Developing countries organizations’ readiness for Big Data analytics

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.

[1]  Janusz Wielki,et al.  Implementation of the Big Data concept in organizations - possibilities, impediments and challenges , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[2]  BILLY M. KALEMA The Application of Structural Equation Modeling Technique to Analyze Students Priorities in Using Course Management Systems , 2012 .

[3]  Keith W. Miller,et al.  Big Data: New Opportunities and New Challenges [Guest editors' introduction] , 2013, Computer.

[4]  Raymond Gardiner Goss,et al.  Heading towards big data building a better data warehouse for more data, more speed, and more users , 2013, ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference.

[5]  Lori Bowen Ayre,et al.  Open Data: What It Is and Why You Should Care , 2017, Public Libr. Q..

[6]  Danny T. Moore,et al.  Roadmaps and Maturity Models: Pathways toward Adopting Big Data , 2014 .

[7]  Mihaela Robila,et al.  Economic pressure and social exclusion in Europe , 2006 .

[8]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[9]  J. Alberto Espinosa,et al.  Big Data: Issues and Challenges Moving Forward , 2013, 2013 46th Hawaii International Conference on System Sciences.

[10]  M. Fleischer,et al.  processes of technological innovation , 1990 .

[11]  Kim Pries,et al.  Big Data Analytics: A Practical Guide for Managers , 2015 .

[12]  Melnned M. Kantardzic Big Data Analytics , 2013, Lecture Notes in Computer Science.

[13]  B. Weiner,et al.  Implementation Science a Theory of Organizational Readiness for Change , 2009 .

[14]  Pornchai Chanyagorn,et al.  ICT Readiness Assessment Model for Public and Private Organizations in Developing Country , 2011 .

[15]  Ajantha Dahanayake,et al.  A Requirements Specification Framework for Big Data Collection and Capture , 2015, ADBIS.

[16]  B. M. Kalema,et al.  Utilizing IT to Enhance Knowledge Sharing for School Educators in Developing Countries , 2016, Electron. J. Inf. Syst. Dev. Ctries..

[17]  J. E. Kurtz,et al.  Internal Consistency, Retest Reliability, and Their Implications for Personality Scale Validity , 2011, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[18]  A. Armenakis,et al.  Change Readiness , 2013 .

[19]  Sachchidanand Singh,et al.  Big Data analytics , 2012 .

[20]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[21]  Julie Pallant,et al.  SPSS survival manual : a step by step guide to data analysis using SPSS for Windows , 2001, Behaviour Change.

[22]  Y T Sweeney,et al.  Successful change: renaissance without revolution. , 1994, Seminars for nurse managers.