scholarly journals Studying the Inter-Relationship amongst Barriers to Adoption of Big Data Analytics in SMEs in Developing Countries

2019 ◽  
Vol 182 (42) ◽  
pp. 5-14
Author(s):  
Lakshay Aggarwal ◽  
Remica Aggarwal ◽  
Veena Aggarwal
Author(s):  
Forgor Lempogo ◽  
Ezer Osei Yeboah-Boateng ◽  
William Leslie Brown-Acquaye

In a world increasingly driven by data, most developed economies are leveraging big data to achieve greater feats in various sectors of their economies. From advertisement, commerce, healthcare, and energy to defense, big data has given new insights into the huge volume of data accumulated over the past few decades that is helping reshape our knowledge and understanding of these sectors. Unfortunately, the same cannot be said about the state of big data in the developing world, where investments in IT infrastructure are dangerously low, keeping huge proportions of the population offline. This chapter discussed the challenges that exist in developing countries, which affect the smooth take-off of big data and data science as well as recommendations as to how countries and companies in the developing world can overcome these challenges to harness the benefits and opportunities presented by this technology.


2019 ◽  
pp. 11-27
Author(s):  
Tran Vu Pham ◽  
Quang Tran Minh ◽  
Hong-Linh Truong ◽  
Hoa Dam

Author(s):  
Koppula Srinivas Rao ◽  
Saravanan S. ◽  
Pattem Sampath Kumar ◽  
Rajesh V. ◽  
K. Raghu

The benefits of data analytics and Hadoop in application areas where vast volumes of data move in and out are examined and exposed in this report. Developing countries with large populations, such as India, face several challenges in the field of healthcare, including rising costs, addressing the needs of economically disadvantaged people, gaining access to hospitals, and conducting medical research, especially during epidemics. This chapter discusses the role of big data analytics and Hadoop, as well as their effect on providing healthcare services to all at the lowest possible cost.


2018 ◽  
Vol 164 ◽  
pp. 01004
Author(s):  
Annas Vijaya ◽  
Linda Salma Angreani ◽  
Mokhamad Amin Hariyadi

The aim of this paper is to design a prototype model that can be used to better understand development equity for villages in terms of public monitoring and evaluation. In designing the model, the research has reviewed several techniques of big data analytics as well as alignment of business strategic objectives and technology. The prototype model also tested using several types of data. Although some obstacles have found, as it also found in the reviewed literature, a prototype model which can guide researchers and practitioners to understand ways to capture public monitoring is presented in this paper. Furthermore, Information systems researchers could use this prototype model for further research to get a deeper understanding of big data analytics roles for development, particularly in developing countries.


2017 ◽  
Vol 15 (1) ◽  
pp. 260-270 ◽  
Author(s):  
Billy Mathias Kalema ◽  
Motau Mokgadi

Regardless of the nature, size, or business sector, organizations are now collecting burgeoning various volumes of data in different formats. As much as voluminous data are necessary for organizations to draw good insights needed for making informed decisions, traditional architectures and existing infrastructures are limited in delivering fast analytical processing needed for these Big Data. For success organizations need to apply technologies and methods that could empower them to cost effectively analyze these Big Data. However, many organizations in developing countries are constrained with limited access to technology, finances, infrastructure and skilled manpower. Yet, for productive use of these technologies and methods needed for Big Data analytics, both the organizations and their workforce need to be prepared. The major objective for this study was to investigate developing countries organizations’ readiness for Big Data analytics. Data for the study were collected from a public sector in South Africa and analyzed quantitatively. Results indicated that scalability, ICT infrastructure, top management support, organization size, financial resources, culture, employees’ e-skills, organization’s customers’ and vendors are significant factors for organizations’ readiness for Big Data analytics. Likewise strategies, security and competitive pressure were found not to be significant. This study contributes to the scanty literature of Big Data analytics by providing empirical evidence of the factors that need attention when organizations are preparing for Big Data analytics.


2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan

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