The rise of industrial systems driven by recent advances in engineering and data science has significantly changed the landscape of manufacturing and production. Equipped with modern tools and capabilities, industrial systems can streamline processes, enhance production, analyze complex scenarios, and support decision-making. Central to these systems is industrial data, which provides the insights and means necessary to drive operations and achieve production objectives. Given its critical value, protecting industrial data from potential risks is essential for ensuring consistency, utility, and competitiveness. While various studies have focused on security factors, the literature addressing industrial data privacy remains limited. Recognizing this gap and the importance of both industrial systems and data privacy, this study thoroughly explores these topics. First, industrial systems are examined, highlighting their prevalent types and establishing a foundation for understanding their distinctive features. Next, 34 selected studies on industrial data privacy are reviewed, discussing its significance, current challenges, and potential solutions. The study identifies 10 common types of industrial systems and their shared characteristics. Additionally, it presents 15 definitions and contexts, proposing an inclusive definition that aligns with modern industrial systems. The study also defines industrial data and identifies eight contexts associated with industrial data privacy, providing a comprehensive review of each. Finally, it highlights and recommends a range of solutions including operational and technical means for protecting industrial data. Overall, the findings underscore the pressing need to prioritize industrial data privacy and address it more closely in both research and practice.