# Data Readiness Levels

arXiv: https://arxiv.org/abs/1705.02245 · HTML: https://mlsanity.com/paper/1705.02245/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2017.

## Built on by (most cited first, 13 of 13)

- [Data Readiness for AI: A 360-Degree Survey](https://mlsanity.com/paper/2404.05779/) (2024)
- [Artificial Intelligence Should Genuinely Support Clinical Reasoning and Decision Making To Bridge the Translational Gap](https://mlsanity.com/paper/2506.05030/) (2025)
- [Faking feature importance: A cautionary tale on the use of differentially-private synthetic data](https://mlsanity.com/paper/2203.01363/) (2022)
- [Towards better data discovery and collection with flow-based programming](https://mlsanity.com/paper/2108.04105/) (2021)
- [Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects](https://mlsanity.com/paper/2409.03805/) (2024)
- [Automated Data Readiness for Scientific AI](https://mlsanity.com/paper/2607.02771/) (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)
- [The 6-Ds of Creating AI-Enabled Systems](https://mlsanity.com/paper/2202.03172/) (2022)
- [How to Data in Datathons](https://mlsanity.com/paper/2309.09770/) (2023)
- [AI Application Operations -- A Socio-Technical Framework for Data-driven Organizations](https://mlsanity.com/paper/2601.06061/) (2025)
- [SetGo: Metadata Readiness for Scientific AI Datasets](https://mlsanity.com/paper/2607.22677/) (2026)
- [Which Buildings Are Artificial Intelligence-Ready? A Measurement-Based Assessment Framework for AI Question Answering and Actuation](https://mlsanity.com/paper/2610.09119/) (2026)

## Abstract (arXiv)

Application of models to data is fraught. Data-generating collaborators often only have a very basic understanding of the complications of collating, processing and curating data. Challenges include: poor data collection practices, missing values, inconvenient storage mechanisms, intellectual property, security and privacy. All these aspects obstruct the sharing and interconnection of data, and the eventual interpretation of data through machine learning or other approaches. In project reporting, a major challenge is in encapsulating these problems and enabling goals to be built around the processing of data. Project overruns can occur due to failure to account for the amount of time required to curate and collate. But to understand these failures we need to have a common language for assessing the readiness of a particular data set. This position paper proposes the use of data readiness levels: it gives a rough outline of three stages of data preparedness and speculates on how formalisation of these levels into a common language for data readiness could facilitate project management.