Big Data’s Big Potential in Developing Economies: Impact on Agriculture, Health and Environmental Security

238 Pages·2016·1.76 MB·English

by  Nir Kshetri

B D ’ B P D
IG ATA S IG OTENTIAL IN EVELOPING
E
CONOMIES
Impact on Agriculture, Health and
Environmental Security
This page intentionally left blank
B D ’ B P
IG ATA S IG OTENTIAL
D E
IN EVELOPING CONOMIES
Impact on Agriculture, Health and
Environmental Security
Nir Kshetri
The University of North Carolina at Greensboro, Greensboro,
USA
CABI is a trading name of CAB International
CABI CABI
Nosworthy Way 745 Atlantic Avenue
Wallingford 8th Floor
Oxfordshire OX10 8DE Boston, MA 02111
UK USA
Tel: [phone] T: [phone]
Fax: [phone] E-mail: [email protected]
E-mail: [email protected]
Website: www.cabi.org
© N. Kshetri 2016. All rights reserved. No part of this publication may be reproduced in
any form or by any means, electronically, mechanically, by photocopying, recording or
otherwise, without the prior permission of the copyright owners.
A catalogue record for this book is available from the British Library, London, UK.
Library of Congress Cataloging-in-Publication Data
Names: Kshetri, Nir, author.
Title: Big data's big potential in developing economies : impact on agriculture, health and
environmental security / Nir Kshetri.
Description: Boston, MA : CABI, [2016] | Includes bibliographical references and index.
Identifiers: LCCN 2016022766| ISBN 9781780648682 (hbk : alk. paper) |
ISBN 9781780648705 (epub)
Subjects: LCSH: Big data--Developing countries. | Agriculture and state--Developing
countries. | Medical policy--Developing countries. | Environmental policy--Developing
countries.
Classification: LCC QA76.9.B45 K74 2016 | DDC 005.7094--dc23 LC record available at
https://lccn.loc.gov/2016022766
ISBN-13: 978 1 78064 868 2
Commissioning editor: David Hemming
Editorial assistant: Emma McCann
Production editor: Tim Kapp
Typeset by AMA DataSet Ltd, Preston, UK.
Printed and bound in the UK by CPI Group (UK) Ltd, Croydon, CR0 4YY
Contents
Abbreviations xi
About the author xiv
Preface and Acknowledgements xv
1 Big Data in Developing Countries: Current Status, Opportunities
and Challenges 1
1.1 Introduction 1
1.2 Definitions and Explanations of Key Terms 4
1.2.1 Algorithm 4
1.2.2 Big Data 4
1.2.3 Business model 4
1.2.4 Cloud computing 5
1.2.5 Developing economies 5
1.2.6 Drip irrigation 5
1.2.7 Environmental monitoring 6
1.2.8 Institutionalization 6
1.2.9 Least developed countries (LDCs) 6
1.2.10 The Internet of Things 6
1.2.11 Machine-to-machine connections 7
1.2.12 Precision agriculture 7
1.2.13 Radio-frequency identification 7
1.2.14 Sensor 7
1.3 Characteristics of Big Data 8
1.3.1 Volume 8
1.3.2 Velocity 10
1.3.3 Variety 11
1.3.4 Variability 12
1.3.5 Complexity 12
v
vi Contents
1.4 Key Areas of Big Data Deployment in Developing Countries 13
1.4.1 E-commerce 13
1.4.2 Oil and gas 14
1.4.3 Banking, finance and insurance 14
1.4.4 Improving disaster mitigation and preparedness 14
1.4.5 Enhancing transparency and reducing corruption 15
1.5 The Relationship between Big Data, Mobility, the Internet of Things
and Cloud Computing in the Context of Developing Countries 17
1.6 Determinants of the Development of the Big Data Industry and
Market 17
1.6.1 Social and political dimensions 18
1.6.2 Economic dimension 19
1.7 Some Forces to Overcome the Adverse Economic, Political and
Cultural Circumstances 20
1.7.1 Multinationals launching Big Data applications in
developing countries 20
1.7.2 The roles of international development agencies 21
1.8 Agriculture, Health and Environment: Intricate Relationship 22
1.9 Discussion and Concluding Comments 22
2 Big Data Ecosystem in Developing Countries 30
2.1 Introduction 30
2.2 Context Dependence in Big Data Models 32
2.3 Barriers, Challenges and Obstacles in Using Big Data 32
2.3.1 Low degree of digitization 33
2.3.2 Costs associated with participating in the digital economy 35
2.3.3 Data usability 37
2.3.4 Poor data quality 37
2.3.5 Low degree of value chain integration and disconnection
between data users and producers 38
2.3.6 Interoperability and standardization issues 39
2.3.7 Big Data skills deficit 40
2.3.8 Values and cultures 41
2.4 Some Encouraging and Favourable Signs 42
2.5 Big Data-Related Entrepreneurship and Some Notable Big Data
Companies Operating in the Developing World 43
2.5.1 Alibaba 43
2.5.2 Mediatrac 44
2.5.3 Nedbank 45
2.6 The Internet of Things as a Key Component of Big Data 45
2.6.1 Health care 46
2.6.2 Environmental security and resource conservation 46
2.6.3 Agriculture 47
2.7 Creating a Virtuous Circle of Effective Big Data Deployment 47
2.7.1 Existing actors in the Big Data ecosystem 48
2.7.2 Entry of new actors in the Big Data ecosystem 51
2.8 Discussion and Concluding Comments 52
Contents vii
3 Big Data in Environmental Protection and Resources
Conservation 62
3.1 Introduction 62
3.2 Various Data Sources in the Context of Environmental Monitoring
and Protection 65
3.2.1 The Internet of Things 65
3.2.2 Social networking websites 66
3.2.3 Remote sensing technologies 67
