What is data-led design?
Recently I participated in a community workshop and discussion around ‘data-driven’ service design. These are my notes and thoughts from the evening.
Event DETAILS (from eventbrite):
Within our human-centred design practices the notion of deeply understanding people through interviews, observation, journey maps and other ethnographic approaches to research dominate, but we increasingly see the use of quantitative data alongside or in place of ethnographic methods. What are the implications of adopting a data-driven process? What is ‘data’? As designers, how are we equipped to perform and interpret different types of data? Is the future of design driven by data, as recently declared by Portable? What does it look like to use quantitative data within an often qualitative process? How might human-centred design approaches amplify quantitative data methods? How might we make the most of data in our work? How might we integrate quantitative and qualitative information?
Rather than providing answers about how to use or not use a data-driven approach to design work, the evening will be an opportunity to discuss, explore and experiment with how we use data to define the services and people for whom and with we are designing. If you are someone who is interested in designing with data, whether you are a pro or questioning it every step of the way, please join us.
What is data-driven design?
Working at the intersection of data science & design:
Questions for discussion (From a design perspective):
How do we define data?
What’s our experience working with data and design?
What does it mean to be data-driven?
Data can validate intuition and qualitative insights.
Data can be a compass, direction not distance.
Data can be a foundation for products and services.
What do we value about quantitative and qualitative approaches?
What’s the relationship between quantitative data, digital data, and big data? How in our work are they treated the same and differently?
What purpose would we like to see big, digital, and quantitative data serve?
How do we choose methods for our research tasks? What informs these decisions? How do we combine different methods in productive ways
Quantitative / Behavioural: A/B testing, data tracking, statistical modelling
Quantitative / Attitudinal: Surveys
Qualitative / Behavioural: Usability testing
Qualitative / Attitudinal: User interviews, diary study