Data is both a challenge and an asset for IT professionals, who rely on IT Operations Analytics (ITOA) to guide them towards operational excellence, system reliability, and swift incident resolution. So whether you’re seeking clarity on understanding what ITOA is and its connection to related technologies, are contemplating how to use it within your organization, or are curious about its enhanced efficiency and cost savings benefits, we’ve got you covered.

Maybe you’re still using monolithic applications, built and refined over many years. You understand that shifting to microservices or containerized architectures is a huge and daunting task. You’re probably grappling with the limitations of legacy systems—maybe they’re slow, tough to update, or can’t scale as you’d like. And you’re likely using more traditional IT monitoring tools or even some cloud observability tools.

IT operations teams are challenged to keep pace with the rapid speed of digital transformation. As companies use more cloud-based apps, increase agile deployments, and develop new microservices-based applications, they add layers and complexity to their technology stacks, making life increasingly challenging for ITOps performance.

Maintaining your Configuration Management Database’s (CMDB) accuracy, keeping it fully updated, and improving its performance is a frustrating and elusive goal for ITOps and IT leaders. Aiming for this ‘golden’ CMDB standard can feel like running on a treadmill where you’re putting in a lot of work, but remain as distant as ever from your goal. Can IT leaders ever catch up?

Mean Time Between Failures (MTBF) measures the average duration between repairable failures of a system or product. MTBF helps us anticipate how likely a system, application or service will fail within a specific period or how often a particular type of failure may occur. In short, MTBF is a vital incident metric that indicates product or service availability (i.e. uptime) and reliability.

So, you’ve spent some money and you’re the proud owner of a shiny new AIOps tool that helps improve your Network Operations. Network alarms are now usable, but with all the constant monitoring, supervision, and incident management, your Network Operations Center (NOC) is still overwhelmed. It’s time to pull out another stop.

IT leaders are thrilled about the potential of Generative AI for IT Operations. But they also want to know how it works, why it works, and what it will do for them before taking the leap and adopting this new technology. Allow me to share my perspective on the hype and the truth behind Generative AI. I’m the Field CTO for BigPanda, Operational Intelligence and Automation driven by AIOps.

Today, the majority of organizations operate under a hybrid cloud structure. Due to this, operations are consistently met with daily infrastructure and software changes and updates, which are also the primary cause of incidents and outages. Long gone are the days when a tech stack could be represented by a single dependency model. Microservices, CI/CD, and containers across multi-cloud make it extremely difficult to track all the changes and connect them to incidents.