Anomaly Detection

Why anomaly detection is important

Anomaly detection does not have the same gravitas as big data buzzwords like machine learning, advanced analytics, and decision support systems, all of which are becoming household names in the business world. But anomaly detection is just as relevant, if not more important, to a thriving business intelligence system, and ultimately, the bottom line. What is anomaly detection?

Real-Time Anomaly Detection: Solving Problems and Finding Opportunities

Success in today’s high-velocity business environments means having the correct information to make the right decisions at the right time. As marketplaces grow more competitive and customer expectations continually rise, the “right time” is often real-time. Every transaction generates a plethora of data. Anomalies within your company’s data set can represent opportunities and threats to the business.

Preventing Shopping Cart Abandonment with Anomaly Detection

The global pandemic has changed B2C markets in many ways. In the U.S. market alone in 2020, consumers spent more than $860 billion with online retailers, driving up sales by 44% over the previous year.eCommerce sales are likely to remain high long after the pandemic subsides, as people have grown accustomed to the convenience of ordering online and having their goods – even groceries – delivered to their door.

What's new with BigQuery ML: Unsupervised anomaly detection for time series and non-time series data

When it comes to anomaly detection, one of the key challenges that many organizations face is that it can be difficult to know how to define what an anomaly is. How do you define and anticipate unusual network intrusions, manufacturing defects, or insurance fraud? If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML.

Bridge the gap in your OSS by adding an AI brain on top

Telecom companies monitor their network using a variety of monitoring tools. There are separate fault management and performance management platforms for different areas of the network (core, RAN, etc.), and infrastructure is monitored separately. Although these solutions monitor network functions and logic – something that would seem to make sense — in practice this strategy fails to produce accurate and effective monitoring or reduce time to detection of service experience issues.

Correlation Analysis Explained

When you detect that something is off in your business, how long does it take you to find the root cause? The longer it takes, the more it can cost you. Correlation analysis identifies relationships between KPIs, which business teams use to accelerate root cause analysis (RCA) and mean time to remediation (MTTR). Doing it manually however can be tedious and limit your visibility.

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.