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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Seitenzahl: 622
Veröffentlichungsjahr: 2018
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Table of Contents
Cover
Title Page
About the Authors
List of Figures
List of Tables
Preface
1 The Opportunity
1.1 Introduction
1.2 The Rise of Data
1.3 Realising Data as an Opportunity
1.4 Our Definition of Monetising Data
1.5 Guidance on the Rest of the Book
2 About Data and Data Science
2.1 Introduction
2.2 Internal and External Sources of Data
2.3 Scales of Measurement and Types of Data
2.4 Data Dimensions
2.5 Quality of Data
2.6 Importance of Information
2.7 Experiments Yielding Data
2.8 A Data‐readiness Scale for Companies
2.9 Data Science
2.10 Data Improvement Cycle
3 Big Data Handling, Storage and Solutions
3.1 Introduction
3.2 Big Data, Smart Data…
3.3 Big Data Solutions
3.4 Operational Systems supporting Business Processes
3.5 Analysis‐based Information Systems
3.6 Structured Data – Data Warehouses
3.7 Poly‐structured (Unstructured) Data – NoSQL Technologies
3.8 Data Structures and Latency
3.9 Data Marts
4 Data Mining as a Key Technique for Monetisation
4.1 Introduction
4.2 Population and Sample
4.3 Supervised and Unsupervised Methods
4.4 Knowledge‐discovery Techniques
4.5 Theory of Modelling
4.6 The Data Mining Process
5 Background and Supporting Statistical Techniques
5.1 Introduction
5.2 Variables
5.3 Key Performance Indicators
5.4 Taming the Data
5.5 Data Visualisation and Exploration of Data
5.6 Basic Statistics
5.7 Feature Selection and Reduction of Variables
5.8 Sampling
5.9 Statistical Methods for Proving Model Quality and Generalisability and Tuning Models
6 Data Analytics Methods for Monetisation
6.1 Introduction
6.2 Predictive Modelling Techniques
6.3 Pattern Detection Methods
6.4 Methods in practice
7 Monetisation of Data and Business Issues: Overview
7.1 Introduction
7.2 General Strategic Opportunities
7.3 Data as a Donation
7.4 Data as a Resource
7.5 Data Leading to New Business Opportunities
7.6 Information Brokering using Data
7.7 Connectivity as a Strategic Opportunity
7.8 Problem‐solving Methodology
8 How to Create Profit Out of Data
8.1 Introduction
8.2 Business Models for Monetising Data
8.3 Data Product Design
8.4 Value of Data
8.5 Charging Mechanisms
8.6 Connectivity as an Opportunity for Streamlining a Business
9 Some Practicalities of Monetising Data
9.1 Introduction
9.2 Practicalities
9.3 Special focus on SMEs
9.4 Special Focus on B2B Lead Generation
9.5 Legal and Ethical Issues
9.6 Payments
9.7 Innovation
10 Case Studies
10.1 Job Scheduling in Utilities
10.2 Shipping
10.3 Online Sales or Mail Order
10.4 Intelligent Profiling with Loyalty Card Schemes
10.5 Social Media: a Mechanism to Collect and Use Contributor Data
10.6 Making a Business out of Boring Statistics
10.7 Social Media and Web Intelligence Services
10.8 Service Provider
10.9 Data Source
10.10 Industry 4.0: Metamodelling using Simulated Data
10.11 Industry 4.0: Modelling Pricing Data in Manufacturing
10.12 Monetising Data in an SME
10.13 Making Sense of Public Finance and Other Data
10.14 Benchmarking who is the Best in the Market
10.15 Change of Shopping Habits Part I
10.16 Change of Shopping Habits Part II
10.17 Change of Shopping Habits Part III
10.18 Service Providers, Households and Facility Management
10.19 Insurance, Healthcare and Risk Management
10.20 Mobility and Connected Cars
10.21 Production and Automation in Industry 4.0
Bibliography
Glossary
Index
End User License Agreement
List of Tables
Chapter 02
Table 2.1 Typical internal and external data in information systems.
Table 2.2 Extract of sales data.
Table 2.3 Company sales data analytics.
Table 2.4 Internal sales data enriched with external data.
Table 2.5 Scales of measurement examples.
Table 2.6 Checklist for data readiness.
Chapter 04
Table 4.1 Confusion matrix for comparing models.
Chapter 05
Table 5.1 Partially tamed data.
Table 5.2 Outcomes of a hypothesis test.
Table 5.3 Typical significance borders.
Table 5.4 Examples of statistical tests.
Table 5.5 Example of a contingency table.
Table 5.6 Target proportions.
Table 5.7 Confusion matrix.
Table 5.8 Gains chart.
Table 5.9 Non‐cumulative lift and gains table.
Chapter 06
Table 6.1 Example of a contingency table.
Table 6.2 Analysis table for goodness of fit.
Chapter 08
Table 8.1 Business models for types of exchange.
Table 8.2 Business models for B2C selling.
Table 8.3 Business models for service providers.
Chapter 09
Table 9.1 Business model canvas of the comparisons between data brokers and insight innovators.
Chapter 10
Table 10.1 Summary of case studies.
Table 10.2 Risk scores in a simple case.
Table 10.3 Distribution of risk scores in different seasons.
Table 10.4 Allowable stress for soft impact.
Table 10.5 Parameters used to describe a four‐sided glass panel.
Table 10.6 Data dimensions and stakeholders.
List of Illustrations
Chapter 01
Figure 1.1 Where does big data come from?.
Figure 1.2 Big data empowers business.
Figure 1.3 Roadmap to success.
Figure 1.4 Wish list for generating money out of data.
Figure 1.5 Monetising data.
Chapter 02
Figure 2.1 Deming’s ‘Plan, Do, Check, Act’ quality improvement cycle.
Figure 2.2 Six Sigma quality improvement cycle.
Figure 2.3 Example of data maturity model.
