Sharing Economy and Big Data Analytics

The different facets of the sharing economy offer numerous opportunities for businesses ? particularly those that can be distinguished by their creative ideas and their ability to easily connect buyers and senders of goods and services via digital platforms. At the beginning of the growth of this economy, the advanced digital technologies generated billions of bytes of data that constitute what we call Big Data.

This book underlines the facilitating role of Big Data analytics, explaining why and how data analysis algorithms can be integrated operationally, in order to extract value and to improve the practices of the sharing economy. It examines the reasons why these new techniques are necessary for businesses of this economy and proposes a series of useful applications that illustrate the use of data in the sharing ecosystem.

Soraya Sedkaoui is a Senior Lecturer at the University of Khemis Miliana, Algeria, as well as a data analyst and a strategic consultant. Her interests include Big Data, and the development of algorithms and models for business applications.

Mounia Khelfaoui is a Researcher and Maître de conférences at the University of Khemis Miliana. Her research mainly focuses on sustainable development and all the relevant themes, with a focus on the social responsibility of organizations.

Preface xi

Introduction xiii

Part 1. The Sharing Economy or the Emergence of a New Business Model 1

Chapter 1. The Sharing Economy: A Concept Under Construction 3

1.1. Introduction 3

1.2. From simple sharing to the sharing economy 5

1.2.1. The genesis of the sharing economy and the break with “consumer” society 5

1.2.2. The sharing economy: which economy? 8

1.3. The foundations of the sharing economy 10

1.3.1. Peer-to-peer (P2P): a revolution in computer networks 10

1.3.2. The gift: the abstract aspect of the sharing economy 13

1.3.3. The service economy and the offer of use 18

1.4. Conclusion 24

Chapter 2. An Opportunity for the Business World 25

2.1. Introduction 25

2.2. Prosumption: a new sharing economy trend for the consumer 27

2.3. Poverty: a target in the spotlight of the shared economy 29

2.4. Controversies on economic opportunities of the sharing economy 31

2.5. Conclusion 37

Chapter 3. Risks and Issues of the Sharing Economy 39

3.1. Introduction 39

3.2. Uberization: a white grain or just a summer breeze? 40

3.3. The sharing economy: a disruptive model 43

3.4. Major issues of the sharing economy 47

3.5. Conclusion 50

Chapter 4. Digital Platforms and the Sharing Mechanism 51

4.1. Introduction 51

4.2. Digital platforms: “What growth!” 52

4.3. Digital platforms or technology at the service of the economy 54

4.4. From the sharing economy to the sharing platform economy 57

4.5. Conclusion 59

Part 2. Big Data Analytics at the Service of the Sharing Economy 61

Chapter 5. Beyond the Word “Big”: The Changes 63

5.1. Introduction 63

5.2. The 3 Vs and much more: volume, variety, velocity 64

5.2.1. Volume 65

5.2.2. The variety 66

5.2.3. Velocity 67

5.2.4. What else? 68

5.3. The growth of computing and storage capacities 69

5.3.1. Big Data versus Big Computing 70

5.3.2. Big Data storage 71

5.3.3. Updating Moore’s Law 73

5.4. Business context change in the era of Big Data 74

5.4.1. The decision-making process and the dynamics of value creation 75

5.4.2. The emergence of new data-driven business models 77

5.5. Conclusion 78

Chapter 6. The Art of Analytics 81

6.1. Introduction 81

6.2. From simple analysis to Big Data analytics 82

6.2.1. Descriptive analysis: learning from past behavior to influence future outcomes 84

6.2.2. Predictive analysis: analyzing data to predict future outcomes 84

6.2.3. Prescriptive analysis: recommending one or more action plan(s) 85

6.2.4. From descriptive analysis to prescriptive analysis: an example 87

6.3. The process of Big Data analytics: from the data source to its analysis 88

6.3.1. Definition of objectives and requirements 90

6.3.2. Data collection 91

6.3.3. Data preparation 92

6.3.4. Exploration and interpretation 94

6.3.5. Modeling 95

6.3.6. Deployment 97

6.4. Conclusion 97

Chapter 7. Data and Platforms in the Sharing Context 99

7.1. Introduction 99

7.2. Pioneers in Big Data 101

7.2.1. Big Data on Walmart’s shelves 101

7.2.2. The Big Data behind Netflix’s success story 102

7.2.3. The Amazon version of Big Data 103

7.2.4. Big data and social networks: the case of Facebook 104

7.2.5. IBM and data analysis in the health sector 105

7.3. Data, essential for sharing 106

7.3.1. Data and platforms at the heart of the sharing economy 108

7.3.2. The data of sharing economy companies 110

7.3.3. Privacy and data security in a sharing economy 111

7.3.4. Open Data and platform data sharing 114

7.4. Conclusion 116

Chapter 8. Big Data Analytics Applied to the Sharing Economy 119

8.1. Introduction 119

8.2. Big Data and Machine Learning algorithms serving the sharing economy 121

8.2.1. Machine Learning algorithms 122

8.2.2. Algorithmic applications in the sharing economy context 124

8.3. Big Data technologies: the sharing economy companies’ toolbox 125

8.3.1. The appearance of a new concept and the creation of new technologies 127

8.4. Big Data on the agenda of sharing economy companies 130

8.4.1. Uber 131

8.4.2. Airbnb 132

8.4.3. BlaBlaCar 133

8.4.4. Lyft 134

8.4.5. Yelp 135

8.4.6. Other cases 137

8.5. Conclusion 139

Part 3. The Sharing Economy? Not Without Big Data Algorithms 141

Chapter 9. Linear Regression 143

9.1. Introduction 143

9.2. Linear regression: an advanced analysis algorithm 144

9.2.1. How are regression problems identified? 145

9.2.2. The linear regression model 146

9.2.3. Minimizing modeling error 148

9.3. Other regression methods 149

9.3.1. Logistic regression 150

9.3.2. Additional regression models: regularized regression 151

9.4. Building your first predictive model: a use case 152

9.4.1. What variables help set a rental price on Airbnb? 152

9.5. Conclusion 169

Chapter 10. Classification Algorithms 171

10.1. Introduction 171

10.2. A tour of classification algorithms 172

10.2.1. Decision trees 172

10.2.2. Naïve Bayes 175

10.2.3. Support Vector Machine (SVM) 177

10.2.4. Other classification algorithms 179

10.3. Modeling Airbnb prices with classification algorithms 183

10.3.1. The work that’s already been done: overview 184

10.3.2. Models based on trees: decision tree versus Random Forest 185

10.3.3. Price prediction with kNN 190

10.4. Conclusion 193

Chapter 11. Cluster Analysis 195

11.1. Introduction 195

11.2. Cluster analysis: general framework 196

11.2.1. Cluster analysis applications 197

11.2.2. The clustering algorithm and the similarity measure 198

11.3. Grouping similar objects using k-means 200

11.3.1. The k-means algorithm 201

11.3.2. Determine the number of clusters 203

11.4. Hierarchical classification 205

11.4.1. The hierarchical model approach 206

11.4.2. Dendrograms 207

11.5. Discovering hidden structures with clustering algorithms 208

11.5.1. Illustration of the classification of prices based on different characteristics using the k-means algorithm 209

11.5.2. Identify the number of clusters k 210

11.6. Conclusion 213

Conclusion 215

References 217

Index 233

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