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.
Monetizing Data: How to Uplift Your Business| Andrea Ahlemeyer-Stubbe (Author), Shirley Coleman (Author) | Wiley
Table of Contents
About the Authors
List of Figures
List of Tables
Preface
Chapter 1. The Opportunity
Introduction, The Rise of Data, Realising Data as an Opportunity, Our Definition of Monetising Data, Guidance on the Rest of the Book.
Chapter 2. About Data and Data Science
Introduction, Internal and External Sources of Data, Scales of Measurement and Types of Data, Data Dimensions, Quality of Data, Importance of Information, Experiments Yielding Data, A Data readiness Scale for Companies, Data Science, Data Improvement Cycle.
Chapter 3. Big Data Handling, Storage and Solutions
Introduction, Big Data, Smart Data, Big Data Solutions, Operational Systems supporting Business Processes, Analysis based Information Systems, Structured Data – Data Warehouses, Poly structured (Unstructured) Data – NoSQL Technologies, Data Structures and Latency, Data Marts.
Chapter 4. Data Mining as a Key Technique for Monetisation
Introduction, Population and Sample, Supervised and Unsupervised Methods, Knowledge discovery Techniques, Theory of Modelling, The Data Mining Process.
Chapter 5. Background and Supporting Statistical Techniques
Introduction, Variables, Key Performance Indicators, Taming the Data, Data Visualisation and Exploration of Data, Basic Statistics, Feature Selection and Reduction of Variables, Sampling, Statistical Methods for Proving Model Quality and Generalisability and Tuning Models.
Chapter 6. Data Analytics Methods for Monetisation
Introduction, Predictive Modelling Techniques, Pattern Detection Methods, Methods in practice.
Chapter 7. Monetisation of Data and Business Issues: Overview
Introduction, General Strategic Opportunities, Data as a Donation, Data as a Resource, Data Leading to New Business Opportunities, Information Brokering using Data, Connectivity as a Strategic Opportunity, Problem solving Methodology.
Chapter 8. How to Create Profit Out of Data
Introduction, Business, Data, Product Design, Value of Data, Charging Mechanisms, Connectivity as an Opportunity for Streamlining a Business.
Chapter 9. Some Practicalities of Monetising Data
Introduction, Practicalities, Special focus on SMEs, Special Focus on B2B Lead Generation, Legal and Ethical Issues, Payments, Innovation.
Chapter 10. Case Studies
Job Scheduling in Utilities, Shipping, Online Sales or Mail Order, Intelligent Profiling with Loyalty Card Schemes, Social Media: A Mechanism to Collect and Use Contributor Data, Making a Business out of Boring Statistics, Social Media and Web Intelligence Services, Service Provider, Data Source, Industry 4.0: Meta modelling using Simulated Data, Industry 4.0: Modelling Pricing Data in Manufacturing, Monetising Data in an SME, Making Sense of Public Finance and Other Data, Benchmarking Who is the Best in the Market, Change of Shopping Habits Part I, Change of Shopping Habits Part II, Change of Shopping Habits Part III, Service Providers, Households and Facility Management, Insurance, Healthcare and Risk Management, Mobility and Connected Cars, Production and Automation in Industry 4.0 .
Bibliography
Glossary
Index
LINK FOR THE BOOK
