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.

在工业化 4.0 时代，人工智能（AI）在工业系统中扮演着越来越重要的角色。然而，实际的 AI 应用并没有像人们所认为的那样发展得那么成熟，其中一个主要原因是 AI 实施中的数据问题。为了解决这些数据问题，首先需要明确这些问题。因此，这项研究进行了一项元评述，探索工业 AI 实施中的数据问题和方法。总共鉴定了 72 个数据问题，并按照数据生命周期的各个阶段进行分类，包括数据源和收集、数据访问和存储、数据集成和互操作、数据预处理、数据处理、数据安全和隐私以及 AI 技术采用。随后，研究分析了各种 AI 算法的数据需求。在这些分析的基础上，提出了一个数据管理框架，解决了如何在数据生命周期的每个阶段系统地解决数据问题的问题。最后，研究指出了未来的研究方向。通过这样做，本研究丰富了现有的知识体系，并为在工业 AI 中实现数据可用性和价值性提供了指导。