5 Steps for Automating Anomaly Detection
Download this eBook about automating anomaly detection & how it can prevent expensive service disruptions before they happen.
The recent, rapid increase of time-series data has upended IT operations. With billions of gigabytes of data generated on a daily basis, it’s no longer possible to manually analyze thousands of performance metrics in real-time. Yet advances in machine learning and anomaly detection have introduced a new wave of performance analytics solutions that analyze colossal amounts of system data and recognize unusual patterns of behavior in real-time.
Anomaly detection is a form of technology that uses artificial intelligence to identify abnormal behavior within a dataset in ways that humans are unable to. Performance analytics platforms with such AI capabilities help organizations establish historical baselines, identify behavioral anomalies, and prevent expensive service disruptions before they happen.
“The goal of anomaly detection is to identify cases that are unusual within data that is seemingly homogenous, rare events that may have great significance, but are hard to find.”Oracle’s Data Mining Concepts
Data collection without restrictions within IT environments allows organizations to shift from reactive, ad-hoc processes toward proactive IT performance management. However, the data alone is not the most valuable piece of the puzzle. There must be a comprehensive data retention and visualization strategy in place in order to make critical business decisions.
Artificial intelligence and machine learning unlock the potential for IT operations teams to continuously detect anomalies and outliers in order to maximize the performance of existing systems, prevent downtimes, and uncover hidden patterns of behavior before operations are negatively impacted.
“Early AI adopters have higher profit margins and expect the performance gap with other firms to widen in the future, with 52 percent more likely to increase market share.”McKinsey Global Institute
This guide details the steps that proactive IT operations teams can take to automate anomaly detection; identify performance issues; and minimize the cost, duration, and impact of IT problems that affect employees, customers, and services. Read the full eBook today.