First Look – Netuitive

Netuitive provides predictive analytics for IT. Based in Reston VA and founded in 2002 they have over 50 large enterprise customers and 300 more through OEMs. Their solution is designed to prevent degradations and outages to critical applications and services by providing an intelligence layer on top of existing monitoring systems.

Netuitive provides predictive analytics for IT. Based in Reston VA and founded in 2002 they have over 50 large enterprise customers and 300 more through OEMs. Their solution is designed to prevent degradations and outages to critical applications and services by providing an intelligence layer on top of existing monitoring systems. Companies use the software for critical systems where either there is revenue dependent on the system being available or where keeping the system up is critical for a company’s reputation (like online banking for a bank for instance).

These critical systems involve lots of pieces. Each is monitored separately and these monitoring systems and key performance indicators are not connected. In addition there is just a lot of data. Netuitive has a patented behavior learning engine that consumes outputs from the applications, physical systems and virtual infrastructure and uses predictive analytic approaches to create an application health score. It can consume data from any source, any monitoring system, and correlate it

These health scores are presented on dashboards or in alerting systems. They can also generate “trusted alarms” that show that things are heading in the wrong direction or getting out of bounds relative to what would be expected. The scores come with diagnostic information and alarms are very focused, targeted at specific people who can address the problem and provided with root cause analysis as part of the alarm. These trusted triggers could drive system behavior too, reconfiguring to move a VM for instance though most users are not using the software in this way yet.

Netuitive claims to have algorithms and approaches that allow them to do this kind of thing without lots of processing power. They can plug in lots of systems and the software will learn about the performance dependencies in those systems. In addition the user of the system can connect the pieces to show which bits contribute to which business capability.

An interesting way of applying predictive analytics to some of IT’s own problems, this also illustrates the power of self-learning or machine-learning in situations with a lot of complexity.