Trends and Challenges Towards an Effective Data-Driven Decision Making in UK SMEs: Case Studies and Lessons Learnt from the Analysis of 85 SMEs
It addresses the problem of effective data science adoption for SMEs, but it is incremental as it focuses on case studies and existing challenges without proposing new solutions.
This paper analyzes trends and challenges in implementing data-driven decision-making for UK SMEs, based on a case study of 85 SMEs, highlighting the potential benefits like productivity and innovation but noting barriers such as limited resources and IT investments.
The adoption of data science brings vast benefits to Small and Medium-sized Enterprises (SMEs) including business productivity, economic growth, innovation and jobs 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 limited resources and restricted access to financing. This paper presents trends and challenges towards an effective data-driven decision making for organisations based on a case study of 85 SMEs, mostly from the West Midlands region of England. The work is supported as part of a 3 years ERDF (European Regional Development Funded project) in the areas of big data management, analytics and business intelligence. We present two case studies that demonstrates the potential of Digitisation, AI and Machine Learning and use these as examples to unveil challenges and showcase the wealth of current available opportunities for SMEs.