Future products are increasingly built around live data from the Internet and the Internet of Things. Data and algorithms become a critical material for informing the design and development of future products that are relevant, useful and appropriate, and deliver real value and impact. However, designers lack the ability to effectively use this emerging material as part of the product design and development process to inform, drive and assess. Here we unpack three design opportunities and five challenging qualities at the core of Data-Centric Design.
3 opportunities for design processes
Data and machine learning algorithms offer a new perspective to better understand contextual environment. One from things that can continuously observe and detect patterns as well as unexpected events. Complementing the designers’ tool box, this perspective enriched the understanding and generate additional lead for designers to explore.
The ‘smart product’ industry is dominated by technology pushes. Facilitating the access of those technologies to designers is key to put them back in control of the design process. It is essential to deliver responsible products that support people needs and values.
The product design field remains dominated by a throw-over-the-fence model in which designers release their products and move on to the next without assessing its impact. Data and machine learning algorithms are critical material and tools to enable never-ending design processes.
5 challenging qualities
The Accessibility Challenge
Current data and data analysis are geared towards data scientists – there is a lack of data and data analysis tools for designers that integrate into the design process.
How can designers effectively collect, store and visualise human activity data?
The Intelligence Challenge
Design approaches and machine learning sciences are misaligned. Data science start with a defined problem and a strategy to extract answers out of the data. Informing design with data requires an exploration of the space to uncover relevant issues from the data .
How can designers prototype intelligence with and for human data?
The Ethical Challenge
Data and machine learning algorithms raise serious ethical and legal concerns and designers need to be able to understand and address these concerns in their designs – yet there is a lack of practical knowledge for designers about data and algorithms ethics.
How can designers leverage human activity data while protecting the right to privacy and promoting the right to science?
The Co-design Challenge
Although ‘Big data’ is a powerful tool to understand what is happening in a contextual environment, it is very limited when it comes to understand why it is happening.
How can designers co-design engage data subjects effectively?
The Scalability Challenge
The extraction of meaningful insights with data and machine learning algorithms relies on large quantity of data, beyond the typical scale of the product design and development process.
How can designers implement a DCD process at scalable?