Citation

Kannan, Rathimala and Rosdi, Intan Soraya and Ramakrishnan, Kannan and Abdul Rasid, Haziq Riza and Mohamed Rafy, Mohamed Haryz Izzudin and Yusuf, Sukinurlin and Mohd Salamun, Siti Nurfara Alia (2021) Leveraging business data analytics and machine learning techniques for competitive advantage: case study evidence from small businesses. International Journal of Management, Finance and Accounting, 2 (1). pp. 1-15. ISSN 2735-1009

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Abstract

Data analytics is the essential component in deriving insights from data obtained from multiple sources. It represents the technology, methods and techniques used to obtain insights from massive datasets. As data increases, companies are looking for ways to gain relevant business insights underneath layers of data and information, to help them better understand new business ventures, opportunities, business trends and complex challenges. However, to date, while the extensive benefits of business data analytics to large organizations are widely published, micro, small, and medium sized organisations have not fully grasped the potential benefits to be gained from data analytics using machine learning techniques. This study is guided by the research question of how data analytics using machine learning techniques can benefit small businesses. Using the case study method, this paper outlines how small businesses in two different industries i.e. healthcare and retail can leverage data analytics and machine learning techniques to gain competitive advantage from the data. Details on the respective benefits gained by the small business owners featured in the two case studies provide important answers to the research question.

Item Type:	Article
Uncontrolled Keywords:	Data Analytics, Machine learning Techniques, Prediction models, Market Basket Analysis.
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI) Faculty of Management (FOM)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 May 2021 18:58
Last Modified:	04 May 2021 18:58
URII:	http://shdl.mmu.edu.my/id/eprint/8661

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