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

By: Material type: TextTextPublisher: Oxford : CAB International, 2016Copyright date: ©2016Edition: 1st edDescription: 1 online resource (258 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781780648705
Subject(s): Genre/Form: Additional physical formats: Print version:: Big Data's Big Potential in Developing EconomiesDDC classification:
  • 005.7094
LOC classification:
  • QA76.9.B45K74 2016
Online resources:
Contents:
Cover Page -- Title Page -- Copyright Page -- Contents -- Abbreviations -- About the author -- Preface and Acknowledgements -- 1 Big Data in Developing Countries: Current Status, Opportunities and Challenges -- 1.1 Introduction -- 1.2 Definitions and Explanations of Key Terms -- 1.2.1 Algorithm -- 1.2.2 Big Data -- 1.2.3 Business model -- 1.2.4 Cloud computing -- 1.2.5 Developing economies -- 1.2.6 Drip irrigation -- 1.2.7 Environmental monitoring -- 1.2.8 Institutionalization -- 1.2.9 Least developed countries (LDCs) -- 1.2.10 The Internet of Things -- 1.2.11 Machine-to-machine connections -- 1.2.12 Precision agriculture -- 1.2.13 Radio-frequency identification -- 1.2.14 Sensor -- 1.3 Characteristics of Big Data -- 1.3.1 Volume -- 1.3.2 Velocity -- 1.3.3 Variety -- 1.3.4 Variability -- 1.3.5 Complexity -- 1.4 Key Areas of Big Data Deployment in Developing Countries -- 1.4.1 E-commerce -- 1.4.2 Oil and gas -- 1.4.3 Banking, finance and insurance -- 1.4.4 Improving disaster mitigation and preparedness -- 1.4.5 Enhancing transparency and reducing corruption -- 1.5 The Relationship between Big Data, Mobility, the Internet of Things and Cloud Computing in the Context of Developing Countries -- 1.6 Determinants of the Development of the Big Data Industry and Market -- 1.6.1 Social and political dimensions -- 1.6.2 Economic dimension -- 1.7 Some Forces to Overcome the Adverse Economic, Political and Cultural Circumstances -- 1.7.1 Multinationals launching Big Data applications in developing countries -- 1.7.2 The roles of international development agencies -- 1.8 Agriculture, Health and Environment: Intricate Relationship -- 1.9 Discussion and Concluding Comments -- 2 Big Data Ecosystem in Developing Countries -- 2.1 Introduction -- 2.2 Context Dependence in Big Data Models -- 2.3 Barriers, Challenges and Obstacles in Using Big Data.
2.3.1 Low degree of digitization -- 2.3.2 Costs associated with participating in the digital economy -- 2.3.3 Data usability -- 2.3.4 Poor data quality -- 2.3.5 Low degree of value chain integration and disconnection between data users and producers -- 2.3.6 Interoperability and standardization issues -- 2.3.7 Big Data skills deficit -- 2.3.8 Values and cultures -- 2.4 Some Encouraging and Favourable Signs -- 2.5 Big Data-Related Entrepreneurship and Some Notable Big Data Companies Operating in the Developing World -- 2.5.1 Alibaba -- 2.5.2 Mediatrac -- 2.5.3 Nedbank -- 2.6 The Internet of Things as a Key Component of Big Data -- 2.6.1 Health care -- 2.6.2 Environmental security and resource conservation -- 2.6.3 Agriculture -- 2.7 Creating a Virtuous Circle of Effective Big Data Deployment -- 2.7.1 Existing actors in the Big Data ecosystem -- 2.7.2 Entry of new actors in the Big Data ecosystem -- 2.8 Discussion and Concluding Comments -- 3 Big Data in Environmental Protection and Resources Conservation -- 3.1 Introduction -- 3.2 Various Data Sources in the Context of Environmental Monitoring and Protection -- 3.2.1 The Internet of Things -- 3.2.2 Social networking websites -- 3.2.3 Remote sensing technologies -- 3.3 Characteristics of Big Data in the Context of Environmental Monitoring and Protection -- 3.3.1 Volume -- 3.3.2 Velocity -- 3.3.3 Variety -- 3.3.4 Variability -- 3.3.5 Complexity -- 3.4 Foreign and Local Big Data Technologies in Environmental Monitoring and Protection -- 3.4.1 Role of foreign multinational corporations -- 3.4.2 Big Data applications created in developing countries -- 3.5 The Roles of Philanthropic and International Development Organizations -- 3.6 Big Data and Transparency: Fighting Environmental Crimes and Injustices -- 3.6.1 The 2015 Indonesian fires -- 3.6.2 Deforestation of rainforests in the Peruvian Amazon.