Figure 2.4 Data improvement cycle.
Chapter 03
Figure 3.1 Big data definition.
Figure 3.2 Internet of things timeline.
Figure 3.3 Example data structure.
Figure 3.4 NoSQL management systems.
Figure 3.5 Big data structure and latency.
Chapter 04
Figure 4.1 Supervised learning.
Figure 4.2 Unsupervised learning.
Figure 4.3 The CRISP‐DM process.
Figure 4.4 The SEMMA process.
Figure 4.5 General representation of the data mining process.
Figure 4.6 Time periods for data mining process.
Figure 4.7 Stratified sampling.
Figure 4.8 Lift chart for model comparison.
Figure 4.9 Lift chart at small scale.
Figure 4.10 An example of model control.
Chapter 05
Figure 5.1 Raw data from a customer transaction.
Figure 5.2 Bar chart of relative frequencies.
Figure 5.3 Example of cumulative view.
Figure 5.4 Example of a Pareto chart.
Figure 5.5 Example of a pie chart.
Figure 5.6 Scatterplot of company age and auditing behaviour with LOWESS line.
Figure 5.7 Scatterplot of design options.
Figure 5.8 Ternary diagram showing proportions.
Figure 5.9 Radar plot of fitness panel data.
Figure 5.10 Example of a word cloud.
Figure 5.11 Example of a mind map.
Figure 5.12 Location heat map.
Figure 5.13 Density map for minivans.
Figure 5.14 SPC chart of shipping journeys.
Figure 5.15 Decision tree analysis for older workers.
Figure 5.16 Gains chart.
Figure 5.17 Lift chart.
Figure 5.18 ROC curve development during predictive modelling.
Chapter 06
Figure 6.1 Example of logistic regression.
Figure 6.2 Corrected logistic regression.
Figure 6.3 Decision tree.
Figure 6.4 Artificial neural network.
Figure 6.5 Bayesian network analysis of survey data.
Figure 6.6 Bayesian network used to explore what‐if scenarios.
Figure 6.7 Plot of non‐linear separation on a hyperplane.
Figure 6.8 Dendrogram from hierarchical cluster analysis.
Figure 6.9 Parallel plot from K‐means cluster analysis.
Figure 6.10 Kohonen network with two‐dimensional arrangement of the output neurons.
Figure 6.11 SOM output.
Figure 6.12 T‐SNE output.
Figure 6.13 Correspondence analysis output: scatterplot of RPC2 vs RPC1, the two principal dimensions showing how the row profiles in a contingency table differ from each other.
Figure 6.14 Association rules.
Figure 6.15 Association analysis of products.
Figure 6.16 Comparison of customer base and population.
Figure 6.17 Relationship between energy usage and deprivation: scatterplot of mean AQ vs percentage of households deprived.
Figure 6.18 Map showing prices.
Chapter 07
Figure 7.1 Strategic opportunities.
Figure 7.2 How data can boost top‐ and bottom‐line results.
Figure 7.3 Typical data request.
Figure 7.4 Observed data and usage.
Figure 7.5 Maslow’s hierarchy of needs.
Figure 7.6 Data sources to empower consumer business.
Figure 7.7 Ready information on market opportunities.
Figure 7.8 Word cloud from keyword occurrences.
Figure 7.9 Using different data sources for analytics.
Figure 7.10 Daily sleep patterns.
Figure 7.11 Predictive analytics in insurance.
Chapter 08
Figure 8.1 Pathways to monetising data.
Figure 8.2 Segmentation features of walk‐in customers.
Figure 8.3 Business opportunities.
Chapter 09
Figure 9.1 Paths to monetisation.
Figure 9.2 Pareto diagram of customer compliments.
Figure 9.3 Graphical dashboard.
Figure 9.4 Decrypting the DNA of the best existing customers.
Figure 9.5 Aspects of digital maturity.
Figure 9.6 Closed loop of B2B customer profiling – continuous learning.
Figure 9.7 Automated B2B lead generation system.
Figure 9.8 New methods, new insights, smart business.
Figure 9.9 Misleading scatterplots.
Figure 9.10 Scatterplot with multiple features.
Figure 9.11 Histogram of suspicious‐quality recordings.
Chapter 10
Figure 10.1 The evolution of data analytics
Figure 10.2 Cumulative distribution of risk scores.
Figure 10.3 Data sources in the shipping industry.
Figure 10.4 Optimum speed recommendation.
Figure 10.5 Pruned decision tree.
Figure 10.6 Detail from decision tree
Figure 10.7 Customised communication.
Figure 10.8 Individualised communication.
Figure 10.9 Complexity of data mining steps.
Figure 10.10 Data in the customer journey.
Figure 10.11 Intelligent profiles and segments in B2C.
Figure 10.12 Personalised journey.
Figure 10.13 The reach of social media.
Figure 10.14 The power of social media.
Figure 10.15 Using peer group behaviour.
Figure 10.16 National statistics oil prices.
Figure 10.17 Example of reports portal
Figure 10.18 Making a business out of boring statistics.
Figure 10.19 Right place, right time.
Figure 10.20 Social media information summarised.
Figure 10.21 Visualisation of user engagement.
Figure 10.22 Concept of newsletter tracking.
Figure 10.23 Example report on testing different versions.
Figure 10.24 Customer profile details.
Figure 10.25 Company profile details.
Figure 10.26 Example of glass facades in buildings.
Figure 10.27 Half normal plot of a screening experiment.
Figure 10.28 Predicted vs calculated resistance factor with validation.
Figure 10.29 Residual plot of prices.
Figure 10.30 Visualisation of groups of products.
Figure 10.31 Open data available to enrich company data.
Figure 10.32 Diffusion map showing clusters of shares.
Figure 10.33 Sampling approach for benchmarking in China.
Figure 10.34 Three‐step approach to survey analytics.
Figure 10.35 Skateboard offer.
Figure 10.36 Customer journey.
