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
  • TO CITE АНОТАЦІЯ
  • Cited by
    21 articles
  • 1929 Views
  • 474 Downloads

Creative Commons License
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.

view full abstract hide full abstract
    • Fig. 1. Research model
    • Table 1. Regression analysis
    • Al-Najran, N., and Dahanayake, A. (2015). A Requirement Specification Framework for Big Data Collection and Capture. In T. Morzy, P. Valduriez and L. Bellatreche (Eds), New Trends in Databases and Information Systems. Communications in Computer and Information Science, 539, (pp. 12-19).
    • Chanyagorn, P., and Kungwannarongkun, B. (2011). ICT Readiness Assessment Model for Public and Private Organizations in Developing Country. International Journal of Information and Education Technology, 1 (2), 99-106.
    • Chen, H., Chiang, R. H. and Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36 (4), 1165-1188.
    • Ferguson, M. (2012). Architecting a Big Data Platform for Analytics. A Whitepaper Prepared for IBM.
    • Forbes Insights (2015). Betting on Big Data: How the right culture, strategy and investments can help you leapfrog the competition.
    • Goss, R. G., and Veeramuthu, K. (2013). Heading towards Big Data Building a Better Data Warehouse for More Data, More Speed, and More Users, IEE, Proceedings of the Advanced Semiconductor Manufacturing Conference (ASMC), (pp. 220-225).
    • Halper, F. and Krishnan, K. (2013). TDWI Big Data Maturity Model Guide Interpreting your Assessment Score.
    • Juniper Networks. (2012). Introduction to Big Data: Infrastructure and Networking Considerations Leveraging Hadoop-Based Big Data Architectures for a Scalable, High-Performance Analytics Platform. White paper [online]
    • Kaisler, S. Armour, F. Espinosa, J. A. and Money, W. (2013). Big data: Issues and Challenges Moving Forward. In System Sciences (HICSS), IEEE, proceedings of the 46th Hawaii International Conference on System Science, (pp. 995-1004).
    • Kalema, B. M., Motjolopane, I. M., and Motsi, L. (2016). Utilizing IT to Enhance Knowledge Sharing for School Educators in Developing Countries. The Electronic Journal of Information Systems in Developing Countries, 73 (8), pp. 1-22.
    • Kalema, B. M., Olugbara, O. O. and Kekwaletswe, R. M. (2011). The application of structural equation modelling technique to analyse students’ priorities in using course management systems. International Journal of Computing and ICT Research, 5, 34 - 44.
    • Kelly, J. (2013). Taming Big Data [Online].
    • McCrae, R. R., Kurtz, J. E. Yamagata, S. and Terracciano, A. (2011). Internal Consistency, Retest Reliability, and their Implications for Personality Scale Validity. Personality and Social Psychological Review. 15(1), 28-50.
    • Michael, K. and Miller, K. W. (2013). Big Data: New Opportunities and New Challenges. IEEE Computer Society, 46, (6), 22-24.
    • Moore, D. T. (2014). Roadmaps and Maturity Models: Pathways toward Adopting Big Data. Proceedings of Information Systems Applied Research, Baltimore, Maryland.
    • Pallant, J. (2010). SPSS Survival Manual. A Step By Step Guide to Data Analysis Using SPSS for Windows. McGraw-Hill International.
    • Pearson, L., Singh, R., and Mackey, K. (2014). How to minimize risks and maximize business results. POINTB WEBSITE.
    • Pries, K. H., and Dunnigan. R. (2014). Big Data Analytics: A Practical Guide for Managers, CRC Press, New York, USA.
    • Rafferty, A. E, Jimmieson, N. L, and. Armenakis, A. A. (2013) Change Readiness: A Multilevel Review. Journal of Management, 39 (1), 110-135.
    • Robila, M. (2006). Economic Pressure and Social Exclusion in Europe. The Social Science Journal, 43, 85-97.
    • Sarfaraz, A. (2016). 7 Steps to Build a Big Data Culture in Your Company. Valuence Analytics [Online].
    • Singh S., and Singh. N. (2012). Big Data Analytics. Proceedings of the International Conference on Communication, Information and Computing Technology (ICCICT), Oct. 19-20, Mumbai, India.
    • Sweeney, Y. T. and Whitaker, C. (1994). Successful Change: Renaissance without Revolution. Semin Nurse Management. 2, 196-202.
    • Tornatzky, L. G. and Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books, Mass.: Lexington, Massachusetts.
    • Venkatesh, V., Morris, M. G. and Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27 (3), 425- 478.
    • Villars, R. L., Olofson, C. W. and Eastwood, M. (2011). Big Data: What it is and why you should Care. IDC. White Paper.
    • Weiner, B. J. (2009). A Theory of Organizational Readiness for Change. Implementation Science, 4 (67), 2009: [online]
    • Wielki, J. (2013). Implementation of the Big Data Concept in Organizations-Possibilities, Impediments and Challenges. In Computer Science and Information Systems (FedCSIS), Federated Conference on IEEE, pp. 985-989.
Related Articles

