Data Science: a Natural Ecosystem
Emilio Porcu, Roy El Moukari, Laurent Najman, Francisco Herrera, Horst Simon
TL;DR
The paper addresses the need for a cohesive, mission-driven framework that spans disciplines and diverse data contexts. It introduces Essential Data Science ($5$D) and the data-life-cycle ($DLC$) to model data challenges, agents, and tasks, tying missions to concrete atomized activities. Its key contributions are a formal fusion model for $5$D×$DLC$ interactions and an agent-based architecture that coordinates human and algorithmic agents within a pan-data science ecosystem. Through a running Urban Heat & Health example, the work demonstrates how missions translate into KPIs, tasks, and equitable, calibrated analyses, with implications for governance and reproducibility across domains.
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.