Figure 10.37 Example of customer segments.
Figure 10.38 Virtual changing room.
Figure 10.39 Virtual supermarket at bus stop.
Figure 10.40 Input from miscellaneous IoT sensors.
Figure 10.41 Appealing sleep sensor display.
Figure 10.42 Sensors connected by mobile phone.
Figure 10.43 The connected car.
Figure 10.44 The new connected eco‐system.
Figure 10.45 Industry 4.0.
Figure 10.46 Industry 4.0 in action.
Guide
Cover
Table of Contents
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Monetising Data: How to Uplift Your Business
Andrea Ahlemeyer-Stubbe
Director Strategical Analytics at the servicepro Agentur für Dialogmarketing und Verkaufsförderung GmbH, Munich, Germany
Shirley Coleman
Technical Director, ISRU, School of Mathematics and Statistics, Newcastle University, UK
This edition first published 2018© 2018 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Andrea Ahlemeyer‐Stubbe and Shirley Coleman to be identified as the authors of this work has been asserted in accordance with law.
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Library of Congress Cataloging‐in‐Publication data applied for
ISBN: 9781119125136
Cover Design: WileyCover Images: (Business people) © JohnnyGreig/Gettyimages; (Currencies) © Inok/iStockphoto
This book is dedicated toAgnes, Albert, Christopher, Dirk, Rosie and RowanWith thanks
About the Authors
Andrea Ahlemeyer‐Stubbe is Director of Strategical Analytics at servicepro‐Agentur für Dialogmarketing und Verkaufsförderung GmbH, Munich, Germany (servicepro).
Upon receiving her Master’s degree in statistics from the University of Dortmund, Andrea formed a consulting firm, offering customised professional services to her clients. She now leads servicepro’s analytics team, working on international projects for well‐known brands in Europe, United States and China, drawing on the wealth of experience gained from her 20 years in the industry, specifically in the areas of data mining, data warehousing, database marketing, CRM, big data and social CRM. She is a frequent lecturer at several universities, as well as an invited speaker at professional conferences. She writes for special interest magazines as well as marketing and management publications. She was President of ENBIS (European Network for Business and Industrial Statistics) from 2007–2009.
Dr Shirley Coleman is Principal Statistician and Technical Director at the Industrial Statistics Research Unit, School of Mathematics and Statistics, Newcastle University and a visiting scholar at the Faculty of Economics, Ljubljana University, Slovenia. She works on data analytics in small and medium enterprises and the energy sector and contributed a highly ranked impact case study to Newcastle University’s Research Excellence Framework. She publishes in trade and academic journals and is co‐editor of several books. She is an elected member of the International Statistics Institute and a Chartered Statistician of the Royal Statistical Society. She is a well‐known international invited speaker and conference chair. She is an ambassador for communication and dissemination of statistics to the wider community. She was President of ENBIS (European Network for Business and Industrial Statistics) from 2004–2005.
The authors have previously collaborated on A Practical Guide to Data Mining for Business and Industry (Wiley, May 2014).
List of Figures
Figure 1.1
Where does big data come from?
Figure 1.2
Big data empowers business
Figure 1.3
Roadmap to success
Figure 1.4
Wish list for generating money out of data
Figure 1.5
Monetising data
Figure 2.1
Deming’s ‘Plan, Do, Check, Act’ quality improvement cycle
Figure 2.2
Six Sigma quality improvement cycle
Figure 2.3
Example of data maturity model
Figure 2.4
Data improvement cycle
Figure 3.1
Big data definition
Figure 3.2
Internet of things timeline
Figure 3.3
Example data structure
Figure 3.4
NoSQL management systems
Figure 3.5
Big data structure and latency
Figure 4.1
Supervised learning
Figure 4.2
Unsupervised learning
Figure 4.3
The CRISP‐DM process
Figure 4.4
The SEMMA process
Figure 4.5
General representation of the data mining process
Figure 4.6
Time periods for data mining process
Figure 4.7
Stratified sampling
Figure 4.8
Lift chart for model comparison
Figure 4.9
Lift chart at small scale
Figure 4.10
An example of model control
Figure 5.1
Raw data from a customer transaction
Figure 5.2
Bar chart of relative frequencies
Figure 5.3
Example of cumulative view
Figure 5.4
Example of a Pareto chart
Figure 5.5
Example of a pie chart
Figure 5.6
Scatterplot of company age and auditing behaviour with LOWESS line
Figure 5.7
Scatterplot of design options
Figure 5.8
Ternary diagram showing proportions
Figure 5.9
Radar plot of fitness panel data
Figure 5.10
Example of a word cloud
Figure 5.11
Example of a mind map
Figure 5.12
Location heat map
Figure 5.13
Density map for minivans
Figure 5.14
SPC chart of shipping journeys
Figure 5.15
Decision tree analysis for older workers
Figure 5.16
Gains chart
Figure 5.17
Lift chart
Figure 5.18
ROC curve development during predictive modelling
Figure 6.1
Example of logistic regression
Figure 6.2
Corrected logistic regression
Figure 6.3
Decision tree
Figure 6.4
Artificial neural network
Figure 6.5
Bayesian network analysis of survey data
Figure 6.6
Bayesian network used to explore what‐if scenarios
Figure 6.7
Plot of non‐linear separation on a hyperplane
Figure 6.8
Dendrogram from hierarchical cluster analysis
Figure 6.9
Parallel plot from K‐means cluster analysis
Figure 6.10
Kohonen network with two‐dimensional arrangement of the output neurons
Figure 6.11
SOM output
Figure 6.12
T‐SNE output
Figure 6.13
Correspondence analysis output
Figure 6.14
Association rules
Figure 6.15
Association analysis of products
Figure 6.16
Comparison of customer base and population
Figure 6.17
Relationship between energy usage and deprivation
Figure 6.18
Map showing prices
Figure 7.1
Strategic opportunities
Figure 7.2
How data can boost top‐ and bottom‐line results
Figure 7.3
Typical data request
