Finding Insights From Noise with AIOps

These days, every company is producing vast amounts of logs and data, and it can be a daunting task to find meaningful insights from this collection. At CoreLogic, using AIOps, or AI Operations, is critical to detecting anomalies, alerting teams, automating responses and even foreseeing future issues before they arise.

AIOps is the shortened term for applying artificial intelligence on a vast amount of operational data. AIOps provide intelligent, actionable insights that help in monitoring and managing IT environments that are hybrid, distributed, and componentized by using operational data, artificial intelligence (AI), and machine learning (ML) to:

  • Collect and aggregate ever-increasing volumes of operations data generated by multiple IT infrastructure components, applications, and performance-monitoring tools.
  • Develop and deploy machine learning bots that continuously sniff for anomalies that could lead to significant events and patterns related to system performance and availability issues.
  • Diagnose root causes and report them to IT for rapid response and remediation—or, in some cases, automatically resolve these issues without human intervention.

It replaces multiple separate, manual IT operations tools with a single, intelligent and automated IT operations platform, thus enabling IT operations teams to respond more quickly—even proactively—to slowdowns and outages, with a lot less effort. This process can help to create predictive outcomes that drive improved system availability and minimize customer impacts.

At CoreLogic, we use a data fabric platform built on the Elastic foundation to aggregate siloed IT operations data in one place. This data includes historical performance, streaming real-time operations events, system logs, network data, incident-related data, ticketing and related document-based data.

We’ve found incredible benefits in this process, and we use it for: Anomaly detection: Single Pane of Glass (SPOG): Alerting: Automating responses, including real-time resolution: Handling future problems:

Ultimately, at CoreLogic, we want to enable IT teams to identify, address, and resolve slow-downs and outages faster. By removing manual processes, we can:  Achieve faster mean time to resolution (MTTR): Transform from reactive to proactive to predictive management: Modernize IT operations and the IT operations teams:

By exploring new technologies and utilizing these efficiencies, CoreLogic is better able to support our clients—and make better use of our IT teams’ time and energy.

Prasad Challa
Senior Leader, Software Engineering