3.7 Discussion and Concluding Comments -- 4 Big Data in Health-Care Delivery and Outcomes -- 4.1 Introduction -- 4.2 Big Data Deployment in Delivering Health-Care Services in Developing Countries: Some Examples -- 4.3 Foreign as well as Locally Developed Big Data-Based Health-Care Solutions -- 4.3.1 Solutions developed in industrialized countries -- 4.3.2 Locally developed solutions -- 4.4 The Role of Big Data in Expanding Access to Health-Care Services -- 4.4.1 Geographic accessibility -- 4.4.2 Availability -- 4.4.3 Financial accessibility -- 4.4.4 Acceptability -- 4.5 Big Data-Based Solutions to Fight Fake Drugs -- 4.5.1 The prevalence of fake drugs and some Big Data-based solutions to fight the problem -- 4.5.2 Expansion to new market segments -- 4.5.3 Some challenges faced -- 4.6 The Role of Big Data in Promoting Transparency and Accountability in the Health-Care Sector -- 4.7 The Internet of Things and Health Care -- 4.8 Discussion and Concluding Comments -- 5 Big Data in Agriculture -- 5.1 Introduction -- 5.2 Various Data Sources and Technological Trends -- 5.2.1 The Internet of Things and agriculture -- 5.2.2 Drip irrigation systems -- 5.2.3 Soil infrared spectroscopy -- 5.2.4 Data and information created via agriculture and farming platforms -- 5.3 The Origin of Big Data-Related Innovations in the Agricultural Sector -- 5.3.1 Big Data technologies developed in industrialized countries -- 5.3.2 Undertaking Big Data-related innovations locally -- 5.4 The Appropriateness and Impacts of Big Data Tools on Smallholder Farmers in Developing Economies -- 5.4.1 Access to inputs and resources -- 5.4.2 Access to insurance and other risk-spreading mechanisms -- 5.4.3 Impacts on farming process and productivity -- 5.4.4 Increase in small-scale farmer's access to market, marketability of products and bargaining power.
5.4.5 Improving efficiency of the downstream activities in the supply chain -- 5.4.6 Improving crop quality -- 5.5 Some Challenges and Obstacles -- 5.6 Adapting to Various Types of Pressures -- 5.7 Agricultural Big Data Projects with Diverse Impacts: A Comparison of TH Milk and Agrilife -- 5.7.1 The TH Milk facility -- 5.7.2 The Agrilife platform: expanding access to credits for African farmers -- 5.7.3 A comparison of Agrilife platform and TH Milk facility -- 5.8 Relevance of Big Data Dimensions -- 5.9 Discussion and Concluding Comments -- 6 Big Data's Roles in Increasing Smallholder Farmers' Access to Finance -- 6.1 Introduction -- 6.2 Diverse Models and Multiple Approaches to Assess Creditworthiness -- 6.3 Big Data Companies Operating in the Developing World -- 6.2.1 Cignifi -- 6.2.2 Kreditech -- 6.2.3 Lenddo -- 6.2.4 Alibaba -- 6.2.5 Tencent -- 6.2.6 Kueski (Mexico) -- 6.2.7 JD.com (Jingdong Mall) -- 6.3 The Role of Big Data in Facilitating Access to Finance for Smallholder Farmers -- 6.3.1 Utilizing different categories of financial and non-financial information -- 6.3.2 The role of BD in reducing information opacity and transaction costs -- 6.4 Enabling and Incentivizing Smallholder Farmers to Participate in the Market -- 6.5 Risks and Challenges -- 6.6 Discussion and Concluding Comments -- 7 Data Privacy and Security Issues Facing Smallholder Farmers and Poor Communities in Developing Countries -- 7.1 Introduction -- 7.2 Privacy, Data Protection and Security Issues Associated with Big Data in Developing Countries -- 7.2.1 Agriculture -- 7.2.2 Health care -- 7.3 Variation in Institutionalization of Cybersecurity and Privacy Issues Across Developing Countries and Groups of People -- 7.3.1 Variation in consumers' orientation to data security and privacy -- 7.4 Institutionalization of Data Privacy and Security Issues in Developing Countries.
