Facing & mitigating common challenges when working with real-world data: The Data Learning Paradigm

Abstract

The rapid growth of data-driven applications is ubiquitous across virtually all scientific domains, and has led to an increasing demand for effective methods to handle data deficiencies and mitigate the effects of imperfect data. This paper presents a guide for researchers encountering real-world data-driven applications, and the respective challenges associated with this. This article proposes the concept of the Data Learning Paradigm, combining the principles of machine learning, data science and data assimilation to tackle real-world challenges in data-driven applications. Models are a product of the data upon which they are trained, and no data collected from real world scenarios is perfect due to natural limitations of sensing and collection. Thus, computational modelling of real world systems is intrinsically limited by the various deficiencies encountered in real data. The Data Learning Paradigm aims to leverage the strengths of data improvement to enhance the accuracy, reliability, and interpretability of data-driven models. We outline a range of methods which are currently being implemented in the field of Data Learning involving machine learning and data science methods, and discuss how these mitigate the various problems associated with data-driven models, illustrating improved results in a multitude of real world applications. We highlight examples where these methods have led to significant advancements in fields such as environmental monitoring, planetary exploration, healthcare analytics, linguistic analysis, social networks, and smart manufacturing. We offer a guide to how these methods may be implemented to deal with general types of limitations in data, alongside their current and potential applications.