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Monetizing Data: How to Uplift Your Business [Kõva köide]

(Technical Director, ISRU, School of Maths and Stats, Newcastle University, UK), (Director Strategical Analytics, HackerAgency GmbH, Munich, Germany)
  • Formaat: Hardback, 384 pages, kõrgus x laius x paksus: 231x160x25 mm, kaal: 590 g
  • Ilmumisaeg: 27-Apr-2018
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119125138
  • ISBN-13: 9781119125136
  • Formaat: Hardback, 384 pages, kõrgus x laius x paksus: 231x160x25 mm, kaal: 590 g
  • Ilmumisaeg: 27-Apr-2018
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119125138
  • ISBN-13: 9781119125136

Practical guide for deriving insight and commercial gain from data 

Monetising Data offers a practical guide for anyone working with commercial data but lacking deep knowledge of statistics or data mining. The authors — noted experts in the field — show how to generate extra benefit from data already collected and how to use it to solve business problems.  In accessible terms, the book details ways to extract data to enhance business practices and offers information on important topics such as data handling and management, statistical methods, graphics and business issues. The text presents a wide range of illustrative case studies and examples to demonstrate how to adapt the ideas towards monetisation, no matter the size or type of organisation.

The authors explain on a general level how data is cleaned and matched between data sets and how we learn from data analytics to address vital business issues. The book clearly shows how to analyse and organise data to identify people and follow and interact with them through the customer lifecycle. Monetising Data is an important resource:

  • Focuses on different business scenarios and opportunities to turn data into value
  • Gives an overview on how to store, manage and maintain data
  • Presents mechanisms for using knowledge from data analytics to improve the business and increase profits
  • Includes practical suggestions for identifying business issues from the data

Written for everyone engaged in improving the performance of a company, including managers and students, Monetising Data is an essential guide for understanding and using data to enrich business practice.

About the Authors xi
List of Figures
xiii
List of Tables
xvii
Preface xix
1 The Opportunity
1(8)
1.1 Introduction
1(1)
1.2 The Rise of Data
1(2)
1.3 Realising Data as an Opportunity
3(2)
1.4 Our Definition of Monetising Data
5(1)
1.5 Guidance on the Rest of the Book
6(3)
2 About Data and Data Science
9(20)
2.1 Introduction
9(1)
2.2 Internal and External Sources of Data
9(4)
2.3 Scales of Measurement and Types of Data
13(4)
2.4 Data Dimensions
17(1)
2.5 Quality of Data
17(3)
2.6 Importance of Information
20(1)
2.7 Experiments Yielding Data
21(2)
2.8 A Data-readiness Scale for Companies
23(4)
2.9 Data Science
27(1)
2.10 Data Improvement Cycle
27(2)
3 Big Data Handling, Storage and Solutions
29(20)
3.1 Introduction
29(1)
3.2 Big Data, Smart Data...
29(2)
3.3 Big Data Solutions
31(2)
3.4 Operational Systems supporting Business Processes
33(2)
3.5 Analysis-based Information Systems
35(3)
3.6 Structured Data -- Data Warehouses
38(5)
3.7 Poly-structured (Unstructured) Data -- NoSQL Technologies
43(3)
3.8 Data Structures and Latency
46(1)
3.9 Data Marts
47(2)
4 Data Mining as a Key Technique for Monetisation
49(22)
4.1 Introduction
49(1)
4.2 Population and Sample
49(1)
4.3 Supervised and Unsupervised Methods
50(2)
4.4 Knowledge-discovery Techniques
52(1)
4.5 Theory of Modelling
53(1)
4.6 The Data Mining Process
54(17)
5 Background and Supporting Statistical Techniques
71(50)
5.1 Introduction
71(1)
5.2 Variables
72(2)
5.3 Key Performance Indicators
74(1)
5.4 Taming the Data
74(3)
5.5 Data Visualisation and Exploration of Data
77(12)
5.6 Basic Statistics
89(11)
5.7 Feature Selection and Reduction of Variables
100(5)
5.8 Sampling
105(2)
5.9 Statistical Methods for Proving Model Quality and Generalisability and Tuning Models
107(14)
6 Data Analytics Methods for Monetisation
121(42)
6.1 Introduction
121(2)
6.2 Predictive Modelling Techniques
123(18)
6.3 Pattern Detection Methods
141(14)
6.4 Methods in practice
155(8)
7 Monetisation of Data and Business Issues: Overview
163(1)
7.1 Introduction
163(1)
7.2 General Strategic Opportunities
164(2)
7.3 Data as a Donation
166(6)
7.4 Data as a Resource
172(8)
7.5 Data Leading to New Business Opportunities
180(4)
7.6 Information Brokering using Data
184(1)
7.7 Connectivity as a Strategic Opportunity
185(1)
7.8 Problem-solving Methodology
186(1)
8 How to Create Profit Out of Data
187(16)
8.1 Introduction
187(4)
8.2 Business Models for Monetising Data
191(5)
8.3 Data Product Design
196(1)
8.4 Value of Data
197(2)
8.5 Charging Mechanisms
199(2)
8.6 Connectivity as an Opportunity for Streamlining a Business
201(2)
9 Some Practicalities of Monetising Data
203(30)
9.1 Introduction
203(1)
9.2 Practicalities
203(6)
9.3 Special focus on SMEs
209(5)
9.4 Special Focus on B2B Lead Generation
214(9)
9.5 Legal and Ethical Issues
223(8)
9.6 Payments
231(1)
9.7 Innovation
232(1)
10 Case Studies
233(98)
10.1 Job Scheduling in Utilities
236(6)
10.2 Shipping
242(4)
10.3 Online Sales or Mail Order
246(8)
10.4 Intelligent Profiling with Loyalty Card Schemes
254(8)
10.5 Social Media: A Mechanism to Collect and Use Contributor Data
262(5)
10.6 Making a Business out of Boring Statistics
267(4)
10.7 Social Media and Web Intelligence Services
271(4)
10.8 Service Provider
275(3)
10.9 Data Source
278(3)
10.10 Industry 4.0: Metamodelling using Simulated Data
281(7)
10.11 Industry 4.0: Modelling Pricing Data in Manufacturing
288(4)
10.12 Monetising Data in an SME
292(5)
10.13 Making Sense of Public Finance and Other Data
297(2)
10.14 Benchmarking Who is the Best in the Market
299(3)
10.15 Change of Shopping Habits Part I
302(6)
10.16 Change of Shopping Habits Part II
308(3)
10.17 Change of Shopping Habits Part III
311(4)
10.18 Service Providers, Households and Facility Management
315(4)
10.19 Insurance, Healthcare and Risk Management
319(3)
10.20 Mobility and Connected Cars
322(4)
10.21 Production and Automation in Industry 4.0
326(5)
Bibliography 331(10)
Glossary 341(16)
Index 357
Andrea Ahlemeyer-Stubbe is Director of Strategical Analytics at the servicepro Agentur für Dialogmarketing und Verkaufsförderung GmbH, Munich, Germany.

Shirley Coleman is Technical Director of ISRU at the School of Mathematics and Statistics, Newcastle University, UK.