Some of the most common DevOps Monitoring challenges we hear about from customers are things that might be all too familiar to some of you. One of the most common is that teams lack visibility into the whole environment. This is both a symptom and cause of labor-intensive visibility, loosely coupled discrete tools, and a lack of hard data to capacity plan or assesses success.

Growth for an enterprise is an exciting thing, but it often presents a unique challenge for IT professionals. There are common roadblocks that are encountered when trying to upscale an IT management environment. In this first blog of our Managing IT Infrastructure at Scale series, we discuss the benefits of distributed monitoring data for large IT environments.

With more and more people working from home and the ever-increasing complexity of IT infrastructure, it’s important to understand the best way to leverage Machine Learning (ML) and Artificial Intelligence (AI) to improve IT operations. ML and AI have promised to bring disruptive changes to IT operations, and many organizations have already decided to adopt Artificial Intelligence for IT Operations (AIOps) or to do it soon. Yet, implementing and deploying AIOps is still very challenging.

Data security improvements can be an expensive necessity, but there are ways to make those improvements for free using your network and systems management data. While your network and systems management platform can’t replace your SIEM or IDS, making these improvements can improve your efficiency in a variety of valuable ways. If you monitor down to the individual switch port level, which we always recommend, you’ll have very granular data that can be used to spot changes in behavior.