You probably used multi-factor authentication (MFA) to access the device you’re using right now. Maybe your phone scanned your face or fingerprint to unlock. Maybe you got a text with a verification code while logging into your work browser profile. Configuring MFA is a go-to measure for system hardening, but MFA enforcement can get unruly, especially at the scale required by enterprise IT.

Alerts that you receive from your observability tool are based on conditions that existed seconds to minutes in the past, because the alert is only triggered after the data has been indexed within the tool. This means that your ability to take timely action in response to the condition is significantly limited, and often your window of opportunity to react is past by the time you receive the alert.

One challenge for the widespread adoption of telemetry pipelines for SRE teams within an organization is knowing where to start when building a pipeline. Faced with a wide assortment of sources, processors, and destinations, setting up a telemetry pipeline can seem like trying to build a Lego set without any instructions. The solution is to provide teams with pre-defined components that provide specific functionality, that they can then use to build pipelines that meet their own requirements.

Teams need robust scheduling tools that enable them to create and manage on-call rotations, ensuring that there's always someone available to respond to urgent issues. Round-robin scheduling is a common approach, where team members take turns being on call. ‍

Postmortem reports are essential in incident management, helping teams learn from past mistakes and prevent future issues. Traditionally, creating these reports was a slow, tedious process, requiring teams to gather data from multiple sources and piece together what happened. But with AI and Large Language Models (LLMs), this process can become faster, smarter, and much less of a headache.

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.

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.

When it comes to managing development projects, many teams use both Azure DevOps and Jira to collaborate across departments. While Jira is often favored by project management teams for issue tracking, Azure DevOps excels with developers for managing code repositories, pipelines, and deployments. Combining these two tools can bring clarity, reduce duplication, and foster better collaboration between both development and non-development teams.

Widespread adoption of Generative AI has infiltrated nearly every business sector. While tools like transcription and content creation are readily accessible to all, AI's transformative potential extends far deeper. Its influence on coding and software development raises profound questions about the future of this industry.

HR organizations safeguard some of a company's most crucial information – from insurance and benefits details to company policies, onboarding and more. An HR knowledge management solution is vital to streamlining those functions. Without the right HR knowledge management solution, dispersing knowledge and answering questions can take up too much of an HR team's time. That pushes more critical tasks down the to-do list.