As applications become richer and support more functionality, information architectures and navigational mechanisms become more complex. Search is commonly viewed as a way to navigate through vast amounts of content, but learning search capabilities can be difficult and some users are more inclined to click than search. By merging search and navigation into one seamless construct, click based navigation is retained, while a more efficient search based way to access content is also available.

Additionally, through conventional click based interaction with the system, users can see their actions reflect through the search bar and thus learn its capabilities. In that way click based navigation isn’t just a crutch for people who aren’t inclined to search, it also provides a natural ramp for users of all types to understand, learn, and adopt search behavior.
This concept of learning was tested and effectively validated during several phases of user research during the course of the project.

The utility of a search based experience extends beyond navigational capabilities.Such a system also affords ranking and the ability to highlight content based on the users’ current context. Suggested content can be driven by technologies like machine learning and become richer over time as such technologies mature.In that way, we can further reduce the interaction burden on the user, by surfacing critical information directly or even automatically resolving issues on the user’s behalf.

As Enterprise software evolves, new models of interaction are needed to keep up with the challenges of complexity and scalability. Such models should enable users to quickly get the information they want, while also providing a framework to suggest information that they may not realize is important. The latter is become much more prominent as new technologies, such as machine learning and anomaly detection, mature and become more prevalent. The platform we’ve designed does all of these things, in a beautiful and delightful way.