Trends and Challenges Towards an Effective Data-Driven Decision Making
in UK SMEs: Case Studies and Lessons Learnt from the Analysis of 85 SMEs
- URL: http://arxiv.org/abs/2305.15454v1
- Date: Wed, 24 May 2023 17:23:32 GMT
- Title: Trends and Challenges Towards an Effective Data-Driven Decision Making
in UK SMEs: Case Studies and Lessons Learnt from the Analysis of 85 SMEs
- Abstract summary: Data Science can support SMEs to optimise production processes, anticipate customers' needs, predict machinery failures and deliver efficient smart services.
integrating data science decisions into an SME requires both skills and IT investments.
This paper presents trends and challenges towards an effective data-driven decision making for organisations based on a case study of 85 SMEs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Perspective: Towards sustainable exploration of chemical spaces with machine learning [49.29528647472741]
We argue that sustainable progress will rely on open data and models, reusable datasets, and domain-specific AI systems.<n>We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning.
arXiv Detail & Related papers (2026-03-31T13:29:20Z) - A Conceptual Model for AI Adoption in Financial Decision-Making: Addressing the Unique Challenges of Small and Medium-Sized Enterprises [0.0]
This paper presents a conceptual model for the adoption of AI in financial decision-making for SMEs.<n>The proposed model addresses key challenges faced by SMEs, including limited resources, technical expertise, and data management capabilities.<n>By implementing AI incrementally, SMEs can optimize financial forecasting, budgeting, investment strategies, and risk management.
arXiv Detail & Related papers (2025-12-03T23:57:34Z) - When Assurance Undermines Intelligence: The Efficiency Costs of Data Governance in AI-Enabled Labor Markets [5.3700224653806865]
We show that restricting data use significantly reduced GenAI efficiency, leading to lower matching rates, higher employee turnover, and heightened labor market frictions.<n>Our findings reveal the unintended efficiency costs of well-intentioned data governance and highlight that information assurance, while essential for trust, can undermine intelligence-driven efficiency when misaligned with AI system design.
arXiv Detail & Related papers (2025-11-02T05:35:37Z) - Improving AI Efficiency in Data Centres by Power Dynamic Response [74.12165648170894]
The steady growth of artificial intelligence (AI) has accelerated in the recent years, facilitated by the development of sophisticated models.<n> Ensuring robust and reliable power infrastructures is fundamental to take advantage of the full potential of AI.<n>However, AI data centres are extremely hungry for power, putting the problem of their power management in the spotlight.
arXiv Detail & Related papers (2025-10-13T08:08:21Z) - ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research [53.736407871322314]
We introduce ORMind, a cognitive-inspired framework that enhances optimization through counterfactual reasoning.<n>Our approach emulates human cognition, implementing an end-to-end workflow that transforms requirements into mathematical models and executable code.<n>It is currently being tested internally in Lenovo's AI Assistant, with plans to enhance optimization capabilities for both business and consumer customers.
arXiv Detail & Related papers (2025-06-02T05:11:21Z) - An Overview of Large Language Models for Statisticians [109.38601458831545]
Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI)
This paper explores potential areas where statisticians can make important contributions to the development of LLMs.
We focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation.
arXiv Detail & Related papers (2025-02-25T03:40:36Z) - Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm [5.864623711097197]
We show how the framework of Conscious Data Contribution can enable user autonomy during under dissolution.
We explore how fine-tuning and the phenomenon of "catastrophic forgetting" could actually prove beneficial as a type of machine unlearning.
arXiv Detail & Related papers (2024-11-20T18:55:51Z) - A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges [60.546677053091685]
Large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain.
We explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation.
We highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
arXiv Detail & Related papers (2024-06-15T16:11:35Z) - A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics [46.025337523478825]
Talent analytics has emerged as a promising field in applied data science for human resource management.
Recent development of Big Data and Artificial Intelligence techniques have revolutionized human resource management.
arXiv Detail & Related papers (2023-07-03T07:53:20Z) - Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines.
We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
arXiv Detail & Related papers (2023-04-15T19:22:37Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - Future Computer Systems and Networking Research in the Netherlands: A
Manifesto [137.47124933818066]
We draw attention to CompSys as a vital part of ICT.
Each of the Top Sectors of the Dutch Economy, each route in the National Research Agenda, and each of the UN Sustainable Development Goals pose challenges that cannot be addressed without CompSys advances.
arXiv Detail & Related papers (2022-05-26T11:02:29Z) - Supporting Tool for The Transition of Existing Small and Medium
Enterprises Towards Industry 4.0 [0.0]
The main purpose of this work is to propose a methodology to support SMEs managers in better understanding the specific requirements for the implementation of Industry 4.0 solutions.
A proposed methodology will be helpful for SMEs manager to take a decision regarding when and how to migrate to Industry 4.0.
arXiv Detail & Related papers (2020-10-15T15:57:23Z) - AI-based Modeling and Data-driven Evaluation for Smart Manufacturing
Processes [56.65379135797867]
We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes.
We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
arXiv Detail & Related papers (2020-08-29T14:57:53Z) - From Data to Knowledge to Action: A Global Enabler for the 21st Century [26.32590947516587]
A confluence of advances in the computer and mathematical sciences has unleashed unprecedented capabilities for enabling true evidence-based decision making.
These capabilities are making possible the large-scale capture of data and the transformation of that data into insights and recommendations.
The shift of commerce, science, education, art, and entertainment to the web makes available unprecedented quantities of structured and unstructured databases about human activities.
arXiv Detail & Related papers (2020-07-31T19:19:42Z) - Trends of digitalization and adoption of big data & analytics among UK
SMEs: Analysis and lessons drawn from a case study of 53 SMEs [3.3808950966890903]
Small and Medium Enterprises (SMEs) now generate digital data at an unprecedented rate.
Data can be transformed into monetary value if put into a proper data value chain.
This requires both skills and IT investments for the long-term benefit of businesses.
arXiv Detail & Related papers (2020-02-14T09:51:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.