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Extracting useful information from business data used to mean waiting for the IT department to run reports. Increasingly though there's demand for users to be able to extract information themselves.

The latest company to join this trend is Israel-based Anodot which is aiming to disrupt the static nature of today's BI using patented machine learning algorithms for big data.

Anodot's Anomaly Detection solution is data agnostic and automates the discovery of outlying events in all business and operational data. The platform isolates issues and correlates them across multiple parameters to find and alert on incidents in real time.

"I experienced the data analysis lag problem first hand as CTO for Gett", says Anodot's founder and CEO David Drai. "As a mobile taxi app, SMS text orders were dropped by the carrier but it could take up to three days to spot critical issues and fix them, costing tens of thousands of dollars per incident. That's where I got the idea for Anodot -- to employ the latest advances in machine learning to detect performance problems automatically and in real time, eliminating the latency".

Features of Anodot Anomaly Detection include real time operation, and the ability to work with any type of metric or KPI and scale to any big data volume. Using proprietary patented machine learning algorithms it correlates different metrics to help identify the root causes of problems and eliminate alert overload.

Its simulation capability optimizes alert planning and reduces false positive alerts as well as cutting out the need for time-intensive manual analysis. As a result non-specialists can gain the insights they want with clear visualizations that help any user to understand what the data is showing them.

The company has received $3 million of venture capital funding to accelerate its product roadmap and expand its sales activity, focusing on the ad tech, e-commerce, IoT and manufacturing industries in the US and EMEA. You can find out more and request a trial of the software on the Anodot website.

Image credit: David Gaylor / Shutterstock

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