  This paper delves into the contrasting roles of data within academic and
industrial spheres, highlighting the divergence between Data-Centric AI and
Model-Agnostic AI approaches. We argue that while Data-Centric AI focuses on
the primacy of high-quality data for model performance, Model-Agnostic AI
prioritizes algorithmic flexibility, often at the expense of data quality
considerations. This distinction reveals that academic standards for data
quality frequently do not meet the rigorous demands of industrial applications,
leading to potential pitfalls in deploying academic models in real-world
settings. Through a comprehensive analysis, we address these disparities,
presenting both the challenges they pose and strategies for bridging the gap.
Furthermore, we propose a novel paradigm: Model-Based Data-Centric AI, which
aims to reconcile these differences by integrating model considerations into
data optimization processes. This approach underscores the necessity for
evolving data requirements that are sensitive to the nuances of both academic
research and industrial deployment. By exploring these discrepancies, we aim to
foster a more nuanced understanding of data's role in AI development and
encourage a convergence of academic and industrial standards to enhance AI's
real-world applicability.
