Developing countries organizations’ readiness for Big Data analytics

  • Received November 7, 2016;
    Accepted December 7, 2016;
    Published May 11, 2017
  • Author(s)
    Billy Mathias Kalema , Motau Mokgadi
  • DOI
    http://dx.doi.org/10.21511/ppm.15(1-1).2017.13
  • Article Info
    Volume 15 2017, Issue #1 (cont.), pp. 260-270
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  • Cited by
    26 articles
  • 2156 Views
  • 520 Downloads

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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.

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    • Fig. 1. Research model
    • Table 1. Regression analysis
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