Monetizing Data: How to Uplift Your Business

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.

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

About the Author :
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.