Data Visualization Styles & Guidelines
As part of the launch of our customer-facing business insights suite, the UX team was asked to create a dashboard for our administrative users. I suggested that the first part of that work was performing an inventory of the types of charts and graphs available in the software, so that we could make recommendations for how different types of data be displayed. I performed an audit of the chart and graphs in use, researched the best practices for them, and documented the chart and graph types while adding an updated visual presentation in-line with our current style guide. This work ended up being heavily utilized by the teams putting together the recommended data templates, as well as our internal analytics team when presenting data to the company about user behavior. My final recommendations included not only updated styles and appropriate uses of chart and graph types currently in use, but some more advanced charts and graphs that expanded the range of display possibilities. This work remains in use today by our internal teams, and in our business insight dashboards.
One of our other areas of focus was the way the screen animations enhanced the user experience: did they clarify, or confuse? Did the UI anticipate the user's next move, and guide them through the necessary steps? Our efforts were positively received by our users:
- "Wow, it's just popping through this stuff automatically...it was easier to use. It seemed to anticipate my next move, and required less tapping."
Overall, participants in the study found the idea of a "suggested trip" to be useful, usable, and desirable. When they noticed the suggested itinerary, they found it to be very useful. They easily understood their preferences and company policy was being accounted for in what was presented. They also were more likely to prefer the suggested itinerary path to the idea of booking each piece of their trip separately. Having the complete itinerary displayed did not detract from the usability of booking flights and hotels separately for those user who preferred a more traditional approach.
The only downfall we found was the reliability and accuracy of our suggested itineraries. They worked best when, obviously, the predictions were relevant to the users. These explorations have been guiding further work on our data sciences team to make our recommendations solid so that we can start presenting itineraries this way in our UI. In the meantime, other concepts that became cornerstone assumptions of this project are being divided out into other project teams and implemented so that they are already in place when we are ready to launch the idea of itineraries as search results.