Anomaly Detection


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.

Using Elastic machine learning rare analysis to hunt for the unusual

It is incredibly useful to be able to identify the most unusual data in your Elasticsearch indices. However, it can be incredibly difficult to manually find unusual content if you are collecting large volumes of data. Fortunately, Elastic machine learning can be used to easily build a model of your data and apply anomaly detection algorithms to detect what is rare/unusual in the data. And with machine learning, the larger the dataset, the better.


Only Autonomous Anomaly Detection Scales

Say you’re looking for a smart product to detect anomalies in your organization’s IT environment. A sales rep drops by and shows you all kinds of great artificial intelligence (AI) features with fancy-sounding algorithms. It sounds very impressive and seems like there is a lot of very valuable AI in the product. But, in fact, the opposite is true. This is a manual AI product wrapped in a deceiving jacket. Let me tell you more.


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.

My best growth experiment: Nitesh Sharoff, growth marketing & analytics consultant

The rise and rise of data analytics has brought with it superior digital experiences, personalisation and a golden era of hyper-growth companies, driven to success by a constantly-experimental mindset. We’re speaking to digital experts about the most effective growth experiments they’ve conducted and their tricks for improving customer experience, boosting ROI and increasing revenue, to help teams develop a more experimental approach to business growth.

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.

Metric Matters: Nitesh Sharoff, growth marketing & analytics consultant

Data analytics is critical for any businesses hoping to be more competitive today. After all, data-driven organisations outperform their non data-driven peers to such a remarkable extent, they’re 23x more likely to acquire customers. From improved efficiency to better financial performance, there are countless benefits to using your data correctly.