The CTOs guide to anomaly detection

There are few resources as abundant in modern enterprises as data. Every day organisations generate more data than they know how to process. 90% of the data in the world has been generated within the last two years. However, few optimised are in the position to make sense of these mounds of information. CTOs are in a constant struggle to find efficient processes for translating this data into insights.

To find a solution to this predicament, CTOs have started to turn to machine learning platforms with anomaly detection. This technology has the potential to scan vast datasets to find patterns and inconsistencies. Anomaly detection is one of the core technologies to gain visibility over this data.

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Anomaly detection example

After adopting an anomaly detection system Anodot co-founder and Chief Data Science Officer Ira Cohen found out first hand the monitoring potential offered by these systems. “Instead of having static dashboards,” the user has a “System that looks at all the data all the time and highlights to you what’s interesting, automatically.”

By deploying a platform that highlights interesting metrics within datasets, human users are free from the constraints of manual monitoring. In practice, this means that employees can be working on other important tasks. The result is a data monitoring strategy that fosters efficiency.

An anomaly detection example, applying efficient data monitoring is showcased clearly in the application performance monitoring space with AppDynamics.

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AppDynamics have an anomaly detection system that can supplement the traditional dashboard monitoring approach. be used alongside the dashboard monitoring experience available to users. Jean-Francois Huard co-founder of Perspica and CTO of AppDynamics states that customers “will get machine learned anomalies as events” and will be able to double click to root cause mode “where suspected root causes will be ranked for quick investigation”.

The use of such platforms offers to make the job of a CTO ten times easier. Minimising the strenuous task of monitoring datasets will lead to better insights that will drive an organisation forward. Eliminating inefficient data monitoring procedures will pay dividends down the line as resources become optimised for future use. Deploying machine learning will help organizations to work smarter rather than harder.

Network and performance monitoring and how anomaly detection is keeping enterprises secure:

Network and performance monitoring platforms using machine learning and anomaly detection have the potential to respond to threats in real-time

Written by Tim Keary, Freelance Tech Writer.

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