Figure 7.4
Observed data and usage
Figure 7.5
Maslow’s hierarchy of needs
Figure 7.6
Data sources to empower consumer business
Figure 7.7
Ready information on market opportunities
Figure 7.8
Word cloud from keyword occurrences
Figure 7.9
Using different data sources for analytics
Figure 7.10
Daily sleep patterns
Figure 7.11
Predictive analytics in insurance
Figure 8.1
Pathways to monetising data
Figure 8.2
Segmentation features of walk‐in customers
Figure 8.3
Business opportunities
Figure 9.1
Paths to monetisation
Figure 9.2
Pareto diagram of customer compliments
Figure 9.3
Graphical dashboard
Figure 9.4
Decrypting the DNA of the best existing customers
Figure 9.5
Aspects of digital maturity
Figure 9.6
Closed loop of B2B customer profiling – continuous learning
Figure 9.7
Automated B2B lead generation system
Figure 9.8
New methods, new insights, smart business
Figure 9.9
Misleading scatterplots
Figure 9.10
Scatterplot with multiple features
Figure 9.11
Histogram of suspicious‐quality recordings
Figure 10.1
The evolution of data analytics
Figure 10.2
Cumulative distribution of risk scores
Figure 10.3
Data sources in the shipping industry
Figure 10.4
Optimum speed recommendation
Figure 10.5
Pruned decision tree
Figure 10.6
Detail from decision tree
Figure 10.7
Customised communication
Figure 10.8
Individualised communication
Figure 10.9
Complexity of data mining steps
Figure 10.10
Data in the customer journey
Figure 10.11
Intelligent profiles and segments in B2C
Figure 10.12
Personalised journey
Figure 10.13
The reach of social media
Figure 10.14
The power of social media
Figure 10.15
Using peer group behaviour
Figure 10.16
National statistics oil prices
Figure 10.17
Example of reports portal
Figure 10.18
Making a business out of boring statistics
Figure 10.19
Right place, right time
Figure 10.20
Social media information summarised
Figure 10.21
Visualisation of user engagement
Figure 10.22
Concept of newsletter tracking
Figure 10.23
Example report on testing different versions
Figure 10.24
Customer profile details
Figure 10.25
Company profile details
Figure 10.26
Example of glass facades in buildings
Figure 10.27
Half normal plot of a screening experiment
Figure 10.28
Predicted vs calculated resistance factor with validation
Figure 10.29
Residual plot of prices
Figure 10.30
Visualisation of groups of products
Figure 10.31
Open data available to enrich company data
Figure 10.32
Diffusion map showing clusters of shares
Figure 10.33
Sampling approach for benchmarking in China
Figure 10.34
Three‐step approach to survey analytics
Figure 10.35
Skateboard offer
Figure 10.36
Customer journey
Figure 10.37
Example of customer segments
Figure 10.38
Virtual changing room
Figure 10.39
Virtual supermarket at bus stop
Figure 10.40
Input from miscellaneous IoT sensors
Figure 10.41
Appealing sleep sensor display
Figure 10.42
Sensors connected by mobile phone
Figure 10.43
The connected car
Figure 10.44
The new connected eco‐system
Figure 10.45
Industry 4.0
Figure 10.46
Industry 4.0 in action
List of Tables
Table 2.1
Typical internal and external data in information systems
Table 2.2
Extract of sales data
Table 2.3
Company sales data analytics
Table 2.4
Internal sales data enriched with external data
Table 2.5
Scales of measurement examples
Table 2.6
Checklist for data readiness
Table 4.1
Confusion matrix for comparing models
Table 5.1
Partially tamed data
Table 5.2
Outcomes of a hypothesis test
Table 5.3
Typical significance borders
Table 5.4
Examples of statistical tests
Table 5.5
Example of a contingency table
Table 5.6
Target proportions
Table 5.7
Confusion matrix
Table 5.8
Gains chart
Table 5.9
Non‐cumulative lift and gains table
Table 6.1
Example of a contingency table
Table 6.2
Analysis table for goodness of fit
Table 8.1
Business models for types of exchange
Table 8.2
Business models for B2C selling
Table 8.3
Business models for service providers
Table 9.1
Business model canvas of the comparisons between data brokers and insight innovators
Table 10.1
Summary of case studies
Table 10.2
Risk scores in a simple case
Table 10.3
Distribution of risk scores in different seasons
Table 10.4
Allowable stress for soft impact
Table 10.5
Parameters used to describe a four‐sided glass panel
Table 10.6
Data dimensions and stakeholders
Preface
When we finished writing our Practical Guide to Data Mining for Business and Industry, we realised that there were still things to say. The growth of interest in data has been enormous and there are now even more opportunities than during the earlier years when there was a steady awakening to the importance of data for business and industry.
Data analytics appears on billboards in mainstream locations such as airports, and even mathematics is being coupled with adverts for cars in a positive way. Everyone is aware that they have data and has seen the graphs and predictions that analysis produces.
The book describes how any business can be uplifted by monetising data. We show how data is generated by sensors, smart homes, apps, website visits, social network usage, digital communication, purchase behaviour, credit card usage, connected car devices and self‐quantification. Enriched by integrating with official statistics, analysis of these datasets brings real business advantage.
The book invites the reader to think about their data resources and be creative in how they use them. The book is not organised as a technical text but includes many examples of innovative applications of statistical thinking and analytical approaches. It does not propose original statistical or machine learning methods but focuses on applications of data‐driven approaches. It is general in scope and can thus serve as an introductory text. It has a management focus and the reader can judge for themselves where they can use the ideas. The structure of the book aims to be logical and cover the whole loop of using data for business decisions. The idea of exploring and giving advice on how to convert data into money is really appealing.
