Operations | Monitoring | ITSM | DevOps | Cloud

As digital transformation continues to be central to an organization’s growth mandate, it’s critical to ensure that customer-facing, revenue-generating, mission-critical applications are operationally reliable and secure. That’s where Sumo Logic comes in—for almost 10 years, we have been providing a Continuous Intelligence platform for DevSecOps that’s utilized by over 2000+ customers in almost every vertical.

It’s been said hundreds of times: in the digital era, customers tolerate no downtime. IT operations teams must keep systems running 24x7x365, as the price of downtime is steep. According to Gartner, in 2014, organizations lost $5,600 per minute of downtime, which worked out to well over $300,000 per hour. Today, it’s likely higher, as organizations increasingly rely on technology to power revenue-generating business services.

Since the beginning, we’ve wanted to make it faster, easier, and even a joy to respond to incidents. We’ve had the typical components of incident response for a while, but orchestrating them together was a manual task by our users. Today we’re marrying together all the features already available in our incident response tool into our newest release: Runbooks.

In any platform of sufficient complexity, multiple anomalies are likely to occur. For many organizations, NOC operators triage multiple anomalies based on their severity. There are internal, non-customer-facing issues that might affect only a small part of your workforce and one-time issues that affect only a small number of customers. Both of the issues get ticketed and sent to low-level support.

Tradeoffs are hard. Think about the time when you had to choose between two equally compelling options - (a) addressing technical debt or (b) pushing out that long-awaited feature release, and risk breaking production. Or when your team couldn’t agree on where to draw the line on improving request latency versus shipping a major new update.

Let’s start with simple definitions. Time series data is largely what it sounds like – a stream of numerical data representing events that happen in sequence. One can analyze this data for any number of use cases, but here we will be focusing on two: forecasting and anomaly detection. First, you can use time series data to extrapolate the future.

As discussed in a previous blog, here at LogicMonitor, we are in the process of rolling out a new user interface (UI) which is designed to streamline workflows, reduce clicks, and include powerful new features. So what’s new in the alerts page?