Most engineering teams are no strangers to key performance indicators (KPIs), those metrics tracking progress toward critical goals and targets. Ideally, tech leaders design KPIs to focus teams on what matters and prove their contribution to the company’s overall performance. Of course, KPI data should also uncover critical information that guides informed decision-making. For engineering teams tasked with managing the customer experience, KPIs often track availability.

According to one survey, 94% agree that AIOps is “important or very important” to manage network and cloud applications performance. AIOps intends to help customers contextualize humongous data volumes and streamline IT operations with automation. As IT infrastructure grows in complexity, alerts flood IT Ops centers and Ops teams drown in managing the deluge.

Engineering teams use a lot of single-domain monitoring tools. In fact, the average team manages and maintains 16 monitoring tools — and up to 40 — according to Moogsoft’s State of Availability Report. While IT leaders select and implement these tools to save teams time, our research finds they do quite the opposite. Engineers spend far and away more time on monitoring than they do on any other task — innovative, value-creating tasks included.

IT operations move fast. If you’re an ITOps leader, you need to be moving just as fast to make sure your team has what it needs. Positioning your team for success isn’t easy: complexity in IT is increasing every year and can reach a point where it exceeds a person’s capacity to keep pace. In the face of massive growth, ITOps teams can face major challenges with productivity, burnout and efficiency.

The volume of data that IT systems generate nowadays is overwhelming, and without intelligent monitoring and analysis tools, it can result in missed opportunities, alerts, and expensive downtime. However, with the advent of Machine Learning and Big Data, a new category of IT operations tool has emerged called AIOps. AIOps can be defined as the practical application of Artificial Intelligence to augment, support, and automate IT processes.

If you’re in IT, no doubt you’ve heard the age-old statistic that an average minute of downtime costs $5,600. It turns out that information is a bit outdated and does not reflect the real and nuanced costs of a modern IT outage. BigPanda suspected this and wanted to uncover the true numbers behind outage costs so ITOps can have a better understanding of costs, causes and “cures” of an IT outage.