Global bank uses Netuitive predictive analytics

Please introduce yourself and your organization?

I'm a vice president of virtualization management for one of the ten largest banks in the world with more than 2 trillion under management. We are managing more than 100,000 virtual desktops.

What were you doing that needed this type of technology?

Following a large acquisition, the bank embarked on a major IT initiative involving the migration and integration of distributed IT infrastructure into the banks’ virtual data center. Previously, any service degradation or downtime translated to significant business loss. Business drivers called for a private cloud service delivery model involving virtual desktop infrastructure (VDI) encompassing more than 100,000 instances of VMware powering multiple storage and network platforms.

The challenge for the bank was to understand the performance of its virtualized infrastructure (comprised of virtualization hosts, networks and storage systems) in the context of the guest operating system, middleware, and applications being supported. Cross-domain insight and exceptional IT performance of the new converged environment is the bank’s number one priority. As an early adopter of large scale virtualization, the bank understands complexity associated with a large private cloud environment supporting 100,000+ users requires an analytics-based approach to provide end-to-end visibility and mathematical certainty in application performance management. This is a precursor to making sure that the cloud environment is production-ready for mission critical applications.

What products did you consider before making a selection?

We considered all the major providers of IT analytics tools, some of which are now being monitored by Netuitive and rolling up application performance metrics into the platform.

Why did you select this product?

As part of the bank’s vision is to deliver autonomic computing to optimize the allocation of resources in real-time based on application needs, Netuitive provides the intelligence needed to fully automate IT infrastructure and provisioning for dynamic resource provisioning and right-sizing of cloud infrastructure end-to-end.

In the end, it came down to risk. As part of such a large IT migration and integration, could the bank risk not using predictive analytics to improve visibility and application performance in the newly converged IT environment? The answer was no. Only through the mathematical certainty provided by predictive analytics could the bank have the confidence to evolve its virtualization and application performance management strategies to an elastic private cloud model.

Central to the solution is Netuitive’s open, self-learning IT analytics software — an analytics layer that excels in physical, virtual and cloud environments. Netuitive’s patented Behavior Learning engine replaces manual, rules-based methods for performance monitoring with automated statistical analysis that correlates and self-learns the operational behavior of IT systems and applications. It allows the bank to resolve IT performance problems as quickly as possible by isolating root cause and, in some cases, forecast problems and prevent them from happening altogether.

What tangible benefits have you received through the use of this product?

Netuitive’s open, technology-agnostic approach delivers cross-domain insight by easily integrating with the banks home grown monitoring tools as well as the other leading industry monitoring and application performance management tools. This provides a holistic view across the bank’s platforms, vendors and users — enabling it to manage IT and application performance proactively and in real time.

This IT initiative is leading the company’s overarching goal of realizing the true promise of virtualization, application performance management and cloud computing. It affects nearly all bank employees and will also allow the banks senior executives to confidently bring users over to the new IT platform. It will also allow the enterprise computing group to start utilizing advanced features inside VMware’s product set.

What advice would you offer others facing similar circumstances?

Here's something I said when asked a similar question in the past

“Self-learning analytics is a must have for our virtualization management initiatives delivering coveted visibility across our large heterogeneous environment.”

Vice President,
Virtualization Management
Global Bank   

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