Design: Data-Informed Design

Over the past decade, more and more organizations have been doing their best to become data driven. This is a huge and much-awaited mindset leap—especially given corporate dinosaurs’ typical way of thinking: “I’m the HiPPO (Highest Paid Person in the Office) and my gut tells me this is what the customer needs.”

User research is one of the most powerful antidotes to this outdated mindset and is becoming part of the vocabulary and practice of an increasing number of organizations. It also signifies an increase in UX maturity, so is a very promising trend!

The Problem with Data Being in the Driver’s Seat

Making decisions based on data reduces risk and increases your potential for creating useful outcomes. However, if you make decisions based on bad data, you risk making a similar—or perhaps an even worse—kind of gamble or bet as when making decisions based on untested assumptions. 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