The health of modern organizations depends on many complex business and operational processes that are impossible to monitor and manage manually. A disruption to these processes can cost millions of dollars and jeopardize customer satisfaction and loyalty. Until recently, few analytical tools could sift through mountains of data, detect subtle changes over time, and proactively notify users about issues that might adversely affect business outcomes. 

While monitoring systems are not new, applying them at scale to business metrics is revolutionary. Using statistics and machine learning algorithms, the systems can analyze millions of factors that affect business metrics over various time intervals. They continuously detect anomalies, trends, and correlations and present individuals with a handful of the most relevant insights. Unlike prior generations of alerting mechanisms, these systems excel at separating signal from noise: they quickly learn what business users consider relevant and deliver only those insights. 

The best part about business monitoring systems is that they require minimal setup. Organizations don’t need to create a special repository, semantic models, feature sets, or visualizations or configure time-series databases and logic. They simply point the systems to existing data sources and press the “start” button. The systems automatically create a baseline of behavior for millions of metric combinations and detect changes in them over time. 

The business monitoring revolution has just begun. Today, these systems detect anomalies, identify correlations, and display potential root causes. Soon, they may suggest remedies, predict change, and suggest ways to optimize processes to avoid issues in the future. In essence, the systems will automate things that humans can’t do and augment what humans can with real-time recommendations and suggestions.