# Big Data and AI-Driven Product Design: A Survey
## 4 Product Design Based on Big Data and AI
### Summary of Opportunities  
Big data and AI algorithms hold enormous potential in modern product design. There are two types of data: structured and unstructured. Structured data offer strong pertinence and high-value density, but they are limited in openness and volume. Obtaining large-scale structured data is challenging for individuals, and even enterprises--it requires substantial workforce and resources for manual collection, sorting, summarizing, and storage in standard databases. On the other hand, unstructured data enjoy high openness and volume compared to structured data. It has great advantages in terms of veracity, velocity, value, and variety, all may strongly contribute to modern product design.  
We have detailed the common data types in the product lifecycle, including text, image, audio, and video. Among these, text and image data are widely used in product design due to their ease of acquisition, mature processing algorithms, and high data quality. Textual data can overcome the limitations of traditional survey methods, such as small sample size, limited survey scope, and high labor-intensive, while also guaranteeing data authenticity, reliability, and timeliness. Image data, on the other hand, is useful for surveys and excels in visual inspiration and schemes display. The color, texture, sharp information in images can inspire designers to create preliminary schemes. Furthermore, generative models can generate new product designs from image data with varying colors, structures, textures, etc. AIGC is a revolutionary advancement in product design, which was previously unattainable through traditional methods.  
Video data are also a visual type of information, which have high value for user behavior observation and product virtual display. Compared to images, video can provide customers with a comprehensive understanding and allowing them to interact with products virtually. However, both video and audio data are still in their early stages, with many technological and data quality challenges to overcome before they can be fully utilized in product design. Despite the development of technologies for audio and video processing, few researchers have employed them in product design. However, these limited studies warrant a discussion to motivate further research in the field.  
Big data have brought about numerous benefits for product design; however, current research still has its limitations and requires further investigation. There are two research directions that are especially promising: (i) the fusion of different types of data. Existing research is confined to only one type of data (i.e., text, image, video, audio, and location), despite these data types co-existing at various stages of the product lifecycle. Furthermore, multi-modal data fusion could enhance the persuasiveness and efficacy of the extracted information; (ii) The synergistic exploitation of big data techniques and design domain knowledge. While previous efforts have focused on technological breakthroughs, especially for data lacking advanced processing technologies (e.g., audio and video), domain knowledge in the field of product design should be ignored. The knowledge accumulated in traditional product design is precious and helpful. Big data and traditional product design methods are not contradictory but complementary. Only by combining them can we better capture user requirements and improve the success rate of design. By addressingthese research directions, we can further unlock the potential of big data and AI-driven product design, leading to more intelligent, personalized, and successful products across various industries.