A crucial step to having efficient DevOps consulting is by being completely transparent with data. By making monitoring data available to everyone in the value stream, everyone shares a common view of reality, which aids in communications, and demonstrates transparency which enhances trust.

In our last four blog posts, we have been sharing tips on eliminating DevOps monitoring challenges. In case you’ve missed them, make sure to catch up with our “Eliminating DevOps Monitoring Challenges” series. The next key point is to make sure you’re engaging the right people at the right time. This sounds obvious, but you’d be surprised how often it doesn’t happen.

DevOps automation solves common challenges that revolve around a lack of visibility to the entire environment. A lack of visibility, non-discrete tools, and a lack of hard data to capacity plan or assess success in your dev environment, often leads to using several tools that tend to contradict each other.

In the third installation of our “Eliminating DevOps Monitoring Challenges” blog series, we’ll share with you the importance of monitoring your dev environment in order to improve your DevOps monitoring.

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.

In today’s IT’s landscape, a variety of tools are available to us to help with root cause analysis process. Leveraging your tools and using them optimally is necessary to any system but it’s important to remember that tools do not have access to all the information available for them to be able to truly solve every problem So to truly get to the true root cause, you need a process that will take us beyond the scope of tools.

The moment after an incident is resolved is perhaps the most relaxing for any IT team. When your system is finally functioning properly it puts the entire organization at ease, but the most daunting task is yet to come: root cause analysis (RCA). Akin to football teams watching previous plays to pinpoint areas of improvement, root cause analysis goes through data and finds what initially caused the incident.