7.4.1 National level -- 7.4.2 Industry standards -- 7.4.3 Trade associations -- 7.4.4 Professional associations -- 7.4.5 Inter-organizational networks -- 7.4.6 Company-specific guidelines -- 7.4.7 Individual farmers -- 7.5 Discussion and Concluding Comments -- 8 Lessons Learned, Implications and the Way Forward -- 8.1 Introduction -- 8.2 The Appropriateness of Big Data in the Developing World -- 8.2.1 Relative advantage -- 8.2.2 Compatibility -- 8.2.3 Complexity -- 8.2.4 Observability -- 8.2.5 Trialability -- 8.3 The Meaning and Significance of Big Data in the Context of Developing Countries -- 8.4 Big Data and Transparency -- 8.5 Trickling up of Big Data-Related Innovations from Developing to Developed Nations -- 8.6 Implications for Businesses -- 8.7 Implications for Policy Makers -- 8.8 Future Research Implications -- 8.9 Final Thought -- Appendix: Integrative Cases of Big Data Deployment in Agriculture, Environmental Security and Health Care -- Case 1: Big Data Deployment in the Chinese Health-Care Industry -- A1.1 Big Data-based mobile health-care apps -- A1.2 Resources to create a healthy society -- A1.3 Government investment as a trigger -- A1.4 Well-known Big Data companies in the value chain of the health-care sector -- A1.5 Foreign companies promoting BD deployment in the Chinese health-care industry -- A1.6 Professional and ethical issues -- A1.7 Concluding comments -- Case 2: Big Data Deployment in the Fight Against Ebola -- A2.1 Citizen engagement and analytics system -- A2.2 Tracking the population movement during the Ebola crisis -- A2.3 Tracking the spread -- A2.4 Some challenges -- A2.5 Concluding comments -- Case 3: Kilimo Salama's Weather-Based Index Insurance for Smallholder Farmers -- A3.1 Kilimo Salama's weather-based index insurance -- A3.2 Appropriateness of index insurance -- A3.3 Benefits to farmers.
A3.4 Concluding comments.
Summary: Big Data has the power to change all aspects of agriculture, environmental protection and healthcare, especially in developing countries, by allowing new levels of analysis and tailoring of impacts. The capacity to develop infrastructure and the practical implications of data security are examined in depth.
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Cover Page -- Title Page -- Copyright Page -- Contents -- Abbreviations -- About the author -- Preface and Acknowledgements -- 1 Big Data in Developing Countries: Current Status, Opportunities and Challenges -- 1.1 Introduction -- 1.2 Definitions and Explanations of Key Terms -- 1.2.1 Algorithm -- 1.2.2 Big Data -- 1.2.3 Business model -- 1.2.4 Cloud computing -- 1.2.5 Developing economies -- 1.2.6 Drip irrigation -- 1.2.7 Environmental monitoring -- 1.2.8 Institutionalization -- 1.2.9 Least developed countries (LDCs) -- 1.2.10 The Internet of Things -- 1.2.11 Machine-to-machine connections -- 1.2.12 Precision agriculture -- 1.2.13 Radio-frequency identification -- 1.2.14 Sensor -- 1.3 Characteristics of Big Data -- 1.3.1 Volume -- 1.3.2 Velocity -- 1.3.3 Variety -- 1.3.4 Variability -- 1.3.5 Complexity -- 1.4 Key Areas of Big Data Deployment in Developing Countries -- 1.4.1 E-commerce -- 1.4.2 Oil and gas -- 1.4.3 Banking, finance and insurance -- 1.4.4 Improving disaster mitigation and preparedness -- 1.4.5 Enhancing transparency and reducing corruption -- 1.5 The Relationship between Big Data, Mobility, the Internet of Things and Cloud Computing in the Context of Developing Countries -- 1.6 Determinants of the Development of the Big Data Industry and Market -- 1.6.1 Social and political dimensions -- 1.6.2 Economic dimension -- 1.7 Some Forces to Overcome the Adverse Economic, Political and Cultural Circumstances -- 1.7.1 Multinationals launching Big Data applications in developing countries -- 1.7.2 The roles of international development agencies -- 1.8 Agriculture, Health and Environment: Intricate Relationship -- 1.9 Discussion and Concluding Comments -- 2 Big Data Ecosystem in Developing Countries -- 2.1 Introduction -- 2.2 Context Dependence in Big Data Models -- 2.3 Barriers, Challenges and Obstacles in Using Big Data.

