{"product_id":"monetizing-data-isbn-9781119125136","title":"Monetizing Data","description":"\u003cp\u003e\u003cb\u003ePractical guide for deriving insight and commercial gain from data\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003ci\u003eMonetising Data \u003c\/i\u003eoffers 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.\u003c\/p\u003e \u003cp\u003eThe 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. \u003ci\u003eMonetising Data \u003c\/i\u003eis an important resource:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eFocuses on different business scenarios and opportunities to turn data into value\u003c\/li\u003e \u003cli\u003eGives an overview on how to store, manage and maintain data\u003c\/li\u003e \u003cli\u003ePresents mechanisms for using knowledge from data analytics to improve the business and increase profits\u003c\/li\u003e \u003cli\u003eIncludes practical suggestions for identifying business issues from the data\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWritten for everyone engaged in improving the performance of a company, including managers and students, \u003ci\u003eMonetising\u003c\/i\u003e\u003ci\u003e Data\u003c\/i\u003e is an essential guide for understanding and using data to enrich business practice.\u003c\/p\u003e \u003cp\u003eAbout the Authors xi\u003c\/p\u003e \u003cp\u003eList of Figures xiii\u003c\/p\u003e \u003cp\u003eList of Tables xvii\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 The Opportunity 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 The Rise of Data 1\u003c\/p\u003e \u003cp\u003e1.3 Realising Data as an Opportunity 3\u003c\/p\u003e \u003cp\u003e1.4 Our Definition of Monetising Data 5\u003c\/p\u003e \u003cp\u003e1.5 Guidance on the Rest of the Book 6\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 About Data and Data Science 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 9\u003c\/p\u003e \u003cp\u003e2.2 Internal and External Sources of Data 9\u003c\/p\u003e \u003cp\u003e2.3 Scales of Measurement and Types of Data 13\u003c\/p\u003e \u003cp\u003e2.4 Data Dimensions 17\u003c\/p\u003e \u003cp\u003e2.5 Quality of Data 17\u003c\/p\u003e \u003cp\u003e2.6 Importance of Information 20\u003c\/p\u003e \u003cp\u003e2.7 Experiments Yielding Data 21\u003c\/p\u003e \u003cp\u003e2.8 A Data]readiness Scale for Companies 23\u003c\/p\u003e \u003cp\u003e2.9 Data Science 27\u003c\/p\u003e \u003cp\u003e2.10 Data Improvement Cycle 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Big Data Handling, Storage and Solutions 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 29\u003c\/p\u003e \u003cp\u003e3.2 Big Data, Smart Data… 29\u003c\/p\u003e \u003cp\u003e3.3 Big Data Solutions 31\u003c\/p\u003e \u003cp\u003e3.4 Operational Systems supporting Business Processes 33\u003c\/p\u003e \u003cp\u003e3.5 Analysis]based Information Systems 35\u003c\/p\u003e \u003cp\u003e3.6 Structured Data – Data Warehouses 38\u003c\/p\u003e \u003cp\u003e3.7 Poly]structured (Unstructured) Data – NoSQL Technologies 43\u003c\/p\u003e \u003cp\u003e3.8 Data Structures and Latency 46\u003c\/p\u003e \u003cp\u003e3.9 Data Marts 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Data Mining as a Key Technique for Monetisation 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 49\u003c\/p\u003e \u003cp\u003e4.2 Population and Sample 49\u003c\/p\u003e \u003cp\u003e4.3 Supervised and Unsupervised Methods 50\u003c\/p\u003e \u003cp\u003e4.4 Knowledge]discovery Techniques 52\u003c\/p\u003e \u003cp\u003e4.5 Theory of Modelling 53\u003c\/p\u003e \u003cp\u003e4.6 The Data Mining Process 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Background and Supporting Statistical Techniques 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 71\u003c\/p\u003e \u003cp\u003e5.2 Variables 72\u003c\/p\u003e \u003cp\u003e5.3 Key Performance Indicators 74\u003c\/p\u003e \u003cp\u003e5.4 Taming the Data 74\u003c\/p\u003e \u003cp\u003e5.5 Data Visualisation and Exploration of Data 77\u003c\/p\u003e \u003cp\u003e5.6 Basic Statistics 89\u003c\/p\u003e \u003cp\u003e5.7 Feature Selection and Reduction of Variables 100\u003c\/p\u003e \u003cp\u003e5.8 Sampling 105\u003c\/p\u003e \u003cp\u003e5.9 Statistical Methods for Proving Model Quality and Generalisability and Tuning Models 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Data Analytics Methods for Monetisation 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 121\u003c\/p\u003e \u003cp\u003e6.2 Predictive Modelling Techniques 123\u003c\/p\u003e \u003cp\u003e6.3 Pattern Detection Methods 141\u003c\/p\u003e \u003cp\u003e6.4 Methods in practice 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Monetisation of Data and Business Issues: Overview 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 163\u003c\/p\u003e \u003cp\u003e7.2 General Strategic Opportunities 164\u003c\/p\u003e \u003cp\u003e7.3 Data as a Donation 166\u003c\/p\u003e \u003cp\u003e7.4 Data as a Resource 