Modern hybrid, multi-cloud, and cloud native environments have created increased management complexity for enterprise IT teams. Dynamic and distributed applications, infrastructure and business-critical services are constantly generating more data in the form of metrics, events, and alerts.
We’re launching a new feature today that allows anyone in your organization to kick off your incident response process with an appropriate severity level attached from Slack. Often people are afraid to open an incident or even share that they’re aware of something going wrong with your applications. When everything is important, nothing is important; users frequently overestimate the impact of an incident and assign an inappropriately high severity level.
This week at re:Inforce in Boston, the AWS team showed off its Security Hub service—a powerful service that provides SecOps teams a comprehensive view of their high-priority security alerts and compliance status across their AWS accounts. We’re excited to join AWS at re:Inforce this week as a Security Hub partner, where we’ll show users how PagerDuty and AWS Security Hub work together to provide real-time SecOps to any team using AWS.
To keep you up-to-date with what’s going on in anomaly detection, we keep an ongoing list of the biggest glitches happening in the business world. Here is what made waves in June. June 25, 2019 When Dutch telco KPN suffered a major outage on the evening of Tuesday, June 25, the 112 emergency number was also knocked out across the country. “We have no reason to think it was (a hack) and we monitor our systems 24/7,” the company spokesperson told Reuters.
Companies invest in anomaly detection in order to proactively identify risks, such as revenue loss, customer churn and operational performance issues. Anomaly detection essentially enhances traditional BI and visualization tools, venturing beyond a summary view of your data. It constantly scans every metric, at a granular level, to find abnormalities. But in order for this technology to have an impact, you must be able to trust it.
Machine Learning (ML) algorithms are designed to automatically build mathematical models using sample data to make decisions. Rather than use specific instructions, they rely on patterns and inference instead. And the business applications abound. In recent years, companies such Google and Facebook have found ways to use ML to utilize the massive amounts of data they have for more profit.
We’re pleased to come home from DattoCon19 with memorable experiences. It was a well-curated conference, striking a fine balance between emerging MSP trends, informative sessions, networking and of course, entertainment. Here are my top takeaways from the event.
On June 20 we have released the 3rd update for EnterpriseAlert 2019. Don’t get confused by the version number, 8.2.0 would better be 8.3.0, but that’s the way it is. Here is a very short EA 2019 release history for CY 2019.
