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Anomaly Detection

Introducing: Business Impact Alerts

Anodot is the only monitoring solution built from the ground up to find and fix key business incidents, as they’re happening. As opposed to most monitoring solutions, which focus on machine and system data to track performance, Anodot also monitors the more volatile and less predictable business metrics that directly impact your company’s bottom line. Now there’s an easy way to measure the business impact of every incident.

Outlier Detection: The Different Types of Outliers

Time series anomaly detection is a tool that detects unusual behavior, whether it's hurtful or advantageous for the business. In either case, quick outlier detection and outlier analysis can enable you to adjust your course quickly, before you lose customers, revenue, or an opportunity. The first step is knowing what types of outliers you’re up against. Chief Data Scientist Ira Cohen, co-founder of Autonomous Business Monitoring platform Anodot, covers the three main categories of outliers and how you'll see them arise in a business context.

How Xandr, AT&T's Adtech Company, Prevents Revenue Loss with Autonomous Business Monitoring

Anodot CEO and Co-Founder David Drai joined Amazon Web Services and Xandr to discuss the shift to machine learning-based anomaly detection in business monitoring. Xandr Chief Technology Officer Ben John shared how their advertising marketplace is using Anodot platform to cut detection from “up to a week to less than a day”. You can watch the webinar at the link above or read on for the highlights of that talk.

Anomaly detection 101

What is anomaly detection? Anomaly detection (aka outlier analysis) is a step in data mining that identifies data points, events, and/or observations that deviate from a dataset’s normal behavior. Anomalous data can indicate critical incidents, such as a technical glitch, or potential opportunities, for instance a change in consumer behavior. Machine learning is progressively being used to automate anomaly detection.

9 Key Areas to Cover in Your Anomaly Detection RFP

Evaluating a new, unknown technology is a complicated task. Although you can articulate the goals you’re trying to achieve, you’re probably faced with multiple solutions that approach the problem in different ways and highlight varying features. To cut through the clutter, you need to figure out what questions to ask in order to evaluate which technology has the optimal capabilities to get the job done in your unique setting.

Correlation Analysis: A Natural Next Step for Anomaly Detection

Over the last decade, data collection has become a commodity. Consequently, there has been a tremendous deluge of data in every area of industry. This trend is captured by recent research, which points to growing volume of raw data and growth of market segments fueled by that data growth.

3 Reasons Why Machine Learning Anomaly Detection is Critical for eCommerce

Do you still find yourself visually monitoring dashboards for anomalies? That leaves catching revenue-related issues to chance. It’s become humanly impossible to catch incidents on streaming data. This is why many eCommerce and data-driven companies have adopted automated anomaly detection.

The Top 10 Anomalies of the Last Decade

As a company known for our anomaly detection, we know a thing or two about spotting irregularities. So as we reached the end of 2019, we couldn’t help but think back on the 2010s and the anomalies that shook the world. Once we got to listing them, it really became tough to pick just 10. Ultimately, after much debate, we ranked them based on their impact, newsworthiness and how utterly unexpected they were.

What is Anomaly detection and how to use it for Marketing

Businesses are collecting massive amounts of data as a part of their analytics pipeline. Most of the time, this data is filtered by a computer and presented in a way that a human interprets, through the analytics dashboard. That's a fantastic resource, and has no doubt been of great value to you in business decisions. However, restricting the interpretation of all that data that you've mined to humans leaves a lot of potential insights on the table.