Even after several years of excitement about big data, there are few practical case studies available. For this reason, we include 21 in the final chapter to give realistic suggestions for what to do. The other chapters of the book give necessary background and motivational content.
It is timely to publish this book now, as big data and data analytics have captured the imagination of business and public alike. Data can be seen as the most powerful resource of the future; we believe it has more influence on the wealth of companies and people than any other resource. The authors have long been proponents of data analysis for business advantage and so it is with delight that we can collate our experience and rationale and share it with other people.
The ideas in this book have arisen from many hours of fascinating consulting work. We have felt honoured to be allowed to immerse ourselves in the company culture and explore their data, and been able to present solutions that in many cases have brought great financial benefits.
We are grateful to all the business people we have worked with. Writing takes considerable time and our families and friends have been very accommodating. We thank them all very much.
1The Opportunity
1.1 Introduction
Data awareness has swept across economic, political, occupational, social and personal life. Making sense of the fabulous opportunities afforded by such an abundance of data is the challenge of every business and each individual. The journey starts with understanding what data is, where it comes from, what insight it can give and how to extract it. These activities are sometimes referred to as descriptive analytics and predictive analytics. In descriptive analytics data is explored by looking at summary statistics and graphics, and the results are highly accessible and informative. Predictive analytics takes the analysis further and involves statistical approaches that utilise the full richness of the data and lead to predictive models to aid decision making.
This introductory chapter discusses the rise in data, changes in attitude to data and the advantages of getting to grips with accessing, analysing and utilising data. Definitions of concepts such as open data and big data are followed by guidance for reading the rest of the book.
1.2 The Rise of Data
There is much more data available and accessible than ever before.
Increasingly data is discussed in the popular press and, rather than shying away from figures, statistics and mathematics, advertisers are using these words more and more often. People are becoming more comfortable with data. This is clear from the increase in the use of self‐measurement and mapping facilities on personal devices such as mobile phones and tablets; people have a thirst for measuring everything in their daily life and like to try and control things to keep their life in good shape. Many people choose vehicles that are fitted with advanced digital measurement devices that manage engine performance and record fuel usage and location. All this is in addition to the increased automation of production lines and machinery, which have resulted in copious measurements being a familiar concept. A major contributor to the rise in importance of data is the impact of cheap data storage. For example, an external hard drive with terabytes of memory can be bought for the price of a visit to the hairdresser.
The common phrase to describe this changed world is ‘big data’ (Figure 1.1). A book on monetising data is inevitably about big data. We will interpret the term big data as data that is of a volume, variability and velocity that means common methods of appraisal are not appropriate. We need analytical methods to see the valuable patterns in it.
Figure 1.1 Where does big data come from?.
Since the early 2000s there has been a drive to make data more available, giving rise to the open data movement. This promotes sharing of data gathered with the benefit of public funding and includes most official statistics, academic research output and some market, product and service evaluation data. The opening up of data has led to a steep increase in requests for access to even more data; the result is a burgeoning interest in action learning and enthusiasm to understand the potential waiting to be uncovered from the data. The profession of data scientist has evolved and now encapsulates the skills and knowledge to handle and generate insights from this information.
Figure 1.2 shows how big data combined with analytics might empower different areas of any business. The aim of this book is to encourage people to use their big data to work out exciting business opportunities, make major changes and optimise the way things are run.
Figure 1.2 Big data empowers business.
1.3 Realising Data as an Opportunity
One of the key motivations for this book on monetising data is the sheer amount of under‐utilised data around. Hardly less important is the under‐achievement in terms of business benefit derived among those who do use their data. This suggests a two‐dimensional representation of the state of organisations, with one axis representing the usage of business data and the other axis representing the business benefit derived from it. Needless to say, the star performers are at the top right‐hand side of the resulting diagram in Figure 1.3. Being in the top and right‐hand corner is better than being at the top or at the right‐hand side of the axes because the two factors reinforce each other in a synergistic manner, giving greater benefits than either alone.
Figure 1.3 Roadmap to success.
The marketplace is highly heterogeneous, with companies and institutions (all referred to as ‘organisations’ henceforth) differentiated in many ways, including:
sector
size of turnover
size in numbers of employees
maturity
research focus
product or service development.
The baseline against which organisations can benchmark themselves in Figure 1.3 is different for different types of organisation.
Familiar players using big data include retail, finance, automotive manufacturers, health providers and process industries. In addition, the following are some of the less familiar organisations likely to be in possession of big data:
Sports societies: these may have larger turnover than expected and hold vast data banks of members’ details and their sporting activities.
Museums and galleries: these may have loyalty cards and multiple entry passes that yield customer details, frequency of visits, distance travelled, inclination and time spent at the venue.
Theatres and entertainment venues: these have names, addresses and frequency of attendance of attendees, and can study their catchment area and the popularity of different acts.
Libraries: these have names and addresses and members’ interests and usage.
Small retailers: these have records of itemised sales by day of week, time of day and season plus amount spent.
Craft and niche experts: who are first aware of trends and may have a global outlook.
All these organisations can take advantage of their data but they start from different points with different resources and capabilities; with good ideas they may have the opportunity to become winners in their own areas. Experience suggests that organisations have a secret wish list for generating money out of their data. Figure 1.4 shows the ranking we observed from our clients. However, this is just a snapshot and does not include business enrichment and transformation, which are also possible.
Figure 1.4 Wish list for generating money out of data.
Figure 1.5 shows a very generalised process for monetising data. Data comes into the process and is first used for business monitoring, leading to business insights; these might generate business optimisation and might lead to monetisation and potential business transformation.
Figure 1.5 Monetising data.
Despite differences in scale, the matrix in Figure 1.3 can help any organisation to map their current situation and plan their next steps to uplift their business.
1.4 Our Definition of Monetising Data
Data is the fundamental commodity, consisting of a representation of facts. However, when the data are summarised and illustrated they can lead to meaningful information, and assessing the meaningful information in context can lead to knowledge and wisdom.
