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Full stack observability increases cloud ROI for SolarWinds customer

A case study by Enterprise Management Associates (EMA) for SolarWinds shows how enterprise cloud operations can multiply returns on investment (ROI) if they get full observability of the software stack.

The published EMA document followed an integrated SolarWinds implementation with a stated goal of modernising and streamlining the operational toolset of an unnamed global enterprise software vendor with about12 SaaS products. The customer also has various on-prem datacentres and works with multiple cloud providers.

“By adopting and extending their investment in this integrated SolarWinds solution a few years ago, this large SaaS company has avoided $1.7 million in one-time capital expenses, saved at least $1.8m annually on operational expenses, and earned $5.4m based on a $1.1m investment in SolarWinds three years ago,” the EMA document said.

The SolarWinds customer’s cloud operations organisation adopted a full-stack observability suite to manage its infrastructure — including network performance monitoring; server, virtual and application monitoring, configuration management, and database/storage management.

“The observability solution streamlined performance management, capacity planning, and operational reporting, conservatively earning more than five times the ROI over three years and breaking even in less than two years,” according to the EMA document.

Full-stack observability helped the customer manage 250 administrators and engineers in multiple teams as well as supporting multiple SaaS offerings, following growth through several mergers and acquisitions.

“Over time, individual teams adopted several different products to manage and monitor infrastructure and services, leading to a fragmented toolset causing operational inefficiencies, from service availability to resource planning,” according to the EMA document.

Previously, the software business had several monitoring tools for different resources, some offering more granularity than others. Data disparities and inaccuracies affected root cause analysis, the EMA document said.

“In addition to disparate data sets, these fragmented tools often had conflicting time stamps. A tool owned by a west coast team would time-stamp the data in one time zone,and a tool owned by an east coast team would time-stamp data in another time zone,” it said.

“Reconciling these conflicts slowed down operations.”

Read the full case study on the SolarWinds website.

( Image by Gerd Altmann from Pixabay )

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