# Data Readiness Report

arXiv: https://arxiv.org/abs/2010.07213 · HTML: https://mlsanity.com/paper/2010.07213/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development](https://mlsanity.com/paper/2108.04308/) (2021)
- [Navigating Dataset Documentations in AI: A Large-Scale Analysis of Dataset Cards on Hugging Face](https://mlsanity.com/paper/2401.13822/) (2024)
- [Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets](https://mlsanity.com/paper/2108.05935/) (2021)
- [Reward Reports for Reinforcement Learning](https://mlsanity.com/paper/2204.10817/) (2022)
- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Data Readiness for AI: A 360-Degree Survey](https://mlsanity.com/paper/2404.05779/) (2024)
- [A domain-specific language for describing machine learning datasets](https://mlsanity.com/paper/2207.02848/) (2022)
- [Completeness of Datasets Documentation on ML/AI repositories: an Empirical Investigation](https://mlsanity.com/paper/2503.13463/) (2025)
- [TechOps: Technical Documentation Templates for the AI Act](https://mlsanity.com/paper/2508.08804/) (2025)
- [From Reflection to Repair: A Scoping Review of Dataset Documentation Tools](https://mlsanity.com/paper/2602.15968/) (2026)
- [Hypothesis Testing for Class-Conditional Label Noise](https://mlsanity.com/paper/2103.02630/) (2021)
- [We Need to Talk About Data: The Importance of Data Readiness in Natural Language Processing](https://mlsanity.com/paper/2110.05464/) (2021)
- [How to Data in Datathons](https://mlsanity.com/paper/2309.09770/) (2023)
- [Documentation Practices of Artificial Intelligence](https://mlsanity.com/paper/2406.18620/) (2024)
- [Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models](https://mlsanity.com/paper/2502.08669/) (2025)
- [CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2505.23849/) (2025)
- [AI Application Operations -- A Socio-Technical Framework for Data-driven Organizations](https://mlsanity.com/paper/2601.06061/) (2025)

## Abstract (arXiv)

Data exploration and quality analysis is an important yet tedious process in the AI pipeline. Current practices of data cleaning and data readiness assessment for machine learning tasks are mostly conducted in an arbitrary manner which limits their reuse and results in loss of productivity. We introduce the concept of a Data Readiness Report as an accompanying documentation to a dataset that allows data consumers to get detailed insights into the quality of input data. Data characteristics and challenges on various quality dimensions are identified and documented keeping in mind the principles of transparency and explainability. The Data Readiness Report also serves as a record of all data assessment operations including applied transformations. This provides a detailed lineage for the purpose of data governance and management. In effect, the report captures and documents the actions taken by various personas in a data readiness and assessment workflow. Overtime this becomes a repository of best practices and can potentially drive a recommendation system for building automated data readiness workflows on the lines of AutoML [8]. We anticipate that together with the Datasheets [9], Dataset Nutrition Label [11], FactSheets [1] and Model Cards [15], the Data Readiness Report makes significant progress towards Data and AI lifecycle documentation.