The latest News and Information on Incident Management, On-Call, Incident Response and related technologies.
This is the final blog in our series focusing on CloudOps maturity, where we’ve been looking at the key findings from a recent IDC study, commissioned by PagerDuty. In our previous blogs, we discussed the people-based transformations and the technological changes that organizations must undergo to mature their CloudOps practices.
The link between DevOps and artificial intelligence for operations (AIOps) has only started to become clear within the last few years. Monitoring and alerting has evolved from a "black box approach," where you don't actually know what's happening, into observability, where you have access to data that provides everything you possibly need to know about your IT systems. How does AIOps come into play? AIOps is the practice of applying artificial intelligence, machine learning, and advanced analytics to automate and improve IT operations. Since it entered as a formal discipline with Gartner in 2016, IT teams have been trying to figure out how to employ it to make their lives easier.
The patient-centered care (PCC) model enhances the way providers interact with patients during the care delivery process. Clinicians that show compassion and empathy toward patients are more likely to achieve meaningful, positive doctor-patient relationships. Indeed, care teams that prioritize PCC have a proven approach to improving patient satisfaction and increasing patient retention.
There’s an incident. Your teams need to communicate with the development team that owns the service, but that team is too busy to stop and chat. Meanwhile, you in central IT have business leaders asking for updates, angry internal users calling the help desk, and customer service representatives asking for information. You have hundreds of tickets all pertaining to the incident in your ticketing system.
Facebook’s October 2021 outage was the type of event that gives SREs nightmares: A series of critical business apps crashed in minutes and remained unavailable for hours, disrupting more than 3.5 billion users around the world and costing about 60 million dollars. As incidents go, this was a pretty big one.
xMatters is part technology, part service reliability, and a little bit of magic. If you’ve spent time on the xMatters website, you’ll likely have seen a number of valuable use cases for the platform—it can alert SREs when there’s a website outage, it can accelerate product development for DevOps teams, it can manage on-call schedules and alerts for support teams.
