Exploring The Future of Data-Driven Product Design

Abstract

Connected devices present new opportunities to advance design through data collection in the wild, similar to the way digital services evolve through analytics. However, it is still unclear how live data transmitted by connected devices informs the design of these products, going beyond performance optimisation to support creative practices. Design can be enriched by data captured by connected devices, from usage logs to environmental sensors, and data about the devices and people around them. Through a series of workshops, this paper contributes industry and academia perspectives on the future of data-driven product design. We highlight HCI challenges, issues and implications, including sensemaking and the generation of design insight. We further challenge current notions of data-driven design and envision ways in which future HCI research can develop ways to work with data in the design process in a connected, rich, human manner.

Data-Driven Design Challenges in the Early Stages of the Product Development Process

“…The need to integrate and collaborate with data scientist and IT experts was also discussed. Finally, Gorkovenko et al (2020) also explored the future of Data-Driven Product Design through a few workshops. A lack of supports within product design to process and make sense out of data was identified.…”

The Impact of Data on the Role of Designers and Their Process

“…However, for this role to be effective, designers need to be knowledgeable and equipped with technical jargon, besides having communication and empathy skills. This essential 'partnership' with data scientist is highlighted by Girardin and Lathia (2017) and Gorkovenko et al (2020).…”

Towards Designerly Data Donation

“…First, machine data collection can be burdensome. It is often expensive, and time-consuming, given that requires scalable sensorization (i.e., building data-driven probes and prototypes) and close collaboration with computer scientist or data scientist [Dove et al 2017;Gorkovenko et al 2020], and it is subject to regulatory frameworks such as the GDPR that might complicate and restrict it [Bourgeois et al 2018;Gorkovenko et al 2020]. Second, people might be reluctant to share machine data, often containing highly personal information, since it could expose them to privacy and security violations [Gabriele and Chiasson 2020;Goodman 2014].…”

“…Second, people might be reluctant to share machine data, often containing highly personal information, since it could expose them to privacy and security violations [Gabriele and Chiasson 2020;Goodman 2014]. Third, machine data is often decontextualized, and lacks the rich and meaningful details that make it valuable for designerly contexts [Bornakke and Due 2018;Gorkovenko et al 2020]. Facing these challenges is critical to enabling a future where people have meaningful physical and digital experiences by design.…”

“…Since contextualization, depicting the nuances of the data (e.g., what, who, where, how, why), currently requires intimate interventions, which are often not scalable [Bogers et al 2016;Bourgeois et al 2014a;Chang et al 2017]. As a result, design researchers and practitioners face several challenges which turn these opportunities into utopian visions [Dove et al 2017;Gorkovenko et al 2020]. In addition, ethical implications emerge as the data is purposefully intimate: the aim is to understand people's behaviours in-depth to surface subtle but critical insights.…”