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A STRATEGIC BIG DATA ANALYTICS FRAMEWORK TO PROVIDE OPPORTUNITIES FOR SMES

Willetts, Matthew, ATKINS, Anthony and Stanier, Clare (2020) A STRATEGIC BIG DATA ANALYTICS FRAMEWORK TO PROVIDE OPPORTUNITIES FOR SMES. In: INTED2020 Proceedings. IATED, pp. 3033-3042. ISBN 978-84-09-17939-8

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Abstract or description

The paper outlines a holistic strategic framework to overcome the barriers of adoption to Big Data Analytics to support SMEs. Big Data adoption has continued to rapidly increase over the last five years, allowing firms that have successfully adopted the technology to analyse large volumes of complicated, unstructured data in a variety of formats, which has only recently been achievable because of advances in technology. Traditionally, Big Data is associated with large enterprises because of the perceived high entry costs of investing in the required infrastructure, software and data scientists to analyse the data. Due to their smaller size and limited resources, it is a common belief that Big Data is too large for SMEs to adopt and they do not have the volumes of data required to justify the investment. However, this paper demonstrates that there are cost-effective options available for SMEs to adopt including Google Analytics, social media and other Big Data as a Service solutions. This paper identifies the barriers encountered by SMEs in adopting Big Data Analytics and proposes a holistic strategic framework which will allow them to achieve a competitive advantage.

Item Type: Book Chapter, Section or Conference Proceeding
Additional Information: 14th International Technology, Education and Development Conference Valencia, Spain. 2-4 March, 2020.
Uncontrolled Keywords: big data, big data analytics, smes, big data adoption, barriers to big data adoption.
Faculty: School of Digital, Technologies and Arts > Computer Science, AI and Robotics
Event Title: INTED2020 Proceedings
Event Location: Valencia, Spain
Event Dates: 02-04/03/2020
Depositing User: Anthony ATKINS
Date Deposited: 19 Nov 2021 15:27
Last Modified: 04 Mar 2022 01:38
URI: http://eprints.staffs.ac.uk/id/eprint/7081

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