2.3.1 Low degree of digitization -- 2.3.2 Costs associated with participating in the digital economy -- 2.3.3 Data usability -- 2.3.4 Poor data quality -- 2.3.5 Low degree of value chain integration and disconnection between data users and producers -- 2.3.6 Interoperability and standardization issues -- 2.3.7 Big Data skills deficit -- 2.3.8 Values and cultures -- 2.4 Some Encouraging and Favourable Signs -- 2.5 Big Data-Related Entrepreneurship and Some Notable Big Data Companies Operating in the Developing World -- 2.5.1 Alibaba -- 2.5.2 Mediatrac -- 2.5.3 Nedbank -- 2.6 The Internet of Things as a Key Component of Big Data -- 2.6.1 Health care -- 2.6.2 Environmental security and resource conservation -- 2.6.3 Agriculture -- 2.7 Creating a Virtuous Circle of Effective Big Data Deployment -- 2.7.1 Existing actors in the Big Data ecosystem -- 2.7.2 Entry of new actors in the Big Data ecosystem -- 2.8 Discussion and Concluding Comments -- 3 Big Data in Environmental Protection and Resources Conservation -- 3.1 Introduction -- 3.2 Various Data Sources in the Context of Environmental Monitoring and Protection -- 3.2.1 The Internet of Things -- 3.2.2 Social networking websites -- 3.2.3 Remote sensing technologies -- 3.3 Characteristics of Big Data in the Context of Environmental Monitoring and Protection -- 3.3.1 Volume -- 3.3.2 Velocity -- 3.3.3 Variety -- 3.3.4 Variability -- 3.3.5 Complexity -- 3.4 Foreign and Local Big Data Technologies in Environmental Monitoring and Protection -- 3.4.1 Role of foreign multinational corporations -- 3.4.2 Big Data applications created in developing countries -- 3.5 The Roles of Philanthropic and International Development Organizations -- 3.6 Big Data and Transparency: Fighting Environmental Crimes and Injustices -- 3.6.1 The 2015 Indonesian fires -- 3.6.2 Deforestation of rainforests in the Peruvian Amazon.

3.7 Discussion and Concluding Comments -- 4 Big Data in Health-Care Delivery and Outcomes -- 4.1 Introduction -- 4.2 Big Data Deployment in Delivering Health-Care Services in Developing Countries: Some Examples -- 4.3 Foreign as well as Locally Developed Big Data-Based Health-Care Solutions -- 4.3.1 Solutions developed in industrialized countries -- 4.3.2 Locally developed solutions -- 4.4 The Role of Big Data in Expanding Access to Health-Care Services -- 4.4.1 Geographic accessibility -- 4.4.2 Availability -- 4.4.3 Financial accessibility -- 4.4.4 Acceptability -- 4.5 Big Data-Based Solutions to Fight Fake Drugs -- 4.5.1 The prevalence of fake drugs and some Big Data-based solutions to fight the problem -- 4.5.2 Expansion to new market segments -- 4.5.3 Some challenges faced -- 4.6 The Role of Big Data in Promoting Transparency and Accountability in the Health-Care Sector -- 4.7 The Internet of Things and Health Care -- 4.8 Discussion and Concluding Comments -- 5 Big Data in Agriculture -- 5.1 Introduction -- 5.2 Various Data Sources and Technological Trends -- 5.2.1 The Internet of Things and agriculture -- 5.2.2 Drip irrigation systems -- 5.2.3 Soil infrared spectroscopy -- 5.2.4 Data and information created via agriculture and farming platforms -- 5.3 The Origin of Big Data-Related Innovations in the Agricultural Sector -- 5.3.1 Big Data technologies developed in industrialized countries -- 5.3.2 Undertaking Big Data-related innovations locally -- 5.4 The Appropriateness and Impacts of Big Data Tools on Smallholder Farmers in Developing Economies -- 5.4.1 Access to inputs and resources -- 5.4.2 Access to insurance and other risk-spreading mechanisms -- 5.4.3 Impacts on farming process and productivity -- 5.4.4 Increase in small-scale farmer's access to market, marketability of products and bargaining power.

