An Investigation of the Potential Benefits of Big Data in the Public Sector of Mauritius

Preethivirajsingh Mohabeer, Mohammad Issack Santally and Roopesh Kevin Sungkur ()
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Preethivirajsingh Mohabeer: Central Informatics Bureau
Mohammad Issack Santally: University of Mauritius
Roopesh Kevin Sungkur: University of Mauritius

Journal of the Knowledge Economy, 2019, vol. 10, issue 3, No 17, 1230-1247

Abstract: Abstract This study examines how big data analytics could optimize the use of public funds while ensuring delivery of quality service by public organizations to the citizens of Mauritius. Political Economic Social Technological (PEST) analysis has been carried out to scan the environment to identify at least two major policies and initiatives corresponding to big data that will be impacting the Mauritian Economy in the next 10 years. Subsequently, causal layered analysis (CLA) has been applied for the two signals to create transformative spaces for the creation of alternative futures. Indeed, the findings have demonstrated that open data initiative and the implementation of e-health project in Mauritius would certainly contribute positively to the government of Mauritius. However, this study has revealed through a matrix diagram for probable futures that the Mauritian government should bring amendments to existing conventional laws through reforms and regulations to fully take advantage of big data analytics applications. This is also one of the recommendations of the Mauritius e-Government 2013–2017–Formulation and Implementation of Data Sharing Policy. Considering only the recent emergence of big data analytics in governments, still there is certain aspect of it that needs careful consideration before the full potential of big data could be realized. This research also highlights the factors that need to be addressed for the successful use of Big Data in this particular context.

Keywords: Big data; Mauritius; Public sector; Causal layered analysis; Futures wheel; Data analytics (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s13132-018-0524-2

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