3.3 Characteristics of Big Data in the Context of Environmental
Monitoring and Protection 67
3.3.1 Volume 68
3.3.2 Velocity 68
3.3.3 Variety 68
3.3.4 Variability 69
3.3.5 Complexity 69
3.4 Foreign and Local Big Data Technologies in Environmental
Monitoring and Protection 70
3.4.1 Role of foreign multinational corporations 70
3.4.2 Big Data applications created in developing countries 71
3.5 The Roles of Philanthropic and International Development
Organizations 71
3.6 Big Data and Transparency: Fighting Environmental Crimes and
Injustices 73
3.6.1 The 2015 Indonesian fires 73
3.6.2 Deforestation of rainforests in the Peruvian Amazon 74
3.7 Discussion and Concluding Comments 75
4 Big Data in Health-Care Delivery and Outcomes 83
4.1 Introduction 83
4.2 Big Data Deployment in Delivering Health-Care Services in
Developing Countries: Some Examples 85
4.3 Foreign as well as Locally Developed Big Data-Based Health-Care
Solutions 87
4.3.1 Solutions developed in industrialized countries 87
4.3.2 Locally developed solutions 87
4.4 The Role of Big Data in Expanding Access to Health-Care Services 87
4.4.1 Geographic accessibility 88
4.4.2 Availability 88
4.4.3 Financial accessibility 91
4.4.4 Acceptability 92
4.5 Big Data-Based Solutions to Fight Fake Drugs 92
4.5.1 The prevalence of fake drugs and some Big Data-based
solutions to fight the problem 92
4.5.2 Expansion to new market segments 94
4.5.3 Some challenges faced 94
4.6 The Role of Big Data in Promoting Transparency and
Accountability in the Health-Care Sector 95
viii Contents
4.7 The Internet of Things and Health Care 96
4.8 Discussion and Concluding Comments 97
5 Big Data in Agriculture 101
5.1 Introduction 101
5.2 Various Data Sources and Technological Trends 103
5.2.1 The Internet of Things and agriculture 103
5.2.2 Drip irrigation systems 104
5.2.3 Soil infrared spectroscopy 104
5.2.4 Data and information created via agriculture and farming
platforms 105
5.3 The Origin of Big Data-Related Innovations in the Agricultural
Sector 107
5.3.1 Big Data technologies developed in industrialized
countries 107
5.3.2 Undertaking Big Data-related innovations locally 108
5.4 The Appropriateness and Impacts of Big Data Tools on
Smallholder Farmers in Developing Economies 109
5.4.1 Access to inputs and resources 111
5.4.2 Access to insurance and other risk-spreading
mechanisms 111
5.4.3 Impacts on farming process and productivity 113
5.4.4 Increase in small-scale farmer’s access to market,
marketability of products and bargaining power 113
5.4.5 Improving efficiency of the downstream activities in the
supply chain 114
5.4.6 Improving crop quality 115
5.5 Some Challenges and Obstacles 115
5.6 Adapting to Various Types of Pressures 117
5.7 Agricultural Big Data Projects with Diverse Impacts:
A Comparison of TH Milk and Agrilife 118
5.7.1 The TH Milk facility 118
5.7.2 The Agrilife platform: expanding access to credits for
African farmers 120
5.7.3 A comparison of Agrilife platform and TH Milk facility 121
5.8 Relevance of Big Data Dimensions 123
5.9 Discussion and Concluding Comments 124
6 Big Data’s Roles in Increasing Smallholder Farmers’ Access to
Finance 132
6.1 Introduction 132
6.2 Diverse Models and Multiple Approaches to Assess
Creditworthiness 134
6.3 Big Data Companies Operating in the Developing World 135
6.2.1 Cignifi 135
6.2.2 Kreditech 135
6.2.3 Lenddo 136
6.2.4 Alibaba 136
Contents ix
6.2.5 Tencent 138
6.2.6 Kueski (Mexico) 138
6.2.7 JD.com (Jingdong Mall) 139
6.3 The Role of Big Data in Facilitating Access to Finance for
Smallholder Farmers 139
6.3.1 Utilizing different categories of financial and non-financial
information 140
6.3.2 The role of BD in reducing information opacity and
transaction costs 142
6.4 Enabling and Incentivizing Smallholder Farmers to Participate in
the Market 143
6.5 Risks and Challenges 145
6.6 Discussion and Concluding Comments 146
7 Data Privacy and Security Issues Facing Smallholder Farmers
and Poor Communities in Developing Countries 152
7.1 Introduction 152
7.2 Privacy, Data Protection and Security Issues Associated with Big
Data in Developing Countries 153
7.2.1 Agriculture 155
7.2.2 Health care 156
7.3 Variation in Institutionalization of Cybersecurity and Privacy
Issues Across Developing Countries and Groups of People 157
7.3.1 Variation in consumers’ orientation to data security and
privacy 157
7.4 Institutionalization of Data Privacy and Security Issues in
Developing Countries 158
7.4.1 National level 158
7.4.2 Industry standards 159
7.4.3 Trade associations 160
7.4.4 Professional associations 160
7.4.5 Inter-organizational networks 160
7.4.6 Company-specific guidelines 161
7.4.7 Individual farmers 162
7.5 Discussion and Concluding Comments 163
8 Lessons Learned, Implications and the Way Forward 169
8.1 Introduction 169
8.2 The Appropriateness of Big Data in the Developing World 171
8.2.1 Relative advantage 171
8.2.2 Compatibility 171
8.2.3 Complexity 172
8.2.4 Observability 173
8.2.5 Trialability 173
8.3 The Meaning and Significance of Big Data in the Context of
Developing Countries 173
8.4 Big Data and Transparency 174