Metric prediction building blocks, when combined with InsightFinder’s incident prediction engine, leads to actionable insights.

In an increasingly data-driven world, businesses are turning to advanced predictive analytics to stay ahead of potential IT issues. The landscape is dotted with solutions like DataDog and Dynatrace, both of which are market leaders in observability capabilities. These systems excel at presenting rich information when a particular problem occurs, which aids businesses in reducing mean time to resolution. While this approach is useful, it often falls short in the realm of proactive incident prevention. As companies seek the fastest path to zero downtime, incident prediction is emerging as a key solution to support IT teams. 

Incident predictions vs. Metric predictions

Traditional statistical analysis can predict a metric value trend, which allows businesses to see what lies ahead for that specific parameter. While this is beneficial, it does not provide a holistic view of how multiple metrics interact and potentially lead to incidents. InsightFinder provides not only metric values, but also leverages raw log and metric data to predict incidents. This provides holistic views of how multiple metrics interact and potentially lead to incidents. These predictions are fine-tuned through learning from historical incident data within observability platforms and/or IT Service Management tools. For businesses, this means having a broader and more integrated understanding of upcoming challenges, well beyond a singular metric.

The Human Element in Machine Learning

For any AI product, one key factor for the ease of the adoption is the level of human intervention required. Observability tools often have internal machine learning components, but these systems rely on human-set percentile values and thresholds to function effectively. This supervised approach can be resource-intensive and may not always capture the dynamic nature of real-world operations. In contrast, InsightFinder utilizes unsupervised machine learning, requiring no manual setting of thresholds or configurations. It’s a more adaptive, efficient, and resource-light approach, aligning more closely with the autonomous needs of modern businesses.

For companies looking for solutions to reduce the workload of their support teams, the distinction between metric and incident prediction is crucial. The common problem of noisy alerts highlights the issue. Without predictive incident capabilities, many alerts need to be manually investigated. Noise in alerting systems can lead to alert fatigue, where critical issues are lost in a sea of false alarms.

InsightFinder Customer Success Story

InsightFinder’s solution integrates Kubernetes, DataDog, and Dynatrace APIs, which can aggregate all machine data in real time, without requiring any alterations to existing monitoring setups. For one of the world’s largest consulting companies, after assimilating three months’ worth of data, InsightFinder provided 85.3% accuracy in incident prediction with a lead time of 105 minutes for critical services. This level of precision and foresight is crucial for businesses aiming to streamline their operations and minimize downtime.


Upgrading from metric prediction to incident prediction is foundational to how businesses can preempt and prepare for IT disruptions. Observability platforms have laid the groundwork with system intelligence, but the advanced incident prediction capabilities of InsightFinder provide a clearer, more actionable path forward. In the dynamic and unpredictable landscape of IT operations, having a system that can predict incidents with high accuracy and substantial lead time is a necessity for maintaining a competitive edge and ensuring operational resilience. To test out a free trial for InsightFinder, sign up here. To learn more about InsightFinder’s integrations, go here.

Other Resources

A major credit card company’s mobile payment service experienced severe performance degradation on a Friday afternoon.
InsightFinder utilizes the industry’s best unsupervised multivariate machine learning algorithms to analyze a large amount of production system data.