Abstract
We are living in a world where data is touted to be the new oil and analytics is the combustion engine. Analytics or data science comprises a number of tools and techniques from the fields of statistics, machine learning and broader AI. While there is no dearth of such tools and techniques ranging from random forests to neural networks, problems most enterprises face is that of identify the most suitable tool (s) for a business problem at hand. This paper aims to address this problem by developing a novel, comprehensive framework that identifies the optimal data science tools given the ‘nature’ and ‘type’ of the business problem and the constraints on the underlying data used. This framework can be an effective device for enterprises to select the suitable data science tools and techniques to apply to their problems. The intention is to empirically test this framework in future research and develop a Natural language Processing (NLP) implementation of this prescriptive framework.
Paper Type
Poster
DOI
10.62036/ISD.2023.57
Towards a Prescriptive Framework for Selecting Suitable Artificial Intelligence Algorithms for Enterprise-Level Problems
We are living in a world where data is touted to be the new oil and analytics is the combustion engine. Analytics or data science comprises a number of tools and techniques from the fields of statistics, machine learning and broader AI. While there is no dearth of such tools and techniques ranging from random forests to neural networks, problems most enterprises face is that of identify the most suitable tool (s) for a business problem at hand. This paper aims to address this problem by developing a novel, comprehensive framework that identifies the optimal data science tools given the ‘nature’ and ‘type’ of the business problem and the constraints on the underlying data used. This framework can be an effective device for enterprises to select the suitable data science tools and techniques to apply to their problems. The intention is to empirically test this framework in future research and develop a Natural language Processing (NLP) implementation of this prescriptive framework.
Recommended Citation
Bhattacharya, P. (2023). Towards a Prescriptive Framework for Selecting Suitable Artificial Intelligence Algorithms for Enterprise-Level Problems. In A. R. da Silva, M. M. da Silva, J. Estima, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development, Organizational Aspects and Societal Trends (ISD2023 Proceedings). Lisbon, Portugal: Instituto Superior Técnico. ISBN: 978-989-33-5509-1. https://doi.org/10.62036/ISD.2023.57