Table of Contents

1. Cover
2. Title Page
3. About the Authors
4. List of Figures
5. List of Tables
6. Preface
7. 1 The Opportunity
   1. 1.1 Introduction
   2. 1.2 The Rise of Data
   3. 1.3 Realising Data as an Opportunity
   4. 1.4 Our Definition of Monetising Data
   5. 1.5 Guidance on the Rest of the Book
8. 2 About Data and Data Science
   1. 2.1 Introduction
   2. 2.2 Internal and External Sources of Data
   3. 2.3 Scales of Measurement and Types of Data
   4. 2.4 Data Dimensions
   5. 2.5 Quality of Data
   6. 2.6 Importance of Information
   7. 2.7 Experiments Yielding Data
   8. 2.8 A Data‐readiness Scale for Companies
   9. 2.9 Data Science
   10. 2.10 Data Improvement Cycle
9. 3 Big Data Handling, Storage and Solutions
   1. 3.1 Introduction
   2. 3.2 Big Data, Smart Data…
   3. 3.3 Big Data Solutions
   4. 3.4 Operational Systems supporting Business Processes
   5. 3.5 Analysis‐based Information Systems
   6. 3.6 Structured Data – Data Warehouses
   7. 3.7 Poly‐structured (Unstructured) Data – NoSQL Technologies
   8. 3.8 Data Structures and Latency
   9. 3.9 Data Marts
10. 4 Data Mining as a Key Technique for Monetisation
    1. 4.1 Introduction
    2. 4.2 Population and Sample
    3. 4.3 Supervised and Unsupervised Methods
    4. 4.4 Knowledge‐discovery Techniques
    5. 4.5 Theory of Modelling
    6. 4.6 The Data Mining Process
11. 5 Background and Supporting Statistical Techniques
    1. 5.1 Introduction
    2. 5.2 Variables
    3. 5.3 Key Performance Indicators
    4. 5.4 Taming the Data
    5. 5.5 Data Visualisation and Exploration of Data
    6. 5.6 Basic Statistics
    7. 5.7 Feature Selection and Reduction of Variables
    8. 5.8 Sampling
    9. 5.9 Statistical Methods for Proving Model Quality and Generalisability and Tuning Models
12. 6 Data Analytics Methods for Monetisation
    1. 6.1 Introduction
    2. 6.2 Predictive Modelling Techniques
    3. 6.3 Pattern Detection Methods
    4. 6.4 Methods in practice
13. 7 Monetisation of Data and Business Issues: Overview
    1. 7.1 Introduction
    2. 7.2 General Strategic Opportunities
    3. 7.3 Data as a Donation
    4. 7.4 Data as a Resource
    5. 7.5 Data Leading to New Business Opportunities
    6. 7.6 Information Brokering using Data
    7. 7.7 Connectivity as a Strategic Opportunity
    8. 7.8 Problem‐solving Methodology
14. 8 How to Create Profit Out of Data
    1. 8.1 Introduction
    2. 8.2 Business Models for Monetising Data
    3. 8.3 Data Product Design
    4. 8.4 Value of Data
    5. 8.5 Charging Mechanisms
    6. 8.6 Connectivity as an Opportunity for Streamlining a Business
15. 9 Some Practicalities of Monetising Data
    1. 9.1 Introduction
    2. 9.2 Practicalities
    3. 9.3 Special focus on SMEs
    4. 9.4 Special Focus on B2B Lead Generation
    5. 9.5 Legal and Ethical Issues
    6. 9.6 Payments
    7. 9.7 Innovation
16. 10 Case Studies
    1. 10.1 Job Scheduling in Utilities
    2. 10.2 Shipping
    3. 10.3 Online Sales or Mail Order
    4. 10.4 Intelligent Profiling with Loyalty Card Schemes
    5. 10.5 Social Media: a Mechanism to Collect and Use Contributor Data
    6. 10.6 Making a Business out of Boring Statistics
    7. 10.7 Social Media and Web Intelligence Services
    8. 10.8 Service Provider
    9. 10.9 Data Source
    10. 10.10 Industry 4.0: Metamodelling using Simulated Data
    11. 10.11 Industry 4.0: Modelling Pricing Data in Manufacturing
    12. 10.12 Monetising Data in an SME
    13. 10.13 Making Sense of Public Finance and Other Data
    14. 10.14 Benchmarking who is the Best in the Market
    15. 10.15 Change of Shopping Habits Part I
    16. 10.16 Change of Shopping Habits Part II
    17. 10.17 Change of Shopping Habits Part III
    18. 10.18 Service Providers, Households and Facility Management
    19. 10.19 Insurance, Healthcare and Risk Management
    20. 10.20 Mobility and Connected Cars
    21. 10.21 Production and Automation in Industry 4.0
17. Bibliography
18. Glossary
19. Index
20. End User License Agreement

