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 It turns out that information is a bit outdated and does not reflect the real and nuanced costs of a modern IT outage.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.

As the rapid digital transformation has put a lot of pressure on IT organizations to be more proactive and agile, DevOps principles and practices have been an invaluable resource. However, to remain at the top of the game, organizations need an even stronger solution. So, what’s the answer? AIOPs (artificial intelligence for IT operations), of course!

Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML) and other artificial intelligence (AI) technologies to automate the identification and resolution of common IT issues. The systems, services and applications in a large enterprise produce immense volumes of log and performance data. AIOps uses this data to monitor assets and gain visibility into dependencies within and outside of IT systems.

Business organizations that want to save money and be competitive take into consideration the time costs associated with investments in new technologies. Will the efficiency gains translate to a rapid return on investment? Will users embrace the change and be more productive? Or will those investments be a hassle to employees and result in time-wasting workarounds and a fallback to inefficient, manual processes?