Noble | Vision – Network discovery based on Machine Learning

UI & UX design for an enterprise network monitoring and big data analytics tool

My role
My role was to be involved in this project from end-to-end, from discovery to delivery with a focus on interaction design and user research. I was involved in the following activities over the course of this project:
Discovery
Stakeholder interviews
Analyse & scope capabilities
Interaction plan
Define & Ideate
Initial workshops
Problem definition
Rough idea sketches & journey maps
Usability tests
Delivery
Concept visualisation (detailed prototypes)
Acceptance criteria creation
Facilitate developer handoff sessions
Development planning with engineering
What is Noble Vision
Noble Vision is a big-data analytics platform that visualises an organisations' network by displaying real time networking information for forensic analysis in an easy to consume manner.
Who are the users
Security analysts who use most of their time triaging alerts coming from SIEM solutions. They have experience using a wide range of security tools for carrying out forensic analysis incase of a suspected network attack.
What problems does it solve
Noble Vision uses its deep learning capabilities to analyse network traffic in realtime. It recognises anomalies in the traffic and flags the involved connections with a score (high or low) depending on the type of activity seen by the deep learning algorithm.
User Research, Problem definitions & sketches
User persona
User persona
How might we...
How might we...
Feature plan
Feature plan
Feature sketch 1
Feature sketch 1
Feature sketch 2
Feature sketch 2
Challenges
Due to the complex nature of th app and the fact that this is a first-gen concept and due to a lack of existing clients, it was hard to test the value of this functionality with real users.
Solution
The easiest way to validate the concept was to develop an MVP with fake network traffic and conduct test sessions with professional analysts. 
I was responsible for setting up remote test sessions with participants who fit our personas and conducting user tests. For this, I had to plan user research goals and questions in advance as well as user tasks for the participants.
I used User Testing for these sessions and recorded them for future analysis.

Highlights of research findings from the Remote Moderated User Testing session for the entire solution

Learnings
Working closely with the machine learning team was a great experience for me. I have great respect for the amount of complex information they weed through on a daily basis. I learned a lot about the different data visualisations they use within their team to make sense of the complex outputs they work with.
I have also learned a lot about working with the engineering team. It is very important to define a handover process and keep reflecting on how it is working for people on both sides (developers & designers) often. Regular communication and asking for updates will save a lot of future confusion and mistakes on both sides.
I also learned to create a lean canvas for deconstructing the basic idea of this feature into key assumptions. This greatly helped when I had to write up research goals for user testing. 
Next steps in the project
Due to the sensitive nature of the project I cannot reveal anymore. Please request a detailed portfolio presentation if you want to know more about my work and process.

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