Design: Data-Informed Design

The amount of data we produce every day is growing exponentially. This explosion of raw data means synthesis, analysis, and interpretation are more important than ever before. Without the right processes and tools in place to understand and act on our data, it has little value. It is essential that we understand what data is available, how it can answer pressing questions, and how it can enable action.

As UX professionals, we collect a wide array of data through a variety of sources and techniques—from market trends to one-on-one interviews to product-usage data to usability testing to sales feedback. We must collate, classify, and comprehend disparate sources of data to create a more holistic understanding of whatever question we need to answer. While the volume of data might seem overwhelming at first, design thinking and a tinkering mindset are invaluable in helping to break down the problem, define a plan of action, and iterate and refine solutions as necessary to turn the raw data into actionable insights and concrete products. Read More

When we think of analytics, we think of marketing campaigns and funnel optimization. Analytics can seem a little overwhelming, with so many charts and lots of new features. How can we use analytics for design insights?

The best thing about analytics is that they can show us what people do on their own. The worst thing is that analytics don’t tell us much about context, motivations, and intent. Like any kind of data, there are limitations. But that doesn’t mean analytics aren’t useful. Working with analytics is about knowing where to look and learning which questions you can reasonably ask. Read More

Envision coffee machines that start brewing just when you think it’s a good time for an espresso, office lights that dim when it’s sunny and workers don’t need them, your favorite music app playing a magical tune depending on your mood, or your car suggesting an alternative route when you hit a traffic jam.

Predictability is the essence of a sustainable business model. In a digital world, with millions of users across the globe, prediction definitely has the power to drive the future of interaction. Feeding a historical dataset into a system that uses machine-learning algorithms to predict outcomes makes prediction possible. Read More