Our data semantic layer contains semantics for starting and stopping an investigation, and the popularity of each action the user is doing with respect to a specific alert. We found that our solution can reduce MTTR by up to 20%.Īs MTTD decreases, users can identify the problem and resolve it faster. As a result, the average user is equipped with the aggregated experience of their entire company, leveraging the wisdom of many. To help customers reduce MTTD and MTTR, Logz.io is turning to machine learning (ML) to provide recommendations for relevant dashboards and queries and perform anomaly detection via self-learning. They’re calculated by measuring the time a user in our platform starts to investigate an issue (such as production service down) to the point when they stop doing actions in the platform that are related to the specific investigation. Mean time to detect (MTTD) and mean time to resolution (MTTR) are key metrics for our customers. It can be overwhelming for new users who are looking to navigate across the various dashboards built over time, process different alert notifications, and connect the dots when troubleshooting production issues.
Customers are sending an increasing amount of data to Logz.io from various data sources to manage the health and performance of their applications and services.
Logz.io offers a software as a service (SaaS) observability platform based on best-in-class open-source software solutions for log, metric, and tracing analytics. Logz.io is an AWS Partner Network (APN) Advanced Technology Partner with AWS Competencies in DevOps, Security, and Data & Analytics.