Operations | Monitoring | ITSM | DevOps | Cloud

Artificial intelligence (AI) and generative AI – they may sound similar, but how do they differ? Although these terms are often used interchangeably, generative AI is actually a subtype of AI. At ScienceLogic, we use many different types of AI, from machine learning to generative AI, to analyze IT environments, provide insights to help IT pros take action, automate and streamline workflows, and drive innovation.

As pressure increases on enterprise IT teams to streamline processes and reduce downtime, many organizations are looking for new tools and strategies. Customers and stakeholders expect operational efficiency and service reliability. Tools within the AIOps industry can relieve the pressure by reducing alert noise, automating manual workflows, and reducing mean time to resolution (MTTR). However, the challenges don’t end at tool purchase.

When implementing new tech, the challenges don’t end at tool selection, purchase, and initial deployment. You can have the best technology in the world, but it won’t help your organization if no one uses it. Many teams look to AIOps solutions like BigPanda to reduce noise, improve workflows, and resolve incidents faster through AI and automation. Bringing in a new platform is part of the equation. The other part is organizational change management to support platform adoption.

A typical enterprise generates a flood of information every day in the form of infrastructure and network data, operational and application data, security data, user access data, and more. With the right visualization capabilities, companies can thoroughly examine the multitudes of data they create daily to glean critical insights. The catch, however, is capturing actionable insights without exhausting the human resources of IT.

As organizations increasingly rely on complex and ephemeral infrastructure to drive business outcomes, the need for faster, more accurate, and automated IT operations has never been greater. Enter AIOps (Artificial Intelligence for IT Operations), a transformative approach that leverages AI and machine learning to automate and enhance IT operations management. These new learning systems can analyze massive amounts of network and machine data to find patterns not always identified by human operators.

Are you looking for a way to increase your AIOps signal to noise ratio and get more value from your data? In this article we will explore how one can utilize OpenTelemetry’s collectors, processors and data models to add or enhance classification attributes. These attributes can help you use your AIOps tools more efficiently and derive more value from your current data.