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?

It is getting spooky out there, folks! Every year on October 31, we don our spookiest (or silliest) garb, an evolution of old practices where people would dress up to ward off ghouls, goblins and all manner of things that go bump in the night. After all, people believed these pesky spirits stirred up trouble. While pieces of this spooky tradition persist, just a few other things have changed in the past 2,000 years. For starters, we are a digital society.

Modern AIOps doesn’t just fix outages — it prevents them – Is your business one accidental click away from a major outage? We saw it happen with Atlassian earlier this year. You may already have an incident management strategy and monitoring, but is it adjusted for the ever-changing IT infrastructure and application architectures? Putting appropriate protocols in place ensures that one human code push can’t shut down an entire system for three weeks.

The absence of topology can be a key inhibitor for AIOps tools, creating blind spots for AIOps as they only have access to event data. A topology, an IT service model, or a dependency map is a real-time picture of tools and services that are connected and dependent on each other to deliver an IT service. Suppose an application is driven by cloud-native technology, connected with any kind of ephemeral systems (containers and microservices), and relies on storage, database, and a load balancing tool.

With BigPanda’s self-service Email Parser, extracting alert data from emails has never been more simple. In our latest video in the Fast track series, we explore the benefits of this tool. This parser is ideal for monitoring tools and systems that do not support REST API and or rely solely on email to generate and send alerts. So no matter what tools your organization utilizes, this feature can help you turn all of those alert emails into actionable incidents within BigPanda’s platform.

Our own Chief Revenue Officer, Todd McNabb, recently sat down with Melissa Graham, Sr. Vice President of Global Sales at SHI International, to discuss our new partnership.