Monetizing Data
Description
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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Additional editions
Persons
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.
Content
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
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...
