Identify and alert from anomalies in your data
Anomaly detection is the process of identifying unexpected events or data points (often referred to as “outliers”) within a dataset. Typically these anomalous events will signal a problem like hacking, bank fraud, network intrusion, malfunctioning equipment, etc.
The explosion of data in the modern world has made anomaly detection incredibly difficult, and almost impossible to do in real-time. But with rising cybersecurity threats, it is more important than ever.
Cybercrime is up 600% due to the COVID-19 pandemic.
Anomaly detection is critical for businesses to identify and react to changing operational conditions. It is a central component to extracting essential business insights and maintaining core operations.
Challenges of anomaly detection include:
- Data from IoT devices, sensors, and more must be rapidly combined with historical and streaming data for real-time analysis
- Low data quality and “noise” distort data, making it difficult to spot outliers
- Environments that must be monitored are dynamically changing
Molecula’s FeatureBase for Anomaly Detection
FeatureBase is designed to deliver secure, fast, continuous access to all your data. The first and most crucial step in leveraging big data for detecting anomalies is ensuring all of the data is accessible and ready for analysis. The sheer amount of data, compliance requirements, and ever-growing collection of data in disparate silos can be overwhelming. By eliminating time-consuming and costly preaggregation, FeatureBase unlocks your data to allow for instant and continuously up-to-date analysis to detect and respond to unexpected events.
Anomaly Detection Use Cases
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