Shift Difficult Problems Left with Graph Analysis on Streaming Data

Some problems are hard to solve. Catching advanced persistent threats; acting on key problems in device systems within microseconds; calculating complex risk in seconds rather than hours or days—these are all vexing challenges. Graph data representation excels at catching anomalies, understanding categories, finding patterns and relationships; but graph databases are notoriously slow. What if you could interpret multiple data streams coming in as a graph and do deep graph analysis on data before it ever lands?

Register for this episode of InsideAnalysis to learn how a unique technology, streaming graph, can solve some of the most significant challenges in real-time data analysis. Host @eric_kavanagh will interview Paige Roberts, Director of Product Innovation at thatDot, who will explain the value of shifting tough analysis to earlier in the process. She’ll discuss how a graph analysis of flowing data can benefit your business.

Key business applications include:

• Cybersecurity and APT Detection: Identify anomalous patterns in network traffic, user logins and device logs to proactively prevent security breaches with no time window limitations.

• IoT Edge Smart Filtering: Monitor sensor data streams from industrial and other devices, understand which data is useful so you don’t flood downstream systems, and act immediately when problems arise.

• Real-time Risk analysis and Fraud Detection: Uncover intricate fraudulent activity patterns across transactions, user behaviors, and device data that traditional rules-based systems would miss.