Data Science: a Natural Ecosystem

TL;DR

This paper presents a systemic, data-centric view of data science as a natural ecosystem, emphasizing the integration of data complexities, agent roles, and interdisciplinary missions within a formal architecture.

Contribution

It introduces a formal ecosystem model of data science, defining discipline-specific and pan-data science, and proposes a fusion-oriented architecture for integrating heterogeneous knowledge and workflows.

Findings

Formal ecosystem model of data science

Definition of discipline-induced and pan-data science

A general-purpose architecture for heterogeneous knowledge integration

Abstract

This manuscript provides a systemic and data-centric view of what we term essential data science, as a natural ecosystem with challenges and missions stemming from the fusion of data universe with its multiple combinations of the 5D complexities (data structure, domain, cardinality, causality, and ethics) with the phases of the data life cycle. Data agents perform tasks driven by specific goals. The data scientist is an abstract entity that comes from the logical organization of data agents with their actions. Data scientists face challenges that are defined according to the missions. We define specific discipline-induced data science, which in turn allows for the definition of pan-data science, a natural ecosystem that integrates specific disciplines with the essential data science. We semantically split the essential data science into computational, and foundational. By formalizing this ecosystemic view, we contribute a general-purpose, fusion-oriented architecture for integrating heterogeneous knowledge, agents, and workflows-relevant to a wide range of disciplines and high-impact applications.