  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.
