AI is revolutionizing the way materials and processes are optimized across laboratories and industries, highlighting the importance of high-quality data for the development of robust AI models. In experimental research, the importance of high-quality data is recognized. However, there exists a significant knowledge gap in data management practices for AI applications. These practices form the backbone of high-quality research, ensuring that the data collected, analyzed, and reported is accurate, reliable, and reproducible. In the SmartFab project, we are addressing the rapidly growing demands of the Finnish photonics industry. We are collaborating with five partners from industry and academia to enhance the manufacturing quality of photonic components and systems through AI-guided experimentation. In the first stage, we focus on data digitalization strategies. To this end, we have designed questionnaires to gain insights into the manufacturing processes and objectives of optimization, thereby collecting valuable information and assisting our partners in establishing proper data management strategies. Our aim is to leverage this information to develop data digitalization best practices guidelines for industrial partners. In this way, we intend to promote the uptake of data science and AI in industrial manufacturing.