Modernising complex social network information in a visual, quickly readable format
Our client conducts investigations, gathers intelligence and provides security advice across the globe. They operate within the cyber-intelligence sector, providing intelligence for businesses of all scales.
Part of their offering includes analysis on an individual’s social media activity and network. Their existing ways of working were slow, with a high workload, unable to scale. They wanted to rectify this by organising information in a fully automated way enabling the software to scale to larger networks.
Discover the current ways of working of the clients cyber intelligence professionals
Facilitate usability testing rounds
Low to high fidelity prototype
Data visualisation of complex information
Dramatically sped up cyber intelligence reports for time critical events
By interviewing multiple stakeholders within the intelligence agency I found the application they were using centred around manual linking and input of information into a non-visual format. The client was dealing with complex information, often with 500+ social media profiles for a single report, the cyber intelligence professionals would often struggle to understand a full network and visualise who was connected to who.
Creating clear groups of connections using intelligent clustering
Customer interviews showed how critical it was to understand a full social media network, understanding who is connected to who, where clusters of connections appear in a subjects life and identifying outliers. I conceptualised how these clusters should appear and behave and worked closely with the engineering team to implement clustering algorithms which create clear groups of entities based on connections, location, employer, and education. Another aspect unearthed in the user interviews was to streamline the clients ways of working, complex networks the cyber intelligence professional is able to filter people by number of connections, allowing their analysts to focus on individuals with a low number of connections and clearly see if those people are connected to each other.
Critical information is displayed where needed, with users able to quickly navigate between connections to understand groupings who is connected to who across multiple social platforms which could easily be missed if only looking at one platform.
Understand and highlight outliers
Each profile is analysed against a series of properties, presenting how the profile ranks against the average, enables users to quickly recognise outliers of interest.
To understand an individuals network in a snapshot, they are able to focus on specific characteristics such as locations, occupations and education to understand the the basics of a network in a glance.