5.4.5 Improving efficiency of the downstream activities in the supply chain -- 5.4.6 Improving crop quality -- 5.5 Some Challenges and Obstacles -- 5.6 Adapting to Various Types of Pressures -- 5.7 Agricultural Big Data Projects with Diverse Impacts: A Comparison of TH Milk and Agrilife -- 5.7.1 The TH Milk facility -- 5.7.2 The Agrilife platform: expanding access to credits for African farmers -- 5.7.3 A comparison of Agrilife platform and TH Milk facility -- 5.8 Relevance of Big Data Dimensions -- 5.9 Discussion and Concluding Comments -- 6 Big Data's Roles in Increasing Smallholder Farmers' Access to Finance -- 6.1 Introduction -- 6.2 Diverse Models and Multiple Approaches to Assess Creditworthiness -- 6.3 Big Data Companies Operating in the Developing World -- 6.2.1 Cignifi -- 6.2.2 Kreditech -- 6.2.3 Lenddo -- 6.2.4 Alibaba -- 6.2.5 Tencent -- 6.2.6 Kueski (Mexico) -- 6.2.7 JD.com (Jingdong Mall) -- 6.3 The Role of Big Data in Facilitating Access to Finance for Smallholder Farmers -- 6.3.1 Utilizing different categories of financial and non-financial information -- 6.3.2 The role of BD in reducing information opacity and transaction costs -- 6.4 Enabling and Incentivizing Smallholder Farmers to Participate in the Market -- 6.5 Risks and Challenges -- 6.6 Discussion and Concluding Comments -- 7 Data Privacy and Security Issues Facing Smallholder Farmers and Poor Communities in Developing Countries -- 7.1 Introduction -- 7.2 Privacy, Data Protection and Security Issues Associated with Big Data in Developing Countries -- 7.2.1 Agriculture -- 7.2.2 Health care -- 7.3 Variation in Institutionalization of Cybersecurity and Privacy Issues Across Developing Countries and Groups of People -- 7.3.1 Variation in consumers' orientation to data security and privacy -- 7.4 Institutionalization of Data Privacy and Security Issues in Developing Countries.

7.4.1 National level -- 7.4.2 Industry standards -- 7.4.3 Trade associations -- 7.4.4 Professional associations -- 7.4.5 Inter-organizational networks -- 7.4.6 Company-specific guidelines -- 7.4.7 Individual farmers -- 7.5 Discussion and Concluding Comments -- 8 Lessons Learned, Implications and the Way Forward -- 8.1 Introduction -- 8.2 The Appropriateness of Big Data in the Developing World -- 8.2.1 Relative advantage -- 8.2.2 Compatibility -- 8.2.3 Complexity -- 8.2.4 Observability -- 8.2.5 Trialability -- 8.3 The Meaning and Significance of Big Data in the Context of Developing Countries -- 8.4 Big Data and Transparency -- 8.5 Trickling up of Big Data-Related Innovations from Developing to Developed Nations -- 8.6 Implications for Businesses -- 8.7 Implications for Policy Makers -- 8.8 Future Research Implications -- 8.9 Final Thought -- Appendix: Integrative Cases of Big Data Deployment in Agriculture, Environmental Security and Health Care -- Case 1: Big Data Deployment in the Chinese Health-Care Industry -- A1.1 Big Data-based mobile health-care apps -- A1.2 Resources to create a healthy society -- A1.3 Government investment as a trigger -- A1.4 Well-known Big Data companies in the value chain of the health-care sector -- A1.5 Foreign companies promoting BD deployment in the Chinese health-care industry -- A1.6 Professional and ethical issues -- A1.7 Concluding comments -- Case 2: Big Data Deployment in the Fight Against Ebola -- A2.1 Citizen engagement and analytics system -- A2.2 Tracking the population movement during the Ebola crisis -- A2.3 Tracking the spread -- A2.4 Some challenges -- A2.5 Concluding comments -- Case 3: Kilimo Salama's Weather-Based Index Insurance for Smallholder Farmers -- A3.1 Kilimo Salama's weather-based index insurance -- A3.2 Appropriateness of index insurance -- A3.3 Benefits to farmers.

A3.4 Concluding comments.

Big Data has the power to change all aspects of agriculture, environmental protection and healthcare, especially in developing countries, by allowing new levels of analysis and tailoring of impacts. The capacity to develop infrastructure and the practical implications of data security are examined in depth.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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