Predicting the implications of strategies, ideas, and solutions for the implementation of creative ideas is a key step in the creative process. In this research line, a better understanding of how prediction occurs as part of the creative process is achieved in order to design new tools that make use of big data and machine learning to enhance the creative process. First, it is proposed that predicting the potential of early ideas based on previous experiences is a requirement for effective idea generation, without which the search space would be too large to parse to find solutions to the complex and ill-defined problems for which creative design is necessary. A first study has confirmed that prediction is pervasive in the idea generation process. Second, it is proposed that prediction is needed after idea generation to forecast and decide what ideas can be implemented, require revision, or should be dropped altogether. A first study has been conducted that shows how prediction constraints during forecasting activity affect uncertainty about and the quality of idea evaluation. While work continues on the fundamental underpinnings of the relationships between idea generation, evaluation and prediction, these findings are now translated into new models of how big data and machine learning affect and should be used in the creative design process.