Monetising data is more than just selling data and information. It includes everything where data is used in exchange for business advantage and supports business success. Large companies are often data rich and some have realised the advantage this gives them. Others consider themselves data rich but information poor because they have lots of data but it is not in a form that they can easily interpret or use to gain business insights. Statistical enthusiasm is a rare commodity but those businesses that pay attention to their data can find the answers to many of their policy and productivity questions. For example, scrutiny of data on sales easily yields information about seasonal trends: sales per customer might show shortfalls in maximising selling opportunities; total income might show overall success in attracting buyers, and so on.
Case studies and real data from our consulting practices are used throughout the book to illustrate the ideas, methods and techniques that are involved. As will be seen, most data can be monetised to bring benefit to the organisation. However, a lot of effort has to be expended to get the data into a suitable format for analysis. Data readiness can be assessed using tools that we will discuss. As analytics progresses, guidelines for data improvement become meaningful and we introduce the concept of the data improvement cycle to help organisations in continuous improvement and moving forward with their data analytics.
This book is aimed at managers in progressive organisations: managers who are keen to develop their own careers and who have the opportunity to suggest new ideas and innovative approaches for their organisation and influence how they are taken forward. The material requires background knowledge of dealing with numbers and spreadsheets and basic business principles. More specialised techniques, such as the use of decision tree analysis and predictive models, are fully explained. The main issue is the strength of desire to join the data revolution and hopefully after reading this book you will be an excited convert.
1.5 Guidance on the Rest of the Book
The rest of the book is planned as follows. Chapters 2and 3 address data collection and preparation issues, including the use of mapping and meteorological data as well as official statistics. Chapter 4 looks at general issues around data mining: as a concept and a mechanism for gathering insights from data. Chapters 5and 6 address technical methods; Chapter 5 looks at descriptive analytics, starting with statistical methods for summarising data and graphical presentations, and Chapter 6 moves on to statistical testing, modelling, segmentation, network analysis and predictive analytics.
Chapters 7and 8 introduce the different strategies, motivations, modes and concepts for monetising data and examine barriers and enablers for organisations seeking to realise the full potential of their data, their valuable asset. Monetisation can be viewed strategically and operationally. Strategically we can look at new business directions, step changes in thinking, disruptive innovation and new income streams. Operationally we can consider optimising current business models, and making better use of customer targeting and segmentation. In Chapter 7 we focus on strategic issues, whilst operational improvements of the existing business will be explored in Chapter 8. In Chapter 9 we will consider the practicalities of implementation, such as issues of ethics, privacy and security; loss of cultural and technical learning due to staff turnover and the other dampers that have to be overcome before we can achieve strategic steps forward and improvement of the current situation.
The mutual importance of theory and practice has long been recognised. As Chebyshev, a founding father of much statistical theory, said back in the 19th century, ‘Progress in any discipline is most successful when theory and practice develop hand in hand’. Not only does practice benefit from theory but theory benefits from practice. So in Chapter 10 we describe a set of case studies in which monetisation has brought big gains and uplifted the business. Thus we will aim to end the book on a high note and provide inspiration to move forward.
If you locate yourself within the grid in Figure 1.3 you can see which parts of the book are most relevant for you. Those readers at the bottom left are probably at the beginning of their exploration of monetisation and could well jump to the case studies in Chapter 10 for motivation and then return to Chapter 2. Those at the bottom right have already gained substantial business advantages but could benefit from learning new statistical and data‐mining techniques to make deeper use of their data, as described in the more technical Chapters 3–6. Those at the top left already have experience of analysing data but need to realise a better business advantage and could go straight to Chapters 7–9. Those at the top right can read the whole book for revision purposes and further insights!
Note that we avoid naming specific companies. Instead we refer to them in a generic way and the reader is welcome to find example companies by searching online.
2About Data and Data Science
2.1 Introduction
There is a pleasing increase in awareness of the importance of data. This extends across industry sectors and organisations of all sizes. Raising the profile of data means that there is more openness to exploring it and more determination to put it to good use. This chapter deals with aspects of data that are relevant to the practitioner wishing to apply data analytics to monetise data. We review the types of data that are available and how they are accessed. We consider the fast‐growing big data from internet exchanges and the attendant quality and storage issues, and consider which employees are best placed to maximise the value added from the data. We also consider the slower build‐up of transactional data from small traders and experiments on consumer behaviour. These can yield discrete collections of valuable figures ready to turn into information.
Internal company data arises as part of day‐to‐day business, and includes transactions, logistics, administration and financial data. This can be enriched by a variety of external data sources such as official statistics and open‐data sources. There is also a mass of useful data arising from social media. We define scales of measurement and terms commonly used to distinguish different types of data, the meaning and necessity of data quality, amounts of data and its storage, the skills needed for different data functions, and data readiness and how to assess where a company is on the cycle of data improvement.
2.2 Internal and External Sources of Data
Data to be used for enterprise information and knowledge creation can come from inside the company or from external sources. Integrating data from different sources is a powerful tactic in data mining for monetisation and gives the most scope for insights and innovation.
Naturally, the features of these different types of data vary and the costs associated with them range from very little to a lot. Internal data arise as part of the business and in principle they should be readily available for further analysis. In practice, the data are often difficult to access, belong to different systems and are owned by different stakeholders. A summary is given in Table 2.1.
Table 2.1 Typical internal and external data in information systems.
Data source
Example
Characteristics
Internal – owned by company
Date a product was manufactured or invoice data
In control of company, may be reliable; if not, the data collection process can be improved
External – owned by a third party
Social network data, credit rating or data about the area the customer lives in
May not be a perfect match in time scale or location
Data collected by someone but no clear ownership
Unattributed data and information, web scraping, aggregated information
Available but perhaps not easily usable, making it usable may cost money as it may involve a service provider
External – open source
National statistics institutions and Eurostat data
Available but usually aggregated with fixed granularity, timescale and coverage
The issue of ownership is important because we may wish to use data and tables that are published but we don’t know to whom they belong, how accurate they are or how carefully they were obtained. The data may be available and easy to collect but we don’t know if there are any intellectual property rights that we may be inadvertently violating.
