# Data Excellence for AI: Why Should You Care

arXiv: https://arxiv.org/abs/2111.10391 · HTML: https://mlsanity.com/paper/2111.10391/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Built on by (most cited first, 14 of 14)

- [Data-centric Artificial Intelligence: A Survey](https://mlsanity.com/paper/2303.10158/) (2023)
- [DataPerf: Benchmarks for Data-Centric AI Development](https://mlsanity.com/paper/2207.10062/) (2022)
- [The Principles of Data-Centric AI (DCAI)](https://mlsanity.com/paper/2211.14611/) (2022)
- [Better, Not Just More: Data-Centric Machine Learning for Earth Observation](https://mlsanity.com/paper/2312.05327/) (2023)
- [Measuring Annotator Agreement Generally across Complex Structured, Multi-object, and Free-text Annotation Tasks](https://mlsanity.com/paper/2212.09503/) (2022)
- [A Bag-of-Prototypes Representation for Dataset-Level Applications](https://mlsanity.com/paper/2303.13251/) (2023)
- [Striving for data-model efficiency: Identifying data externalities on group performance](https://mlsanity.com/paper/2211.06348/) (2022)
- [Improving Label Error Detection and Elimination with Uncertainty Quantification](https://mlsanity.com/paper/2405.09602/) (2024)
- [Deep Learning methodology for the identification of wood species using high-resolution macroscopic images](https://mlsanity.com/paper/2406.11772/) (2024)
- [Core-Set Selection for Data-efficient Land Cover Segmentation](https://mlsanity.com/paper/2505.01225/) (2025)
- [Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutions](https://mlsanity.com/paper/2510.16207/) (2025)
- [Class-Adaptive Sampling Policy for Efficient Continual Learning](https://mlsanity.com/paper/2311.16485/) (2023)
- [Continual Learning: Less Forgetting, More OOD Generalization via Adaptive Contrastive Replay](https://mlsanity.com/paper/2410.07110/) (2024)
- [What's the next frontier for Data-centric AI? Data Savvy Agents](https://mlsanity.com/paper/2511.01015/) (2025)

## Abstract (arXiv)

The efficacy of machine learning (ML) models depends on both algorithms and data. Training data defines what we want our models to learn, and testing data provides the means by which their empirical progress is measured. Benchmark datasets define the entire world within which models exist and operate, yet research continues to focus on critiquing and improving the algorithmic aspect of the models rather than critiquing and improving the data with which our models operate. If "data is the new oil," we are still missing work on the refineries by which the data itself could be optimized for more effective use.