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

TL;DR

This paper conducts a comprehensive meta-review of data issues in industrial AI, categorizing 72 problems across the data lifecycle, analyzing AI data requirements, and proposing a systematic data management framework to enhance AI implementation in industry.

Contribution

It provides the first extensive categorization of data issues in industrial AI and proposes a structured framework to address these challenges systematically.

Findings

72 data issues identified and categorized across data lifecycle stages

Analysis of AI algorithm data requirements

Proposed a data management framework for industrial AI

Abstract

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.