“There is a story in your data. But your tools don’t know what that story is.” – Cole Nussbaumer
The dashboard framework we usually used to design screens is designed to be informative and descriptive. We are generally dealing with precise numbers and so it fits that need.
Unfortunately this was a complicated and abstract problem. Claims are machines that collect baggage and sort them out into conveyor belts after flights land at their destination. From there, bags are sorted out and delivered to thousands of people a day. We were dealing with 28 claims. So how do you convey the information of thousands of bags split into 28 groups, and show their journeys per claim with the added element of a filterable time-frame? All at the same time on the same screen.
Transfer Analysis Before
Having the usual amount of information would drown out our dashboards and make the screens unusable. So I had to drown out the noise. Legends and numbers were stripped and the target was normalized as an anchor. This was a macro level view and so the elements that were crucial were now intrusive. Leaving just the essence of the data that was needed on the screen made it clear to see where and when a claim is in trouble. A screen that is easy to scan makes it easy to anticipate surges in traffic, and in turn leads to much more efficient airports since problems can be addressed before they happen.
Transfer Analysis After