Anodot Introduces Anomaly Detection Tool


Tool uses machine learning on time-series data; aimed at the ad tech, e-commerce, IoT, and manufacturing industries.

Real-time analytics company Anodot announced last week that their automated anomaly detection tool has exited stealth and is now available. Aimed at ad tech, IoT, manufacturing and e-commerce verticals, the tool uses machine-learning algorithms to detect problems and find opportunities with time-series data.

“There is a huge opportunity to disrupt the BI market by enabling automated and real-time insights into big data pools of metrics and KPIs,” said Tal Barnoach, Anodot board member.

The product flags anomalies in data and assesses how “off” the data is, and for how long, and provides alerts and visualizations.

The goal of the tool is to reduce or eliminate data analysis lag from batch processing by generating real-time insights that companies can use for instant decisions. The tool is already being used by companies like Wix, a website development company, and Avantis, an advertising technology company.

Anodot, based in Israel, also announced Dec. 9 that they received $3 million in Series A funding, bringing total funding to $4.5 million.


Want more? Check out our most-read content:

Research from Gartner: Real-Time Analytics with the Internet of Things
The Value of Bringing Analytics to the Edge
Frontiers in Artificial Intelligence for the IoT: White Paper

Data Visualization: How a Futures Exchange Sees Clearly
John Bates, Plat.One: Enterprise IoT Doesn’t Have to Be Hard
Why Edge Computing Is Crucial for the IoT
Why Gateways and Controllers Are Critical for IoT Architecture

Liked this article? Share it with your colleagues!

Sue Walsh

About Sue Walsh

Sue Walsh is News Writer for RTInsights, and a freelance writer and social media manager living in New York City. Her specialties include tech, security and e-commerce. You can follow her on Twitter at @girlfridaygeek.

Leave a Reply

Your email address will not be published. Required fields are marked *