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Anomaly Detection

How to use anomaly detection and machine learning together

Anomaly detection can be essential to identifying potential incidents using data—including fraud detection, intrusion and security alerts, manufacturing quality control, and medical diagnostics. Flexible and powerful, anomaly detection is an important part of the analyses you’ll need to track and optimize business operations.

Gain Business Value With Big Data AI Analytics

“Data-driven” is the latest buzzword in organizations in which data-based decision making is directly connected to business success. According to Gartner’s Hype Cycle, more than 77% of the C-suite now say data science is critical to their organization meeting strategic objectives. For top organizations looking to adopt a data-driven culture to stay competitive, what does that mean?

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.