IT people know that data is king, especially in optimizing IT operations. However, figuring out which metrics to collect and how to collect them can be challenging. IT teams have to factor in what IT directors, team managers, and the people overseeing operations want, what they’re concerned about, and what they consider important.

This is the story of an insurance company that was getting six million IT alerts every 90 days and how they used BigPanda’s AIOps to reduce it to less than 50,000. Before we get into that though, let’s take a step back. How did we, as an IT sector, get to a place where organizations receive 6,000,000 IT alerts in the first place?

It may be counterintuitive to tackle a problem without knowing exactly what the problem is, but an incident commander often does just that. In fact Rob Schnepp—founding partner at Blackrock 3, an Alameda, California-based incident management consulting group—says identifying the root cause of an incident is typically secondary to addressing the symptoms.

ITOps professionals know that their AI and automation goals can only be achieved with high-quality data. How can you get good-quality data? Incident Intelligence. In this on-demand session from Pandapalooza, our Group Product Manager, Orr Ganani, joined our Regional VP of Professional Services Sales, Jordan Gamble, to discuss Incident Intelligence and its benefits. Read on to learn more about Incident Intelligence from this webinar.

Today, many digital technologies in IT can operate with minimal human intervention. However, while they boost productivity and drive growth, any failure or unpredictable behavior can pose a significant challenge for the ITOps and DevOps teams. So, effective IT incident management helps minimize the impact of incidents on business operations and ensures that systems are restored as quickly as possible.

It’s no secret that every ITOps leader can face an ever-increasing amount of alerts. Since the dawn of digital, alerts have served an important purpose. Sometimes all those alerts can become overwhelming noise, and sorting out what is and is not a priority can become challenging. The good news is that artificial intelligence (AI) and machine learning (ML) are adept at processing large data sets in real time, looking for patterns and being able to aid in decision making.

To hear Ehab Tarabay explain it, the need for retailers to continue evolving their digital operations is an age-old problem. I recently hosted Tarabay, head of workplace IT services at TMF Group, on our That’s Great IT podcast. As an avid information technology specialist with a track record of more than 20 years in the technology field, he had a unique perspective to share about the shift that’s happening in retail right now.

As an ITOps professional, it can be challenging to justify all of your actions to your organization. After talking with many of you, we saw first-hand the pains and gaps around showing the impact of your team and the constant struggle to measure how you’re improving. That’s where Unified Analytics comes into play.

The average organization can have ten or more monitoring or observability tools in their IT stack. These tools keep generating an overwhelming amount of noise. IT Ops, NOC and DevOps teams drown in this noise and can’t focus on real incidents until it’s too late. Your organization’s alerts don’t have to turn into an untameable tsunami with no end in sight—there’s a better way forward.

Breaking down cloud management platforms and hybrid/multicloud management In our recent Whiskey and Wisdom session, we discussed how ITOps teams are coping with the evolution of cloud management. Whiskey and Wisdom is a monthly executive-only forum where IT operations leaders can network independently and discuss high-level AI operations and ITOps strategies with their industry peers.

There’s a familiar saying: garbage in, garbage out. For ITOps, this directly applies to data engineering. BigPanda’s Area Vice President of Value and Adoption, Craig Ferrara, says the importance of data hygiene—putting good data in to get good data out—is the core of data engineering, and it requires ITOps to take a look at their data before integrating with an AIOps solution.