This week, I had the pleasure of speaking at Strata, an O’Reilly Data Science conference in London. The session I was in looked at the good, the bad and the ugly of using data and machine learning.
Data has opened up huge possibilities for designing, analysing and customising products and services. We can now create experiences that dynamically target audiences and respond immediately. However, what we see is that context is often missing in these experiences, leading to a knowledge gap that will only increase over time.
User expectations have changed drastically in the past years. This is mainly down to services that are driven by data, whether location, social graph or utilising information we provide to give us contextual help and recommendations. We are a long, long way from the traditional linear interaction with a product or service. Journeys are now a collection of moments occurring across devices and platforms, and data gives us the chance to design across all of these experiences.
However, data/design, quant/qual can no longer work in our comfortable silos. Without qualitative and human understanding of the world, data can never reach its full potential. To fully understand not only the context of the information we can see, but also the implications of what we do with that data we need to combine these two skill sets.
In my talk, I walked through examples and case studies that illustrate how we might combine these two skill sets to create truly personalised responsive services.