  With the improvement of living standards, user requirements of modern
products are becoming increasingly more diversified and personalized.
Traditional product design methods can no longer satisfy the market needs due
to their strong subjectivity, small survey scope, poor real-time data, and lack
of visual display, which calls for the development of big data driven product
design methodology. Big data in the product lifecycle contains valuable
information for guiding product design, such as customer preferences, market
demands, product evaluation, and visual display: online product reviews reflect
customer evaluations and requirements; product images contain information of
shape,color, and texture which can inspire designers to get initial design
schemes more quickly or even directly generate new product images. How to
efficiently collect product design related data and exploit them effectively
during the whole product design process is thus critical to modern product
design. This paper aims to conduct a comprehensive survey on big data driven
product design. It will help researchers and practitioners to comprehend the
latest development of relevant studies and applications centered on how big
data can be processed, analyzed, and exploited in aiding product design. We
first introduce several representative traditional product design methods and
highlight their limitations. Then we discuss current and potential applications
of textual data, image data, audio data, and video data in product design
cycles. Finally, major deficiencies of existing data driven product design
studies and future research directions are summarized. We believe that this
study can draw increasing attention to modern data driven product design.