Data collected by ‘web scraping’ is an interesting case; the data here might be people’s online comments, obtained, for example, by text mining websites. The comments may be anonymous or attributed to a nickname, so that ownership is not clear. If the comments are attributed to someone then they are owned by a third party, but otherwise thought is required before using them.
Internal, operational information systems move large amounts of internally produced data through various processes and subsystems, such as payment control, warehouse, planning/forecasting, web servers, adserver technology systems and newsletter systems. One drawback with internal data is that it is used primarily to handle the daily business and operational systems may lack a facility for keeping a comprehensive history. However, at least the quality and reliability of internal data is in the control of the company. This is not the case for external data unless it has been generated under very strict guidelines, such as those of a research institute or government statistical service.
External data is generated outside the company’s own processes; it is often needed as a set of reference values. For example, a service provider can compare the characteristics of their customer base with those of the target population. Characteristics such as employment, housing and age distribution are available from national statistics institutions (NSIs). Official statistics are necessarily aggregated to conserve confidentiality. The level of granulation has to be such that people cannot identify individuals by triangulating knowledge from several sources.
Eurostat collects data from all European NSIs and has a very comprehensive website at www.eu.eurostat.org. Considerable effort has been invested by government statistical services to make their websites user‐friendly, not least because they are under pressure to show that they provide a useful service and are worth the public expense that they represent. Aggregated data are available as tables and graphics that can be animated, and there is a vast amount of detail available. However, it can take some patience to navigate to the data required and it is a good idea to make advance preparations against the possibility of needing the data in a hurry. An example of the use of NSI data is included in the case study in Section 10.6.
As well as providing reference information, external data is often also valuable for providing additional information about a customer. Analytically focused information systems such as marketing databases and customer relationship management (CRM) systems frequently add external data. This may be in the form of specifically purchased information about the customer, such as their address, peer group or segment, or their credit rating.
As an example, consider a company that has data about books bought in a certain geographical area over a period of time. The data is in time order for each sale and so is long and thin; an extract is shown in Table 2.2. Each row represents a sale and additional information is in each column. Sometimes the rows are referred to as ‘cases’.
Table 2.2 Extract of sales data.
Sale ID
Date
Category
Quantity
Value
Customer ID
1
14/01/2016
2
2
45
12221
2
14/01/2016
3
1
55
12221
3
15/01/2016
3
3
44
14334
4
15/01/2016
2
2
33
21134
5
15/01/2016
2
2
66
22443
6
18/01/2016
3
1
75
11232
7
19/01/2016
2
2
33
22234
8
20/01/2016
3
3
78
23231
9
20/01/2016
3
4
56
24422
The data is valuable even without further additions, but descriptive analytics may yield a wide range of important information as shown in Table 2.3.
Table 2.3 Company sales data analytics.
Company data
Tables
Graphics
Statistics
Quantity and value of sales in different categories with time stamp and customer identification (ID)
Quantity and value of sales in each category
Time trends of sales values; bar charts of quantity and value in different categories
Mean quantity and value of sales per category and customer
This data can be enriched by adding company‐owned information about the customer, including their address, date of first purchase, date of last purchase, and the frequency and monetary value of their purchases. These last factors feature in segmentation methods based on RFM: the recency, frequency and monetary value of purchases. Descriptive analytics of the data can now be enhanced to include statistics such as sales per customer segment.
The data can be further augmented by adding freely available open data collected by an NSI or by providing knowledge about the customer based on their location, such as the type of housing in the area, the population age range, socio‐economic activity, and so on. Other more specific data may be obtained about their peer group or segment from commercial sources such as www.caci.co.uk.
Descriptive analytics of the data can now be enhanced to include statistics such as sales per socio‐economic group. This could have implications for the effectiveness of promotional activities, or allow assessment of the impact of opening an outlet in an area or of increasing salesperson presence in an area (Table 2.4). Predictive analytics can address issues such as which factors are most related to sales quantities and values.
Table 2.4 Internal sales data enriched with external data.
Company data
Enrichment data
Descriptive analytics
Predictive analytics
Customer RFM and location
Area details of location of customer
Sales per area, housing type
Clusters of similar locations
In the example, the company now has more information about book sales and can use this in their promotions.
Combining data from different areas and plotting them as they change over time is the background to the ground breaking Gapminder website, www.gapminder.org, developed by Hans Rosling. For example, scatterplots of income per person against life expectancy at birth for each country plotted over time from 1809 to 2009 show the amazing changes that have taken place in different countries. Animated graphics are a powerful way to show the relative changes. Work by Stotesbury and Dorling has explored the relationships between country wealth and their waste production, water consumption, education levels and so on.
In a well‐organised, data‐aware company, the quality of internal data may be better than that from external resources, not least because the company can control exactly how and when the internal data is generated. External data may not match the internal data exactly in time (being contemporaneous) or location, but nevertheless the availability (often free of charge) and the extent of this data means that even poorly matched external data can be useful.
2.3 Scales of Measurement and Types of Data
Knowing about the different scales of measurement and types of data is important as it helps to determine how the data should be analysed. Measurements such as value of sales are quite different from counts of how many customers entered a retail outlet, or of the proportion of times sales exceeded a certain limit. Descriptive data, such as a location being ‘Rural’, ‘Coastal’, ‘Urban’, or ‘Suburban’, need to be treated quite differently from measurement data. ‘Frequency of occurrence’ can be evaluated for descriptive data but it does not make sense to calculate an average value (say, for location) unless some ordering is applied, for example a gradation between agricultural and industrial locations, so that an average has some sort of meaning.
