This major multinational financial services institution is one of the largest banks in the world and serves over 16 million clients with over 700 products and more than 80,000 employees worldwide.

With the increasing complexity from their growing hybrid cloud environments and networks, their IT organization has actively introduced initiatives around big data, machine learning and collaborative technologies to streamline and optimize their incident management processes and workflows.

 

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“Moogsoft is about streamlining our workflow through advanced event correlation”

– Director, Systems Management

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Key Challenges

The bank’s IT organization has roughly 250 operators working across IT support, with additional teams working across their infrastructure, network and application stacks. Millions of events from their monitoring tools were sent to their legacy event management system. Operators became overwhelmed with operational noise, over-time, and suffered from lack of incident awareness as events were manually analyzed and correlated across their environments.

According to a Manager of Alarm and Event Management Systems, “Operational noise and lack of event correlation meant our teams had to manually analyze and prioritize incidents, this often led to duplicate tickets”.

These challenges meant operators were often reactive to incident detection and resolution as it would sometimes take hours to manually piece together and join the various dots.

IT leadership decided they needed to modernize their event management processes and start streamlining their workflow. It was at this point they decided to evaluate the AIOps solution from Moogsoft.

“Unlike the other tools, we saw that Moogsoft didn’t take a cookie-cutter approach. It massively reduced noise and provided context using real-time machine learning
algorithms across our big data event feeds”.

Moogsoft AIOps Solution

Today, the bank has completely decommissioned their legacy event manager with Moogsoft, ingesting over six million events across a dozen tools each month and delivering over 50% reduction in operational noise and actionable alarms for operators.

To date, the bank has experienced a 35% reduction in Mean-Time-To-Detect (MTTD) and a 43% reduction in Mean-Time-To-Restore (MTTR) with Moogsoft.