172\u003c\/p\u003e \u003cp\u003e7.5 Data Leading to New Business Opportunities 180\u003c\/p\u003e \u003cp\u003e7.6 Information Brokering using Data 184\u003c\/p\u003e \u003cp\u003e7.7 Connectivity as a Strategic Opportunity 185\u003c\/p\u003e \u003cp\u003e7.8 Problem]solving Methodology 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 How to Create Profit Out of Data 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 187\u003c\/p\u003e \u003cp\u003e8.2 Business\u003c\/p\u003e \u003cp\u003e8.3 Data\u003c\/p\u003e \u003cp\u003eProduct Design 196\u003c\/p\u003e \u003cp\u003e8.4 Value of Data 197\u003c\/p\u003e \u003cp\u003e8.5 Charging Mechanisms 199\u003c\/p\u003e \u003cp\u003e8.6 Connectivity as an Opportunity for Streamlining a Business 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Some Practicalities of Monetising Data 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 203\u003c\/p\u003e \u003cp\u003e9.2 Practicalities 203\u003c\/p\u003e \u003cp\u003e9.3 Special focus on SMEs 209\u003c\/p\u003e \u003cp\u003e9.4 Special Focus on B2B Lead Generation 214\u003c\/p\u003e \u003cp\u003e9.5 Legal and Ethical Issues 223\u003c\/p\u003e \u003cp\u003e9.6 Payments 231\u003c\/p\u003e \u003cp\u003e9.7 Innovation 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Case Studies 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Job Scheduling in Utilities 236\u003c\/p\u003e \u003cp\u003e10.2 Shipping 242\u003c\/p\u003e \u003cp\u003e10.3 Online Sales or Mail Order 246\u003c\/p\u003e \u003cp\u003e10.4 Intelligent Profiling with Loyalty Card Schemes 254\u003c\/p\u003e \u003cp\u003e10.5 Social Media: A Mechanism to Collect and Use Contributor Data 262\u003c\/p\u003e \u003cp\u003e10.6 Making a Business out of Boring Statistics 267\u003c\/p\u003e \u003cp\u003e10.7 Social Media and Web Intelligence Services 271\u003c\/p\u003e \u003cp\u003e10.8 Service Provider 275\u003c\/p\u003e \u003cp\u003e10.9 Data Source 278\u003c\/p\u003e \u003cp\u003e10.10 Industry 4.0: Metamodelling using Simulated Data 281\u003c\/p\u003e \u003cp\u003e10.11 Industry 4.0: Modelling Pricing Data in Manufacturing 288\u003c\/p\u003e \u003cp\u003e10.12 Monetising Data in an SME 292\u003c\/p\u003e \u003cp\u003e10.13 Making Sense of Public Finance and Other Data 297\u003c\/p\u003e \u003cp\u003e10.14 Benchmarking Who is the Best in the Market 299\u003c\/p\u003e \u003cp\u003e10.15 Change of Shopping Habits Part I 302\u003c\/p\u003e \u003cp\u003e10.16 Change of Shopping Habits Part II 308\u003c\/p\u003e \u003cp\u003e10.17 Change of Shopping Habits Part III 311\u003c\/p\u003e \u003cp\u003e10.18 Service Providers, Households and Facility Management 315\u003c\/p\u003e \u003cp\u003e10.19 Insurance, Healthcare and Risk Management 319\u003c\/p\u003e \u003cp\u003e10.20 Mobility and Connected Cars 322\u003c\/p\u003e \u003cp\u003e10.21 Production and Automation in Industry 4.0 326\u003c\/p\u003e \u003cp\u003eBibliography 331\u003c\/p\u003e \u003cp\u003eGlossary 341\u003c\/p\u003e \u003cp\u003eIndex 357\u003c\/p\u003e   \u003cp\u003e \u003cstrong\u003eAndrea Ahlemeyer-Stubbe \u003c\/strong\u003eis Director of Strategical Analytics at the servicepro Agentur für Dialogmarketing und Verkaufsförderung GmbH, Munich, Germany.  \t\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eShirley Coleman\u003c\/strong\u003e is Technical Director of ISRU at the School of Mathematics and Statistics, Newcastle University, UK.     \u003c\/p\u003e\u003cp\u003e \u003cstrong\u003ePractical guide for deriving insight and commercial gain from data \u003c\/strong\u003e  \u003c\/p\u003e\u003cp\u003e \u003cem\u003eMonetising Data: How to Uplift Your Business\u003c\/em\u003e 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.   \u003c\/p\u003e\u003cp\u003e 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. \u003cem\u003eMonetising Data\u003c\/em\u003e is an important resource that:   \u003c\/p\u003e\u003cul\u003e \u003cli\u003eFocuses on different business scenarios and opportunities to turn data into value\u003c\/li\u003e \u003cli\u003eGives an overview on how to store, manage, and maintain data\u003c\/li\u003e \u003cli\u003ePresents mechanisms for using knowledge from data analytics to improve business and increase profits\u003c\/li\u003e \u003cli\u003eIncludes practical suggestions for identifying business issues from the data\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e  \u003cp\u003e Written for everyone engaged in improving the performance of a company, including managers and students, \u003cem\u003eMonetising Data\u003c\/em\u003e is an essential guide for understanding and using data to enrich business practice.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989651013861,"sku":"NP9781119125136","price":89.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119125136.jpg?v=1761784963","url":"https:\/\/k12savings.com\/products\/monetizing-data-isbn-9781119125136","provider":"K12savings","version":"1.0","type":"link"}