After the flood: Design using data

The brief

Working closely with data design agency After the flood, we created an opinion piece for design site Digital Arts, revealing how the team brings clarity to complex datasets.

The story

There’s a deluge of data in the world. Doctors need to explain complex results to patients. Sports scientists optimise player performance in real time. Airline operators diagnose delays and communicate that information to passengers. These operations require explanation: finding the patterns in the data, extracting insights from the actions, and presenting them with clarity to the people who are not experts.

This is where designers step in. Designers today are working with more data, more channels and a wider breadth of user expertise. They’re data mediators, responsible for taking that complex data, digesting it, and outputting clear, unambiguous information that anyone can easily understand.

However, there’s a perceptible problem in the interpretation of this data. A dominative narrative is emerging of visually complex ‘data-vis’ infographics bursting with exciting visuals, but containing little meat in terms of real insight.

When designing with data, what is actually required is a considered process behind the scenes – long before visuals are even considered. Designers need to understand the end user, their needs, the business, and how it is distinct. Only then will the story within the data – and its uses as a building material – start to become apparent.

So – once you’ve gathered this data, what do you do? For starters, don’t make the mistake of showing all of the data that you have. Designers enjoy complexity and visual chicanery: to create something complex is often to create something of value in the eyes of their peers. But this is far from the right way to treat data projects.

This doesn’t mean just to focus on simplicity, but to wring out meaning from the data available. The answer to ‘what is the most valuable thing we can show?’ is the designer’s responsibility. The designer must borrow from the storyteller’s rulebook – show, don’t tell.

80% of the time, the client will make the wrong assumption regarding what the users need to see or what the data needs to do. They may overvalue technical features, but chances are these will leave the general viewer confounded. Instead, the data designer must get to the heart of the problem that needs solving – and then decide whether the data available can achieve that goal.

Good designers have always done this. It’s just that the design materials have updated.