Tawil, Abdel-Rahman H., Mohamed, Muhidin, Schmoor, Xavier, Vlachos, Konstantinos and Haidar, Diana (2024). Trends and Challenges Towards Effective Data-Driven Decision Making in UK Small and Medium-Sized Enterprises: Case Studies and Lessons Learnt from the Analysis of 85 Small and Medium-Sized Enterprises. Big Data and Cognitive Computing, 8 (7),
Abstract
The adoption of data science brings vast benefits to Small and Medium-sized Enterprises (SMEs) including business productivity, economic growth, innovation and job creation. Data science can support SMEs to optimise production processes, anticipate customers’ needs, predict machinery failures and deliver efficient smart services. Businesses can also harness the power of artificial intelligence (AI) and big data, and the smart use of digital technologies to enhance productivity and performance, paving the way for innovation. However, integrating data science decisions into an SME requires both skills and IT investments. In most cases, such expenses are beyond the means of SMEs due to their limited resources and restricted access to financing. This paper presents trends and challenges towards effective data-driven decision making for organisations based on a 3-year long study which covered more than 85 UK SMEs, mostly from the West Midlands region of England. In particular, this study attempts to find answers to several key research questions around data science and AI adoption among UK SMEs, and the advantages of digitalisation and data-driven decision making, as well as the challenges hindering their effective utilisation of these technologies. We also present two case studies that demonstrate the potential of digitisation and data science, and use these as examples to unveil challenges and showcase the wealth of currently available opportunities for SMEs.
Publication DOI: | https://doi.org/10.3390/bdcc8070079 |
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Divisions: | College of Business and Social Sciences > Aston Business School > Operations & Information Management College of Business and Social Sciences College of Business and Social Sciences > Aston Business School Aston University (General) |
Funding Information: | This work was funded by the European Union under the European Regional Development Fund (The Big Data Corridor project: Project no. 12R16P00220) and match-funded by six Project Partners: Birmingham City Council, Aston University, Birmingham City Universit |
Additional Information: | Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Uncontrolled Keywords: | AI adoption,UK small and medium-sized enterprises (SMEs),big data,data analytics,digitalisation,Artificial Intelligence,Information Systems,Management Information Systems,Computer Science Applications |
Publication ISSN: | 2504-2289 |
Data Access Statement: | Restrictions apply to the availability of these data. Data were obtained from third party and are available from the authors with the permission of the third party. |
Last Modified: | 02 Dec 2024 09:07 |
Date Deposited: | 06 Aug 2024 10:02 |
Full Text Link: | |
Related URLs: |
https://www.mdp ... 504-2289/8/7/79
(Publisher URL) http://www.scop ... tnerID=8YFLogxK (Scopus URL) |
PURE Output Type: | Article |
Published Date: | 2024-07 |
Published Online Date: | 2024-07-12 |
Accepted Date: | 2024-07-04 |
Authors: |
Tawil, Abdel-Rahman H.
Mohamed, Muhidin Schmoor, Xavier Vlachos, Konstantinos Haidar, Diana |