Delayed business insights cost companies millions of dollars with data-centric companies, such as web-based businesses, adtech, fintech and IoT, facing particularly unique challenges. It is impossible to manually track the millions of metrics that are generated in today’s digital businesses and businesses are increasingly finding that static thresholds for seasonal data are either meaningless or cause alert-storms. Companies are often finding that dashboards can’t keep up with these sudden spikes and the data can only ever be used in hindsight.

In this webinar, Uri Maoz, former VP of U.S. Business at Anodot, discusses how predictive anomaly detection can better identify revenue-impacting business incidents in minutes, not days or weeks. He also looks at the benefits and challenges of implementing anomaly detection, sharing industry benchmarks and customer case studies. 

In this talk, Uri covers:

  • The fundamentals of Anomaly Detection, what it means and how you can get real-time business incident detection using Anomaly Detection
  • The steps one should take to implement a real-time Anomaly Detection solution in scale
  • The various business use cases from customer experience and how Anomaly Detection helped them save millions of dollars


Written by Anodot

Anodot leads in Autonomous Business Monitoring, offering real-time incident detection and innovative cloud cost management solutions with a primary focus on partnerships and MSP collaboration. Our machine learning platform not only identifies business incidents promptly but also optimizes cloud resources, reducing waste. By reducing alert noise by up to 95 percent and slashing time to detection by as much as 80 percent, Anodot has helped customers recover millions in time and revenue.

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