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

The field of artificial intelligence (AI) is increasingly focusing on the utility of 'small data' settings, which involve limited information, to address societal challenges. This research provides a conceptual overview, clarifying small data's relationship with big data, and identifies common themes across various case studies and application areas. It also details current data analysis and modelling techniques from diverse disciplines that offer potential solutions for leveraging small data.

Why it matters

The ability to derive insights and make informed decisions from limited datasets (small data) is crucial for addressing specific, often localized, challenges and ensuring equitable representation. This approach can unlock strategic value in situations where large datasets are unavailable or insufficient, particularly for marginalized populations or nascent technologies, thus enabling more tailored and effective interventions.

Key insights

• Breakthrough AI techniques are driving a renewed focus on 'small data' settings, characterized by limited information.
• Small data methods are relevant to societal issues, including the inclusion of under-represented groups in data-driven policy and decision-making.
• Assistive technologies for health benefits are identified as a key application area for small data.
• The analysis clarifies the conceptual relationship between small data and big data.
• The research identifies common themes from exemplary case studies and application areas of small data.
• Current data analysis and modelling techniques are described, highlighting contributions from various disciplines.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2507.11773