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In February 2025, NIST researcher Dr. M. Sharp published the second installment of a 4-part Manufacturing Extension Partnership (MEP) manufacturing innovation blog series that provides a beginner’s guide to Industrial Artificial Intelligence (IAI) applications. The current blog posting focuses on the data characteristics needed for AI to add measurable value to manufacturing operations. The author notes that it is important to understand what data, assumptions, rules, and shortcuts feed into an IAI system during its training, testing, development, and deployment stages. Various AI data topics are covered, including the need for available data to match real-world conditions and represent the full scope of the desired use cases. Common data pitfalls are also identified, including incomplete data, inadequate data variation, and large gaps in data. For additional background and context, the first blog in the series described IAI and provided simple questions to ask when investing in and using AI-enhanced systems and tools in an industrial application.