# Data Validation

arXiv: https://arxiv.org/abs/2012.12028 · HTML: https://mlsanity.com/paper/2012.12028/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

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

- [Auto-Tag: Tagging-Data-By-Example in Data Lakes](https://mlsanity.com/paper/2112.06049/) (2021)
- [Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research](https://mlsanity.com/paper/2206.14153/) (2022)

## Abstract (arXiv)

Data validation is the activity where one decides whether or not a particular data set is fit for a given purpose. Formalizing the requirements that drive this decision process allows for unambiguous communication of the requirements, automation of the decision process, and opens up ways to maintain and investigate the decision process itself. The purpose of this article is to formalize the definition of data validation and to demonstrate some of the properties that can be derived from this definition. In particular, it is shown how a formal view of the concept permits a classification of data quality requirements, allowing them to be ordered in increasing levels of complexity. Some subtleties arising from combining possibly many such requirements are pointed out as well.