DEEP 360 MONITORING™

Built for Autonomous Business Monitoring

Deep 360 monitoring™ is engineered to monitor business metrics comprehensively and intelligently at enterprise scale

No more blind spots. No more “dark data.”

Deep 360 monitoring™ technology leverages AI to both learn the behavior of every single metric in HD quality and map the network of correlations between the metrics in the data. Deep 360 then mines the stream of incoming data to rapidly identify and score anomalies. Backed by four patents, Deep 360 ensures fast and accurate monitoring of the organization’s most revenue-critical metrics.

Get visibility into 100% of your data

Deep 360 seamlessly aggregates inputs from storage, databases, analytics, monitoring, APIs and SDKs, CRM and data streams into one centralized analytics platform to analyze 100% of data streams and metrics, regardless of the business’s original data architecture and silos. Starting with your machine, application, and business data at its most granular level, Anodot’s unique technology engineers an HD view of your data aggregation layers and top line KPIs.

Autonomous learning of metric behavior

Deep 360 leverages advanced AI and ML to learn the unique behavior of every metric and its weekly, monthly and annual seasonality—in real time and at scale, using Anodot’s patented Vivaldi Method. Every metric that comes in goes through a classification phase, and is matched with the optimal model from a library of model types for different signal types. Modified Holt-Winters, ARIMA and other algorithms are used for the sequential adaptive learning that initializes a model of what is normal on the fly, and then computes the relation of each new data point going forward.

Comprehensive metric & events correlation

Correlation is crucial for understanding metrics in context. Anodot uses a patented combination of four derivatives of behavioral topology learning: abnormal correlation, naming correlation, graph correlation, and implicit analytics topology. Scale is achieved through algorithmic metric partitioning and grouping, which enables to maintain rapid run time at any scale, without increasing computational costs.

Alert scoring and false positive reduction

At Anodot, false positive reduction is our main KPI. Every alert counts. Alerts are scored according to deviation, duration, frequency, and related conditions. Anodot’s patented anomaly scoring method runs probabilistic Bayesian models to evaluate the anomaly delta both relative to normal, and relative to each other. Statistical models—such as ratios between metrics and influencing metrics—group and correlate different metrics in order to analyze them according to the specific business context. Feedback from end users is collected for each alert instance to further improve the system’s ML brain.