Mist is a WLAN platform that combines AI, machine learning, and data science with the latest cloud technologies to deliver innovative scalable solutions that optimize the wireless experience and brings insight into the network.
UX Lead & Visual Designer
Senior UX Designer
3 Months - 2019
I transformed the research into insights and collabrated with the UX Lead in defining the framework. My sole responsibility was to create wireframes and interactions while presenting to the project team.
Mist System wanted to distinguish themselves from their competitors. They had the technology. They had data. They wanted to offer their customers a unique experience but how?
We began our research with a competitive analysis for an overview of the product landscape. We discovered all six competitors provided high-level health dashboards but lack the discernment necessary for effective actions.
Figure 1: Competitor Dashboards
Qualitative research showed our primary users, network administrators, felt frustrated with monitoring dashboards. They knew when a problem occurred but were challenged in identifying what, where, and how to resolve it.
In order to be effective in their job, network administrator needed a system that would dynamically:
Pinpoint the network problems,
Show where the problems are occurring within the system,
Communicate the impact,
Demonstrate the cause and,
Tell them how to fix it.
As a result, our design theme became "Root Cause Into Human Actions". Our strategy was to utilize Mist’s AI technology to expose problems and effectively demonstrate where the problem lies, what causes it, who is impacted, and how can it be resolved.
The UX lead and I decided it would be useful to include a "Latest Update" feed so teams are aware of new, resolved, and closed problems effortlessly. We broke the screen into 2 main components - Latest Updates and Actions at an organizational or Site level.
Figure 2: Framework
Latest update of new, resolved, and closed problems.
List of problems with the date of occurrence, scope, effect, and recommended action.
The initial design was a good start where we had the right content. Further iteration was needed to improve upon the scalability of the design and become visually disparate from its competitors. We blocked off our calendar and set up multiple workshops with the project team, engineer, and data scientist to progress the design to the next level up.
After numerous iterations, our final solution was to keep Marvis Action clean by presenting a simple visualization of the system's health. Each number indicated the number of actions to be taken per entity.
Network engineers have the option to address the most critical issue to the very left or a different entity.
We also provided different views (Summary and Site) based on users' needs and preferences. The Summary offered a consolidated view the same actions to be taken. The Site view a rendition of the geographical location where the problems occurred.
Figure 3: Summary
Figure 3: Site (based on specific store location)
Mist did a soft launch of Marvis Action in August 2019 and have received positive feedback for its unique design and sophisticated AI intelligence. Customers are asking when the product can be installed into their systems.
I am proud of the product we delivered. Marvis Action is a game-changer because it transforms the root cause analysis into actionable solutions. My key takeaways are:
Conduct competitive analysis to identify a gap in the industry and validate the need through user research.
Learn the data early on the project to understand its complexity and scope for scalability.
Collaborate frequently and often with engineers and data scientists to incorporate their expertise into the product.