A multilevel approach to big data analysis using analytic tools and actor network theory
Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that...
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| Format: | Article |
| Jezik: | English |
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AOSIS
2018-08-01
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| Serija: | South African Journal of Information Management |
| Teme: | |
| Online dostop: | https://sajim.co.za/index.php/sajim/article/view/914 |
| _version_ | 1828240587661246464 |
|---|---|
| author | Tiko Iyamu |
| author_facet | Tiko Iyamu |
| author_sort | Tiko Iyamu |
| collection | DOAJ |
| description | Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.
Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.
Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.
Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.
Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness. |
| first_indexed | 2024-04-12T21:43:42Z |
| format | Article |
| id | doaj.art-c64b5370b51e4e6290f4fb8deddcad8a |
| institution | Directory Open Access Journal |
| issn | 2078-1865 1560-683X |
| language | English |
| last_indexed | 2024-04-12T21:43:42Z |
| publishDate | 2018-08-01 |
| publisher | AOSIS |
| record_format | Article |
| series | South African Journal of Information Management |
| spelling | doaj.art-c64b5370b51e4e6290f4fb8deddcad8a2022-12-22T03:15:42ZengAOSISSouth African Journal of Information Management2078-18651560-683X2018-08-01201e1e910.4102/sajim.v20i1.914592A multilevel approach to big data analysis using analytic tools and actor network theoryTiko Iyamu0Department of Information Technology, Cape Peninsula University of TechnologyBackground: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness. Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis. Method: In the study, the qualitative methods were employed from the interpretivist approach perspective. Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks. Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.https://sajim.co.za/index.php/sajim/article/view/914actor network theoryanalyticsbig datadata analysisinformation systemsmultilevel |
| spellingShingle | Tiko Iyamu A multilevel approach to big data analysis using analytic tools and actor network theory South African Journal of Information Management actor network theory analytics big data data analysis information systems multilevel |
| title | A multilevel approach to big data analysis using analytic tools and actor network theory |
| title_full | A multilevel approach to big data analysis using analytic tools and actor network theory |
| title_fullStr | A multilevel approach to big data analysis using analytic tools and actor network theory |
| title_full_unstemmed | A multilevel approach to big data analysis using analytic tools and actor network theory |
| title_short | A multilevel approach to big data analysis using analytic tools and actor network theory |
| title_sort | multilevel approach to big data analysis using analytic tools and actor network theory |
| topic | actor network theory analytics big data data analysis information systems multilevel |
| url | https://sajim.co.za/index.php/sajim/article/view/914 |
| work_keys_str_mv | AT tikoiyamu amultilevelapproachtobigdataanalysisusinganalytictoolsandactornetworktheory AT tikoiyamu multilevelapproachtobigdataanalysisusinganalytictoolsandactornetworktheory |