  • Regulating Big Data effects in the European insurance market

    Donatella PorriniORCID Researcher ID doi: http://dx.doi.org/10.21511/ins.08(1).2017.01
    Insurance Markets and Companies Volume 8, 2017         Issue #1         pp. 6-15 Views: 1988 Downloads: 349 TO CITE АНОТАЦІЯ

    The article analyzes the regulatory framework in the insurance market in connection with the advent of Big Data, such as information collected from different sources that can be manipulated by new technologies. The use of Big Data offers significant opportunities to the insurance companies in terms of digitization of the distribution channels and greater knowledge of the customers, which is instrumental to a more effective identification of the individual’s risk profile, as well as improvement of the competitiveness. However, regulatory measures are needed for a proper use of Big Data in terms of respect of the individual privacy, potential discrimination and constraint on competition.

  • Big data, 4IR and electronic banking and banking systems applications in South Africa and Nigeria

    Patrick AjibadeORCID Researcher ID, Stephen M. Mutula doi: http://dx.doi.org/10.21511/bbs.15(2).2020.17
    Banks and Bank Systems Volume 15, 2020         Issue #2         pp. 187-199 Views: 1114 Downloads: 293 TO CITE АНОТАЦІЯ

    Efficient banking solutions are an integral part of the business integration of South African and Nigerian economies as the two largest economies in the continent. Security, effectiveness, and integration of banking systems are critical to the sustainable development of the African continent. Therefore, an empirical analysis of the production of research on banking services and systems was conducted. The aim of the study was to examine the robustness of the research findings on banking systems in terms of their importance for the economic sustainability of the continent in the era of the fourth industrial revolution. The study adopted a bibliometric analysis using software clusters to visualize the results. Due to higher visibility of outputs and likely citations, the results showed that the key terms from Google Scholar are ranked higher than outputs from Scopus. Main research interests were related to internet banking (f = 70), e-payment systems (f = 57), telephone banking (f = 56), automated teller machines (f = 54), and mobile banking (f = 40). The results also showed a very low research interest in the technical aspect of online banking services such as security (f = 19, TLS = 40), authentication (f = 17, TLS =33), network security (f =13, TLS = 33), computer crime (f = 16, TLS = 42), and online banking (f = 11, TLS =32). The study found there were insufficient outputs in the area of the fourth industrial revolution (4IR) and banking services in Africa. Future research trends should examine the impact of the 4IR and big data on the banking system, regional economic integration, and sustainable growth in the continent.

  • Digital business research for small and medium-sized enterprises: The case of Ukraine

    Anton KvitkaORCID , Anna KramarenkoORCID , Denys DavydovORCID , Maryna PasmorORCID , Olga DiachekORCID doi: http://dx.doi.org/10.21511/ppm.19(1).2021.15
    Problems and Perspectives in Management Volume 19, 2021         Issue #1         pp. 177-184 Views: 961 Downloads: 371 TO CITE АНОТАЦІЯ

    The use of available digital tools, the entrepreneur’s personal human capital, and the formation and filling of niche markets are seen as the main factors of enterprises’ prosperity. Thus, the study aims to identify the impact of business research on the dynamics of operational and commercial efficiency of small and medium-sized enterprises (SME). The online survey and regression analysis methods based on the data obtained were used. The study revealed that SMEs constantly need to perform data analysis, but despite the need for business intelligence, enterprises rarely use Big Data (cloud technologies). Most small businesses that conduct business research use digital tools, which poses acute problems of staff development and building an effective model for outsourcing research agents. Currently, the economic efficiency of business research is not clearly visible, which is due to the influence of strong external factors, as well as a low level of innovation and research activity of economic agents.

Download Preview
Close
Problems and Perspectives in Management
Problems and Perspectives in Management