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Guide to incident response metrics and KPIs

IT incident management focuses on quickly identifying and resolving IT issues to restore normal service operations. Tracking key performance indicators (KPIs) of incident response is vital in minimizing service disruptions affecting customers and users. With so much data and many things to track, it’s difficult to identify which metrics and KPIs are right to track. What are the right incident response metrics to use to drive meaningful improvements?

Private Cloud Providers: 10 Best Options And Key Features to Consider

While not every organization will opt for a private cloud, those who do must navigate a challenging market with numerous options. But what exactly are private cloud providers? How do they differ from other options, like public or hybrid cloud models? Understanding these distinctions is essential for selecting a provider that meets your organization's specific needs and strategic goals. Let's explore how the private cloud works, the features it provides, and what to look for when choosing a provider.

Redefining RUM: A Comparative Gap Analysis of Existing Tools

Real user monitoring (RUM) began as a straightforward approach to tracking basic web performance metrics. Focused on things like page load times and response rates, RUM relied on server-side logging and simple browser timings. While these tools captured Core Web Vitals (CWVs), they offered limited insights into how users actually interacted with pages, focused mainly on server-side performance.

Understanding Java Logs

Logs are the notetakers for your Java application. In a meeting, you might take notes so that you can remember important details later. Your Java logs do the same thing for your application. They document important information about the application’s ability to function and problems that keep it from working as intended. Logs give you information to help fix coding errors, but they also give your end users information that helps them monitor performance and security.

Hurricane Helene Devastates Network Connectivity in Parts of the South

In this post, we dig into the impacts from Hurricane Helene which came ashore late last month wreaking destruction and severe flooding in the Southeastern United States. Using Kentik’s traffic data as well as Georgia Tech’s IODA, we detail the impacts in three of the hardest-hit states: Georgia, South Carolina, and North Carolina.

The Vitals Signs: Why Managed IT Services for Healthcare?

Organizations across the globe are seeing rapid growth in the technologies they use every day. And while the healthcare industry has always been slow to adopt, they are quickly starting to benefit from the role new technologies play in enhancing patient care and operational efficiency. However, one major setback for healthcare SMBs when investing in advanced technology is working out how they are going to keep up with cybersecurity, performance, and management of these IT solutions.

Optimizing IT Incident Resolution with Automation: A Game Changer for ITSM Investments

Businesses heavily rely on IT Service Management (ITSM) platforms to ensure the smooth functioning of critical services. While these platforms offer excellent incident management capabilities—tracking, organizing, and reporting incidents—they often fall short when it comes to fast, reliable resolution. Enter incident resolution automation, a game-changer in optimizing ITSM investments.

Cloud Migration: Unlock the Full Power of Your Unified Endpoint Management Solution

To understand the challenges facing modern IT teams in managing a diverse range of endpoints, one word that keeps coming up? Proliferation. Organizations are faced with a rapidly expanding ecosystem of network devices, tools and assets–some of which are so hidden from view that IT doesn’t even realize they exist.

An Introduction to AI Inference

As a straightforward definition, AI inference is the process of applying a pre-trained machine learning model to new, unseen data in order to generate predictions, classifications, or decisions. Unlike the training phase, where the model learns from a dataset, inference involves utilizing the learned patterns to analyze and interpret new inputs.