# What About the Data? A Mapping Study on Data Engineering for AI Systems

arXiv: https://arxiv.org/abs/2402.05156 · HTML: https://mlsanity.com/paper/2402.05156/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [Adoption and Effects of Software Engineering Best Practices in Machine Learning](https://mlsanity.com/paper/2007.14130/) (2020)
- [The Principles of Data-Centric AI (DCAI)](https://mlsanity.com/paper/2211.14611/) (2022)
- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [On the experiences of adopting automated data validation in an industrial machine learning project](https://mlsanity.com/paper/2103.04095/) (2021)
- [Data Smells in Public Datasets](https://mlsanity.com/paper/2203.08007/) (2022)
- [An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context](https://mlsanity.com/paper/2204.12781/) (2022)
- [Data Smells: Categories, Causes and Consequences, and Detection of Suspicious Data in AI-based Systems](https://mlsanity.com/paper/2203.10384/) (2022)
- [A Compositional Approach to Creating Architecture Frameworks with an Application to Distributed AI Systems](https://mlsanity.com/paper/2212.13570/) (2022)
- [Data Engineering for HPC with Python](https://mlsanity.com/paper/2010.06312/) (2020)
- [Scalable Modular Synthetic Data Generation for Advancing Aerial Autonomy](https://mlsanity.com/paper/2211.05335/) (2022)

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

- [Transduction is All You Need for Structured Data Workflows](https://mlsanity.com/paper/2508.15610/) (2025)
- [WORKSWORLD: A Domain for Integrated Numeric Planning and Scheduling of Distributed Pipelined Workflows](https://mlsanity.com/paper/2603.12214/) (2026)

## Abstract (arXiv)

AI systems cannot exist without data. Now that AI models (data science and AI) have matured and are readily available to apply in practice, most organizations struggle with the data infrastructure to do so. There is a growing need for data engineers that know how to prepare data for AI systems or that can setup enterprise-wide data architectures for analytical projects. But until now, the data engineering part of AI engineering has not been getting much attention, in favor of discussing the modeling part. In this paper we aim to change this by perform a mapping study on data engineering for AI systems, i.e., AI data engineering. We found 25 relevant papers between January 2019 and June 2023, explaining AI data engineering activities. We identify which life cycle phases are covered, which technical solutions or architectures are proposed and which lessons learned are presented. We end by an overall discussion of the papers with implications for practitioners and researchers. This paper creates an overview of the body of knowledge on data engineering for AI. This overview is useful for practitioners to identify solutions and best practices as well as for researchers to identify gaps.