requirements That Lead to Successful Information Systems
151(3)
7.4 Stakeholder Buy-In
154(1)
7.5 How Do We Measure Success
155(1)
7.6 Managing Change
156(2)
7.7 Cost Benefits and Total Cost of Ownership
158(3)
7.7.1 Open Source
158(1)
7.7.2 Off the Shelf vs Bespoke
159(1)
7.7.3 Gauging Benefits
160(1)
7.8 Insourcing or Outsourcing?
161(2)
7.9 The Effect of Cloud
163(1)
7.10 Implementing `Big Data'
164(1)
7.11 Summary
165(1)
7.12 Review Questions
166(1)
7.13 Group Work Research Activities
166(3)
7.13.1 Discussion Topic 1
166(1)
7.13.2 Discussion Topic 2
166(1)
References
166(3)
8 Systems
169(24)
8.1 What the Reader Will Learn
169(1)
8.2 What Does Big Data Mean for Information Systems?
169(1)
8.3 Data Storage and Database Management Systems
170(7)
8.3.1 Database Management Systems
172(1)
8.3.2 Key-Value Databases
173(1)
8.3.3 Online Transactional Processing (OLTP)
173(2)
8.3.4 Decision Support Systems (DSS)
175(1)
8.3.5 Column-Based Databases
176(1)
8.3.6 In Memory Systems
176(1)
8.4 What a DBA Worries About
177(6)
8.4.1 Scalability
177(1)
8.4.2 Performance
178(1)
8.4.3 Availability
179(1)
8.4.4 Data Migration
179(3)
8.4.5 Not All Systems Are Data Intensive
182(1)
8.4.6 And There Is More to Data than Storage
183(1)
8.5 Open Source
183(1)
8.6 Application Packages
184(3)
8.6.1 Open Source vs Vendor Supplied?
186(1)
8.7 The Cloud and Big Data
187(2)
8.8 Hadoop and NoSQL
189(1)
8.9 Summary
190(1)
8.10 Review Questions
190(1)
8.11 Group Work Research Activities
191(2)
8.11.1 Discussion Topic 1
191(1)
8.11.2 Discussion Topic 2
191(1)
References
191(2)
9 Sources
193(22)
9.1 What the Reader Will Learn
193(1)
9.2 Data Sources for Data - Both Big and Small
193(1)
9.3 The Four Vs-Understanding What Makes Data Big Data
194(2)
9.4 Categories of Data
196(6)
9.4.1 Classification by Purpose
196(2)
9.4.2 Data Type Classification and Serialisation Alternatives
198(4)
9.5 Data Quality
202(4)
9.5.1 Extract, Transform and Load (ETL)
205(1)
9.6 Meta Data
206(3)
9.6.1 Internet of Things (IoT)
206(3)
9.7 Data Ownership
209(1)
9.8 Crowdsourcing
210(2)
9.9 Summary
212(1)
9.10 Review Questions
212(1)
9.11 Group Work Research Activities
212(3)
9.11.1 Discussion Topic 1
213(1)
9.11.2 Discussion Topic 2
213(1)
References
213(2)
10 IS Security
215(24)
10.1 What the Reader Will Learn
215(1)
10.2 What This
Chapter Could Contain but Doesn't
215(1)
10.3 Understanding the Risks
216(2)
10.3.1 What Is the Scale of the Problem?
216(2)
10.4 Privacy, Ethics and Governance
218(6)
10.4.1 The Ethical Dimension of Security
218(3)
10.4.2 Data Protection
221(3)
10.5 Securing Systems
224(6)
10.5.1 Hacking
224(1)
10.5.2 Denial of Service
225(1)
10.5.3 Denial of Service Defence Mechanisms
226(1)
10.5.4 Viruses and Worms and Trojan Horses (Often Collectively Referred to as Malware)
227(1)
10.5.5 Spyware
228(1)
10.5.6 Defences Against Malicious Attacks
228(2)
10.6 Securing Data
230(5)
10.6.1 Application Access Control
233(1)
10.6.2 Physical Security
234(1)
10.6.3 Malicious Insiders and Careless Employees
235(1)
10.7 Does Big Data Make for More Vulnerability?
235(1)
10.8 Summary
235(1)
10.9 Review Questions
236(1)
10.10 Group Work Research Activities
236(3)
10.10.1 Discussion Topic 1
236(1)
10.10.2 Discussion Topic 2
236(1)
References
237(2)
11 Technical Insights
239(28)
11.1 What the Reader Will Learn
239(1)
11.2 What You Will Need for This
Chapter
239(1)
11.3 Hands-on with Hadoop
240(22)
11.3.1 The Sandbox
240(8)
11.3.2 Hive
248(3)
11.3.3 Pig
251(5)
11.3.4 Sharing Your Data with the Outside World
256(2)
11.3.5 Visualization
258(3)
11.3.6 Life Is Usually More Complicated!
261(1)
11.4 Hadoop Is Not the Only NoSQL Game in Town!
262(1)
11.5 Summary
263(1)
11.6 Review Questions
263(1)
11.7 Extending the Tutorial Activities
263(1)
11.7.1 Extra Question 1
263(1)
11.7.2 Extra Question 2
264(1)
11.7.3 Extra Question 3
264(1)
11.8 Hints for the Extra Questions
264(1)
11.9 Answer for Extra Questions
264(3)
11.9.1 Question 1
264(1)
11.9.2 Question 2
265(1)
11.9.3 Question 3
265(1)
Reference
266(1)
12 The Future of IS in the Era of Big Data
267(22)
12.1 What the Reader Will Learn
267(1)
12.2 The Difficulty of Future Gazing with IT
267(1)
12.3 The Doubts
268(1)
12.4 The Future of Information Systems (IS)
269(6)
12.4.1 Making Decisions About Technology
271(4)
12.5 So What Will Happen in the Future?
275(11)
12.5.1 The Future for Big Data
275(4)
12.5.2 Ethics of Big Data
279(1)
12.5.3 Big Data and Business Intelligence
280(1)
12.5.4 The Future for Data Scientists
281(1)
12.5.5 The Future for IS Management
282(3)
12.5.6 Keeping Your Eye on the Game
285(1)
12.6 Summary
286(1)
12.7 Review Questions
286(1)
12.8 Group Work Research Activities
287(2)
12.8.1 Discussion Topic 1
287(1)
12.8.2 Discussion Topic 2
287(1)
References
287(2)
Index 289
Peter Lake is a Principal Lecturer at Sheffield Hallam University. Formerly the Technical Director of a software house, he has 30 years of industrial experience as well as 15 years as a lecturer specialising in database technology. His other Springer publications include the successful titles Concise Guide to Databases and Guide to Cloud Computing.

Robert Drake is also a Lecturer at Sheffield Hallam University, and has prior experience as Regional Director of Allocate Software, Kuala Lumpur, Malaysia.