Architects are constantly facing design challenges that have to be solved efficiently and effectively not only in terms of spatial design but of social behavior and business strategy. Bio-inspired principles fueled by sets of data collected by users could predict patterns of necessity and trends, ultimately aiding on informed decision making. Imitating nature has become a recurring approach for contemporary architects, basing design on biological structures that minimize their efforts for specific outcomes, enabled by their improvement over the evolutionary process. A data driven process can create much more personalized user experiences and identify the essential aspects of a project, visually and functionally, transforming the way space is designed, built and used through widely available and accessible information and emerging types engagement. This dissertation will define and exemplify, through a set of five projects, the different nomenclatures and functions of bio-inspired design, using decision making techniques such as meta-heuristic optimization algorithms, visual comparison and environmental simulation; Introduce public data collection and its use in architecture, and finally result in a case study on a Feasibility Study platform that uses non-linear, bio-inspired algorithms, directed by user-generated data, to generate building typologies and inform potential development locations.