bIG data visualisation enhanced with machine learning.
40% Capacity reduction
Already a successful big data visualization platform, mosaicOA from CJC is now enhanced with machine learning-led analytics, delivering a 40% capacity reduction in CJC environments.
Firms across the capital markets spectrum typically have multiple bespoke and specialist IT infrastructures distributing real time data, both internally and to global counterparts. These are critical to running their market operations and disruption comes at a high cost.
MosaicOA, a SaaS-based big data visualisation platform from CJC is designed to support IT Operations Analytics and deliver insight via visualisations, analytics and reports from clients’ mission critical IT infrastructures.
A typical capital market server can easily create 50 million statistics every day – from CPU, networking and application level. The data is recorded in real-time from the entire infrastructure and then presented to the client via an interactive front end. CJC needed a way of identifying the metrics that were important and allowing the end user to visualise that information in an actionable fashion.
CJC wished to offer advanced machine led analytics, particularly in highlighting metrics which did not typically correlate. They also wanted the final presentation to be actionable, so users could immediately see and understand system behaviour. The team needed specific expertise to address the project and structure their database to deliver the enhanced insight. They called on The Data Analysis Bureau (T-DAB) to provide that expertise.
T-DAB used Principle Component Analysis (PCA) to identify co-linearity in infrastructure metrics collected so we could reduce the number of dimensions required to represent the metrics. The metrics with the highest feature importance were then normalised from their base units of measure into, a value between -100 (under-utilised) to +100 (over-utilised). This meant the metrics could then be combined into a Performance index, that can be easily visualised.
The use of Principle Component Analysis (PCA) in mosaicOA enabled T-DAB to simplify the complex interrelation between different metrics which allowed our clients to more comprehensively realise the predictive power of IT Operations Analytics.
T-DAB helped CJC structure the software development process into two-week sprints and the tools for configuration management were built in a central location to reduce the support overhead. MosaicOA uses a continuous integration server with automated testing that allows not only automated regression tests for releases, but also automated stress tests to help define specific use-case versions of the offering.
The engagement enables CJC to focus their business investment to data, improve their data properties and pipeline and improve their understanding of infrastructure performance.
Our approach allows CJC clients to quickly identify the most used (and most underutilised) servers. This allows them to proactively manage resources and make cost savings on their infrastructure. CJC used these tools on their own environment and were able to find a 40% capacity reduction, which they are using to drive adoption with other clients.