Business information comes in many forms. Reports and opinions are qualitative in nature whereas sales figures and numbers of customers are clearly quantitative. Qualitative data can usefully be quantified into non‐numerical and numerical data. For example, theme analysis applied to reports gives a non‐numerical summary of the themes in their content and the frequency of occurrence of the themes gives a meaningful numerical summary.
There are different types of quantitative data, and they may be described in a number of ways. Table 2.5 contrasts some of the more common terms.
Table 2.5 Scales of measurement examples.
Scales of measurement
Examples
Continuous vs categorical
Income (30,528 per year) vs size of family (medium = 3–5 family members)
Categorical: ordinal vs nominal
Opinion levels in market research (+2 = strongly agree, 1 = agree, 0 = no opinion, −1 = disagree, −2 = strongly disagree) vs industry sector (steel, craft, agriculture)
Numerical vs non‐numerical
Age (35.4 years old) vs colour (blue)
Data can be classified as continuous or categorical. Categories can be nominal or ordinal. The simplest level of measurement is nominal data, which indicates which named category is applicable. For example, a customer may live in an urban area, a rural area or a mixed area. In a dataset, this nominal data may be given in a column of values selected from urban/rural/mixed, with one row for each customer.
Once data has been identified as a useful analytical entity, it is often referred to as a ‘variable’. A data item such as income has a different value for each person and is called a variable because it varies across the sample of people being investigated. Note that being referred to as a ‘variable’ does not imply that the income of a particular person is uncertain, just that income varies across different people.
If a categorical variable has only two levels, for example ‘Male’ or ‘Female’, then the data is referred to as ‘binary’. Note that sex and gender refer to different concepts, with sex being biological and gender referring to the way the person sees themselves. Datasets can have several categories for gender. For example, one of the public datasets made available for data mining for the Knowledge, Discovery and Datamining Cup lists people who have lapsed from making donations to US veterans (see http://www.kdnuggets.com/meetings/kdd98/kdd‐cup‐98.html). The pivot table for gender has entries for ‘Male’, ‘Female’, ‘Missing’ and ‘Not known’ because the donation was from a joint account. In addition, some entries are blank and there is one case with the letter C, which does not have a defined meaning. There are six categories, some of which are only sparsely filled. If gender is used as a variable in analysis this sparseness may cause problems and the data should be pre‐processed before analysis. Note that there may also be additional accidental categories for ‘M’, ‘m’, ‘man’, and other erroneous entries.
If there is any order associated with the categories, then they are referred to as ‘ordinal’ data. Opinions can be captured as ordinal variables using questions, such as:
How was your experience today? Dreadful, poor, OK, good or very good
The responses usually need to be quantified if any meaningful analysis is to be carried out. In this example, it makes sense to code ‘Dreadful’ as −2, ‘Poor’ as −1, ‘OK’ as zero, ‘Good’ as +1 and ‘Very good’ as +2. The words can be replaced by pictures or emoticons as a more effective way of extracting opinion. Researchers have also investigated physical ways of gathering opinions; the engagement of a person can be evaluated by the length of time they keep eye contact and their certainty can be evaluated by the time they take to answer the question.
Variables that represent size are referred to as measures, measurements, scales or metrics. In data mining, the term ‘metric’ includes continuous measurements such as time spent, and counts such as the number of page views. Some statistical software packages, such as WEKA and SPSS, distinguish between scale and string variables, and will only allow certain actions with certain types of data. A string variable, such as ‘Male’ or ‘Female’ often needs to be recoded as a binary scale variable, taking values such as 1 or 2, as an additional alternative form, to ensure flexibility in the subsequent analysis. MINITAB distinguishes between quantitative variables and text variables and will not perform actions unless the appropriate data type is presented. Excel distinguishes between numbers and text. In R software, variables have to be specified as either numeric (numbers with decimal places), integers (whole numbers positive or negative), characters (string variables) or logical (true or false).
Many data items are measured on a continuous scale, for example the distance travelled to make a purchase. Continuous data does not need to be whole numbers like 4 km, but can be fractions of whole numbers, say 5.68 km. Continuous data may be of the interval type or the ratio type. Interval data has equal intervals between units but an arbitrary zero point. For example shoe or hat sizes. Ratio data is interval‐type data with the additional feature that zero is meaningful, for example a person’s salary. The fixed zero means that ratios are constant: €20,000 is twice as much as €10,000, and €6 is twice as much as €3.
Dates and times are interval data that have special treatment in statistical software because of their specific role in giving the timeline in any analysis. Usually a variety of formats are allowed. A numerical value can be extracted from the date as the number of days since a specified start date. The day of the week and the day of the month can be identified and both are useful depending on the analysis being carried out.
The different numbers of days in a month can sometimes cause problems (see Box).
Box Example of problems with days of the month.
Wet weather ‘behind drop in mortgages’
Metro newspaper, Tuesday 1 April 2014
The article states that:
The number of mortgages granted to home‐buyers fell to a four‐month low in February, Bank of England figures show. The drop to 70,309 from 76,753 in January was likely because of wet weather, analysts said. Ed Stansfield of Capital Economics said the temporary fall ‘should go some way towards calming fears the housing market recovery is rapidly spiralling out of control’.
76,753 mortgages in January equates to 2476 per day. At the same rate, February, with 28 days, should have 69,325 mortgages. The ‘drop’ is therefore actually an increase of 984.
Any comments?
The time variable can be represented by the number of minutes, hours, and so on since a start time. Time calculations can cause problems in practice, as some days start at 00:00, while others start at 06:00 or 07:00, say in Central European Time. These small discrepancies can have big implications in data analysis. For example, analysing the pattern of temperatures recorded across a geographical area quickly illustrated that some records were of mean temperature for the 24 hours from 00:00 and some were from 06:00.
Nominal and ordinal variables, referred to as categorical or classification variables, often represent dimensions, factors or custom variables that allow you to break down a metric by a particular value, for example screen views by screen name.