List of Tables

1. Chapter 02
   1. Table 2.1 Typical internal and external data in information systems.
   2. Table 2.2 Extract of sales data.
   3. Table 2.3 Company sales data analytics.
   4. Table 2.4 Internal sales data enriched with external data.
   5. Table 2.5 Scales of measurement examples.
   6. Table 2.6 Checklist for data readiness.
2. Chapter 04
   1. Table 4.1 Confusion matrix for comparing models.
3. Chapter 05
   1. Table 5.1 Partially tamed data.
   2. Table 5.2 Outcomes of a hypothesis test.
   3. Table 5.3 Typical significance borders.
   4. Table 5.4 Examples of statistical tests.
   5. Table 5.5 Example of a contingency table.
   6. Table 5.6 Target proportions.
   7. Table 5.7 Confusion matrix.
   8. Table 5.8 Gains chart.
   9. Table 5.9 Non‐cumulative lift and gains table.
4. Chapter 06
   1. Table 6.1 Example of a contingency table.
   2. Table 6.2 Analysis table for goodness of fit.
5. Chapter 08
   1. Table 8.1 Business models for types of exchange.
   2. Table 8.2 Business models for B2C selling.
   3. Table 8.3 Business models for service providers.
6. Chapter 09
   1. Table 9.1 Business model canvas of the comparisons between data brokers and insight innovators.
7. Chapter 10
   1. Table 10.1 Summary of case studies.
   2. Table 10.2 Risk scores in a simple case.
   3. Table 10.3 Distribution of risk scores in different seasons.
   4. Table 10.4 Allowable stress for soft impact.
   5. Table 10.5 Parameters used to describe a four‐sided glass panel.
   6. Table 10.6 Data dimensions and stakeholders.

