Zero-Shot Transfer Learning for Cross-Industry BI Models

Abstract

Most Business Intelligence (BI) models are very industry specific and are thus hard to be reused in industries with divergent data models, business priorities and decision-making context. In this research article, Zero-Shot Transfer Learning of Cross-Industry BI Models, the authors discuss the application of the concept of zero-shot transfer learning that would allow BI systems to transfer knowledge in one field to another without the enormous volumes of labelled data in the target field. The suggested framework incorporates a trained base model, feature embedding without domain knowledge, semantic metadata embedding, prompt-based task-adaptation and cross-industry validity. The information in the source domain BI of retail, healthcare, financial, manufacturing and logistics is translated into a generic semantic representation in this model. The model then extends patterns learned to the unobservable scenarios in the industry based on the contextual embeddings and alignment of business ontology. This aids the system to implement the novel functions of demand forecasting, customer segmentation, risk prediction, anomaly detection and KPI recommendation with minimal retraining. The article finds the potential of zero-shot learning to reduce the cost of the model development, increase its scalability, and provide faster decision-making in a dynamic business environment. It also addresses important problems of domain shift, data heterogeneity, model interpretability, transfer of bias and trust of automated BI recommendations. The article concludes that zero-shot transfer learning can be an effective method to create flexible, reusable and intelligent BI models that can be used to cross-industry analytics. Future studies ought to concentrate on clarifiable transfer schemes, industry-specific assessment parameters, and the safe incorporation with enterprise BI systems.