Small Data Explainer -- The impact of small data methods in everyday life

TL;DR

This paper explores the significance of small data methods in everyday life, emphasizing their societal and health applications, contrasting them with big data, and outlining technical approaches and future research directions.

Contribution

It provides a comprehensive overview of small data techniques, contrasting them with big data, and proposes an agenda for leveraging small data across various disciplines and applications.

Findings

Small data can address societal issues like inclusion and health.

Current techniques from statistics and computer science are effective for small data.

A future research agenda is proposed for fully leveraging small data.

Abstract

The emergence of breakthrough artificial intelligence (AI) techniques has led to a renewed focus on how small data settings, i.e., settings with limited information, can benefit from such developments. This includes societal issues such as how best to include under-represented groups in data-driven policy and decision making, or the health benefits of assistive technologies such as wearables. We provide a conceptual overview, in particular contrasting small data with big data, and identify common themes from exemplary case studies and application areas. Potential solutions are described in a more detailed technical overview of current data analysis and modelling techniques, highlighting contributions from different disciplines, such as knowledge-driven modelling from statistics and data-driven modelling from computer science. By linking application settings, conceptual contributions and specific techniques, we highlight what is already feasible and suggest what an agenda for fully leveraging small data might look like.