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dc.contributor.advisorCoetzee, J.C.
dc.contributor.authorMakhele, S.L.
dc.date.accessioned2018-09-13T13:26:26Z
dc.date.available2018-09-13T13:26:26Z
dc.date.issued2018
dc.identifier.urihttps://cid.org/0000-0003-4798-5364
dc.identifier.urihttp://hdl.handle.net/10394/31006
dc.descriptionMBA, North-West University, Potchefstroom Campus, 2018en_US
dc.description.abstractGlobally the Small and medium enterprises (SMEs) are the main drivers of the economic growth and the big data analytics has been seen as a support solution to help the SMEs to grow and become competitive. The primary goal of this study is to focus on growth, efficiency and effectiveness of SMEs using big data analytics. It also stresses how the big data analytics can add value to the SMEs by using available information to do the meaningful and well-informed business decisions (Ogbuokiri et al., 2015). Coleman et al. (2015) claim that the big data analytics is currently the buzzword in many organizations globally; it can add value in products and services development and improvements, in customer relations, in profitability and in the creation of competitive advantage using available data and information. They also claim that according to the research, many SMEs are not yet using big data analytics and they are therefore behind regarding the big data analytics benefits (Coleman et al., 2015). In this study, the challenges that SMEs face in the adoption of the big data analytics and how to overcome them will be discussed. There will also be some recommendations in order to assist SMEs in how they can manage their available resources to be able to adapt the big data analytics.en_US
dc.language.isoenen_US
dc.publisherNorth-West Universityen_US
dc.subjectSMEsen_US
dc.subjectBig data analyticsen_US
dc.subjectITen_US
dc.subjectData Warehousingen_US
dc.subjectBusiness Intelligenceen_US
dc.subjectClouden_US
dc.subjectMIen_US
dc.subjectOpen Sourceen_US
dc.subjectInterneten_US
dc.subjectComputingen_US
dc.titleInvestigating how SMEs can benefit from Big Data Analyticsen_US
dc.typeThesisen_US
dc.description.thesistypeMastersen_US
dc.contributor.researchID10306498 - Coetzee, Johannes Cornelius (Supervisor)


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