List of Illustrations

1. Chapter 01
   1. Figure 1.1 Where does big data come from?.
   2. Figure 1.2 Big data empowers business.
   3. Figure 1.3 Roadmap to success.
   4. Figure 1.4 Wish list for generating money out of data.
   5. Figure 1.5 Monetising data.
2. Chapter 02
   1. Figure 2.1 Deming’s ‘Plan, Do, Check, Act’ quality improvement cycle.
   2. Figure 2.2 Six Sigma quality improvement cycle.
   3. Figure 2.3 Example of data maturity model.
   4. Figure 2.4 Data improvement cycle.
3. Chapter 03
   1. Figure 3.1 Big data definition.
   2. Figure 3.2 Internet of things timeline.
   3. Figure 3.3 Example data structure.
   4. Figure 3.4 NoSQL management systems.
   5. Figure 3.5 Big data structure and latency.
4. Chapter 04
   1. Figure 4.1 Supervised learning.
   2. Figure 4.2 Unsupervised learning.
   3. Figure 4.3 The CRISP‐DM process.
   4. Figure 4.4 The SEMMA process.
   5. Figure 4.5 General representation of the data mining process.
   6. Figure 4.6 Time periods for data mining process.
   7. Figure 4.7 Stratified sampling.
   8. Figure 4.8 Lift chart for model comparison.
   9. Figure 4.9 Lift chart at small scale.
   10. Figure 4.10 An example of model control.
5. Chapter 05
   1. Figure 5.1 Raw data from a customer transaction.
   2. Figure 5.2 Bar chart of relative frequencies.
   3. Figure 5.3 Example of cumulative view.
   4. Figure 5.4 Example of a Pareto chart.
   5. Figure 5.5 Example of a pie chart.
   6. Figure 5.6 Scatterplot of company age and auditing behaviour with LOWESS line.
   7. Figure 5.7 Scatterplot of design options.
   8. Figure 5.8 Ternary diagram showing proportions.
   9. Figure 5.9 Radar plot of fitness panel data.
   10. Figure 5.10 Example of a word cloud.
   11. Figure 5.11 Example of a mind map.
   12. Figure 5.12 Location heat map.
   13. Figure 5.13 Density map for minivans.
   14. Figure 5.14 SPC chart of shipping journeys.
   15. Figure 5.15 Decision tree analysis for older workers.
   16. Figure 5.16 Gains chart.
   17. Figure 5.17 Lift chart.
   18. Figure 5.18 ROC curve development during predictive modelling.
6. Chapter 06
   1. Figure 6.1 Example of logistic regression.
   2. Figure 6.2 Corrected logistic regression.
   3. Figure 6.3 Decision tree.
   4. Figure 6.4 Artificial neural network.
   5. Figure 6.5 Bayesian network analysis of survey data.
   6. Figure 6.6 Bayesian network used to explore what‐if scenarios.
   7. Figure 6.7 Plot of non‐linear separation on a hyperplane.
   8. Figure 6.8 Dendrogram from hierarchical cluster analysis.
   9. Figure 6.9 Parallel plot from K‐means cluster analysis.
   10. Figure 6.10 Kohonen network with two‐dimensional arrangement of the output neurons.
   11. Figure 6.11 SOM output.
   12. Figure 6.12 T‐SNE output.
   13. 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.
   14. Figure 6.14 Association rules.
   15. Figure 6.15 Association analysis of products.
   16. Figure 6.16 Comparison of customer base and population.
   17. Figure 6.17 Relationship between energy usage and deprivation: scatterplot of mean AQ vs percentage of households deprived.
   18. Figure 6.18 Map showing prices.
7. Chapter 07
   1. Figure 7.1 Strategic opportunities.
   2. Figure 7.2 How data can boost top‐ and bottom‐line results.
   3. Figure 7.3 Typical data request.
   4. Figure 7.4 Observed data and usage.
   5. Figure 7.5 Maslow’s hierarchy of needs.
   6. Figure 7.6 Data sources to empower consumer business.
   7. Figure 7.7 Ready information on market opportunities.
   8. Figure 7.8 Word cloud from keyword occurrences.
   9. Figure 7.9 Using different data sources for analytics.
   10. Figure 7.10 Daily sleep patterns.
   11. Figure 7.11 Predictive analytics in insurance.
8. Chapter 08
   1. Figure 8.1 Pathways to monetising data.
   2. Figure 8.2 Segmentation features of walk‐in customers.
   3. Figure 8.3 Business opportunities.
9. Chapter 09
   1. Figure 9.1 Paths to monetisation.
   2. Figure 9.2 Pareto diagram of customer compliments.
   3. Figure 9.3 Graphical dashboard.
   4. Figure 9.4 Decrypting the DNA of the best existing customers.
   5. Figure 9.5 Aspects of digital maturity.
   6. Figure 9.6 Closed loop of B2B customer profiling – continuous learning.
   7. Figure 9.7 Automated B2B lead generation system.
   8. Figure 9.8 New methods, new insights, smart business.
   9. Figure 9.9 Misleading scatterplots.
   10. Figure 9.10 Scatterplot with multiple features.
   11. Figure 9.11 Histogram of suspicious‐quality recordings.
10. Chapter 10
    1. Figure 10.1 The evolution of data analytics
    2. Figure 10.2 Cumulative distribution of risk scores.
    3. Figure 10.3 Data sources in the shipping industry.
    4. Figure 10.4 Optimum speed recommendation.
    5. Figure 10.5 Pruned decision tree.
    6. Figure 10.6 Detail from decision tree
    7. Figure 10.7 Customised communication.
    8. Figure 10.8 Individualised communication.
    9. Figure 10.9 Complexity of data mining steps.
    10. Figure 10.10 Data in the customer journey.
    11. Figure 10.11 Intelligent profiles and segments in B2C.
    12. Figure 10.12 Personalised journey.
    13. Figure 10.13 The reach of social media.
    14. Figure 10.14 The power of social media.
    15. Figure 10.15 Using peer group behaviour.
    16. Figure 10.16 National statistics oil prices.
    17. Figure 10.17 Example of reports portal
    18. Figure 10.18 Making a business out of boring statistics.
    19. Figure 10.19 Right place, right time.
    20. Figure 10.20 Social media information summarised.
    21. Figure 10.21 Visualisation of user engagement.
    22. Figure 10.22 Concept of newsletter tracking.
    23. Figure 10.23 Example report on testing different versions.
    24. Figure 10.24 Customer profile details.
    25. Figure 10.25 Company profile details.
    26. Figure 10.26 Example of glass facades in buildings.
    27. Figure 10.27 Half normal plot of a screening experiment.
    28. Figure 10.28 Predicted vs calculated resistance factor with validation.
    29. Figure 10.29 Residual plot of prices.
    30. Figure 10.30 Visualisation of groups of products.
    31. Figure 10.31 Open data available to enrich company data.
    32. Figure 10.32 Diffusion map showing clusters of shares.
    33. Figure 10.33 Sampling approach for benchmarking in China.
    34. Figure 10.34 Three‐step approach to survey analytics.
    35. Figure 10.35 Skateboard offer.
    36. Figure 10.36 Customer journey.
    37. Figure 10.37 Example of customer segments.
    38. Figure 10.38 Virtual changing room.
    39. Figure 10.39 Virtual supermarket at bus stop.
    40. Figure 10.40 Input from miscellaneous IoT sensors.
    41. Figure 10.41 Appealing sleep sensor display.
    42. Figure 10.42 Sensors connected by mobile phone.
    43. Figure 10.43 The connected car.
    44. Figure 10.44 The new connected eco‐system.
    45. Figure 10.45 Industry 4.0.
    46. Figure 10.46 Industry 4.0 in action.

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