When there's too much data to monitor manually, automated anomaly detection is a MUST for preventing data quality issues.

Because opportunities can open and close very quickly on social media, your anomaly detection needs to track and correlate data in real time.

A precursor to our three-part series “The Ultimate Guide to Anomaly Detection”, which introduces the high-level and technical concepts to monitoring time series data.

In this webinar, we’ll cover everything you ever wanted to know about time series anomaly detection, and how you can make it work for you.

Learn more about the different types of outliers and why knowing these is key for correct outlier analysis.

Learn how Xandr, AT&T’s advanced advertising company, prevents revenue loss and customer churn across its large-scale network, finding incidents early by using the Anodot Autonomous Business Monitoring platform built on Amazon Web Services (AWS).

What is customer experience monitoring? Why is it so difficult? Dive into this webinar with Customer Success Engineer, Steven Kirkpatrick, to better understand what it means to effectively monitor your user’s experience from start to finish.

One of the problems with statistical outliers is that they can be difficult to detect within the context of time series data. In this post, we’ll discuss how we can tune automated detection systems so that they become integral when it comes to identifying outliers across thousands to billions of metrics.

Telcos are under tremendous pressure to reduce churn and increase ARPU. Break free from manual tracking and time-consuming root cause analysis: Anodot’s AI-driven monitoring is dynamic and adaptive enough to easily accommodate all your network complexities. Customer Success Specialist Daniel Tub shows how leading telcos use Anodot to autonomously monitor their OSS and BSS.