Intelligent Information Management

Volume 12, Issue 5 (September 2020)

ISSN Print: 2160-5912   ISSN Online: 2160-5920

Google-based Impact Factor: 1.6  Citations  

Data Mining for Small and Medium Enterprises: A Conceptual Model for Adaptation

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DOI: 10.4236/iim.2020.125011    893 Downloads   3,415 Views  Citations


The following paper explored data mining issues in Small and Medium Enterprises’ (SMEs), firstly exploring the relationship between data mining and economic development. With SME’s contributing most employment prospects and output within any emerging economy such as the Kingdom of Saudi Arabia. Adopting technology will improve SME’s potential for effective decision making and efficient operations. Hence, it is important that SMEs have access to data mining techniques and implement the most suited into their business to improve their business intelligence (BI). The paper is aimed to critically review the existing literature on data mining in the field of SME to provide a theoretical underpinning for any future work. It has been found data mining to be complicated and fragmented with a multitude of options available for businesses from quite basic systems implemented within Excel or Access to more sophisticated cloud-based systems. For any business, data mining is trade-off between the need for data analysis, and intelligence against the cost and resource-use of the system put in place. Multiple challenges have been identified to data mining, most notably the resource-intensive nature of systems (both in terms of labor and capital) and the security issues of data collection, analysis and storage; with General Data Protection Regulation (GDPR) a key focus for Kingdom of Saudi Arabia businesses. With these challenges the paper suggests that any SME starts small with an internal data mining exercise to digitalize and analyze their customer data, scaling up over time as the business grows and acquires the resources needed to properly manage any system.

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Saeed, T. (2020) Data Mining for Small and Medium Enterprises: A Conceptual Model for Adaptation. Intelligent Information Management, 12, 183-197. doi: 10.4236/iim.2020.125011.

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