Data-informed design thinking (DIDT) is a collaborative approach to problem-solving that combines design thinking principles with data analytics. By incorporating a range of data sources, multidisciplinary teams can effectively address complex issues and develop relevant solutions that better meet the needs of end-users. The AHL project serves as a case study demonstrating how DIDT enhances student learning by helping them to ground their design solutions in evidence and data.
The project involved collaboration between students from different schools to enhance the liveability and sustainability of a residential healthcare facility for patients with dementia. The students used digital technologies to manage and integrate spatial data to better understand and articulate architectural design and the physical environment. They also used the design process as a framework to integrate vast amounts of data and translate complex information into objective outcomes.
The empathy stage of DIDT involved research to gain an understanding of the needs of the users. The data collected informed the design, visualization, resulting in better-tailored solutions that improved the overall effectiveness of the design process. Students synthesized their research findings, analysed key data to generate and evaluate potential solutions, and prototyped and tested their solutions to ensure they met the needs and expectations of users and stakeholders.
The use of data analysis facilitated problem-solving by providing valuable insights into complex problems. It grounded designs in evidence and data, justifying design decisions and fostering accountability. Additionally, the integrative learning approach helped to nurture and train a new generation of critical thinkers and doers to be ready for our ever-changing job market.
Teaching DIDT to students is imperative due to the rising demand for data-driven decision-making in today's organizations. Adopting DIDT helps students create more efficient products and equip students with crucial skills for succeeding in the modern workplace while addressing user needs.
Data extracted from performance-based design analysis simulations such as air flow and solar analysis, energy-modelling, and carbon life cycle analysis, influenced the sustainable building design. The students used integrated spatial analytical models, including computational simulations, to quantify and measure sustainable design performance in AHL.
AHL Client engagement makes this IDL setting authentic for student learning, exposing them to realities of work-life. In conclusion, DIDT is an effective approach to problem-solving and decision-making that leverages data and analytics to drive innovation.