# Data Issues in Industrial AI System: A Meta-Review and Research Strategy

arXiv: https://arxiv.org/abs/2406.15784 · HTML: https://mlsanity.com/paper/2406.15784/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 22 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing](https://mlsanity.com/paper/2007.14791/) (2020)
- [Industrial Artificial Intelligence](https://mlsanity.com/paper/1908.02150/) (2019)
- [Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Open Source Data from PHM Data Challenges: A Review](https://mlsanity.com/paper/2312.16810/) (2023)

## Built on by (most cited first, 4 of 4)

- [Engineering Artificial Intelligence: Framework, Challenges, and Future Direction](https://mlsanity.com/paper/2504.02269/) (2025)
- [Is PMBOK Guide the Right Fit for AI? Re-evaluating Project Management in the Face of Artificial Intelligence Projects](https://mlsanity.com/paper/2506.02214/) (2025)
- [The record is part of the task: matched-record evaluation of text classifiers across maintenance, safety and recall reporting](https://mlsanity.com/paper/2609.16267/) (2026)
- [Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos](https://mlsanity.com/paper/2609.33754/) (2026)

## Abstract (arXiv)

In the era of Industry 4.0, artificial intelligence (AI) is assuming an increasingly pivotal role within industrial systems. Despite the recent trend within various industries to adopt AI, the actual adoption of AI is not as developed as perceived. A significant factor contributing to this lag is the data issues in AI implementation. How to address these data issues stands as a significant concern confronting both industry and academia. To address data issues, the first step involves mapping out these issues. Therefore, this study conducts a meta-review to explore data issues and methods within the implementation of industrial AI. Seventy-two data issues are identified and categorized into various stages of the data lifecycle, including data source and collection, data access and storage, data integration and interoperation, data pre-processing, data processing, data security and privacy, and AI technology adoption. Subsequently, the study analyzes the data requirements of various AI algorithms. Building on the aforementioned analyses, it proposes a data management framework, addressing how data issues can be systematically resolved at every stage of the data lifecycle. Finally, the study highlights future research directions. In doing so, this study enriches the existing body of knowledge and provides guidelines for professionals navigating the complex landscape of achieving data usability and usefulness in industrial AI.