In an increasingly data-driven world, businesses are turning to advanced predictive analytics for IT Operations and DevOps. 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, predictive analytics 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.
Conclusion
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.
FAQs about The Evolution of Predictive Analytics in IT Operations and DevOps
Q. What specific technical challenges does InsightFinder face when integrating with existing IT infrastructure, and how are these addressed?
A. InsightFinder can be deployed via SaaS or to On-Premise systems. It integrates with more than 60 different data source (see the full list here) and uses that data to model system performance.
Q. How does InsightFinder handle sensitive data?
A. Data sensitivity is a critical concern for most industries, particularly healthcare, government, and banking. InsightFinder adds regex patterns to log entries to filter out any sensitive data for both json or string format log entries. Matches sensitive fields are dropped and never enter the InsightFinder platform.
Q. What level of technical expertise is required for a team to implement and manage InsightFinder within their IT operations and DevOps environments?
A. InsightFinder is built for use by IT Operations, DevOps, and SRE teams. Setup and administration requires a working knowledge of containers, databases, and network systems, as well as of your in-house systems. InsightFinder is often integrated with different system agents, third-party APIs, and other monitoring apps (Splunk, DataDog, etc.). While InsightFinder uses unsupervised behavior learning (UBL) and the self-organizing map (SOM) scheme to perform automatic anomaly detection, users don’t need to be experts in artificial neural networks to leverage the power of machine learning to ensure system uptime.
