Software Sales Veteran Matt Cillis Joins Instana as Chief Revenue Officer
Former Splunk and CloudBees Executive Joins Leadership Team
Instana, the leading provider of automated observability solutions for cloud-native microservice applications, announced today that software industry veteran Matt Cillis has joined its executive leadership team as Chief Revenue Officer (CRO).
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Cillis has over a decade of experience working with leading software organizations as a revenue focused executive. In his new role as CRO at Instana, he will oversee the strategy and execution of revenue and customer operations for the organization.
“I’m thrilled to welcome Matt to our leadership team as Chief Revenue Officer,” said Mirko Novakovic, Instana co-founder and CEO. “With steady growth and demand for our enterprise observability platform, Instana will lean on Matt’s sales and field operations leadership experience to help guide continued revenue growth.”
Prior to joining Instana, Cillis served as Vice President of Sales at CloudBees. Before that, he was Director of Sales for Splunk focusing on major accounts.
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“I’ve seen Instana radically change the way organizations approach application performance and observability,” said Matt Cillis, Instana CRO. “I look forward to being a part of such an exciting company and building on the momentum Instana has gathered for itself.”
Instana’s Enterprise Observability Platform with automated Application Performance Monitoring (APM) solution discovers all application service components and application infrastructure, including multi-cloud infrastructure such as Google Cloud Platform, AWS and IBM / Red Hat Marketplace, orchestration like Kubernetes, OpenShift and Docker, adding visibility in both application performance and the CI/CD pipeline.
Instana automatically deploys monitoring sensors for each part of the application technology stack, traces all application requests and profiles every process – without requiring any human configuration or application restarts. Application and infrastructure changes are detected in real-time, with adjustments to both the internal model and visualizations – application, infrastructure and service maps – identifying changes and any performance impact in seconds.
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