Data in design: How big data and thick data inform design thinking projects

Scholars and practitioners have recognized that making innovation happen today requires renewed approaches focused on agility, dynamicity, and other organizational capabilities that enable firms to cope with uncertainty and complexity. In turn, the literature has shown that design thinking is a useful methodology to cope with ill-defined and wicked problems. In this study, we address the question of the little-known role of different types of data in innovation projects characterized by ill-defined problems requiring creativity to be solved. Rooted in qualitative observation (thick data) and quantitative analyses (big data), we investigate the role of data in eight design thinking projects dealing with ill-defined and wicked problems. Our findings highlight the practical and theoretical implications of eight practices that differently make use of big and thick data, informing academics and practitioners on how different types of data are utilized in design thinking projects and the related principles and practices.

Automated summary

This study explores the role of different types of data in innovation projects characterized by ill-defined problems. Through qualitative observation and quantitative analyses, the findings highlight eight practices that utilize big and thick data in design thinking projects, providing practical and theoretical implications for academics and practitioners.