# How big is Big Data?

arXiv: https://arxiv.org/abs/2405.11404 · HTML: https://mlsanity.com/paper/2405.11404/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 18 May 2024.

## Built on by (most cited first, 2 of 2)

- [Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms](https://mlsanity.com/paper/2502.00944/) (2025)
- [Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets](https://mlsanity.com/paper/2606.29975/) (2026)

## Abstract (arXiv)

Big data has ushered in a new wave of predictive power using machine learning models. In this work, we assess what it big means in the context of typical materials-science machine-learning problems. This concerns not only data volume, but also data quality and veracity as much as infrastructure issues. With selected examples, we ask (i) how models generalize to similar datasets, (ii) how high-quality datasets can be gathered from heterogenous sources, (iii) how the feature set and complexity of a model can affect expressivity, and (iv) what infrastructure requirements are needed to create larger datasets and train models on them. In sum, we find that big data present unique challenges along very different aspects that should serve to motivate further work.