What is ITOM? Information technology operations management (ITOM) is the administration and management of an organization’s hardware, network, applications and technology needs. Generally regarded as the true meaning of “tech support,” it is a service-centric approach to IT infrastructure, IT support operations, IT networking and end user support.

Predictive modeling is the process of using known results to create a statistical model that can be used for predictive analysis, or to forecast future behaviors. It’s a tool within predictive analytics, a field of data mining that tries to answer the question: “What is likely to happen next?”

In in order to achieve DevOps success, you must measure how well your DevOps initiatives work. Tracking the right DevOps metrics will help you evaluate the effectiveness of your DevOps practices. In this article, I’ll explain many DevOps metrics, including their significance, the key metrics for various goals, and — best of all — tips for improving the score of each DevOps metric discussed here.

ITSM, which stands for IT service management, is a strategy for delivering IT services and support to an organization, its employees, customers and business partners. ITSM focuses on understanding end users’ expectations and improving the quality of both IT services and their delivery. In the early days of computers, employees relied on the company IT department for help whenever a computer issue arose.

In the world of business management and performance tracking, OKRs, KPIs, and Metrics are common terms thrown around. Each plays a distinct role in helping organizations define their vision, measure their progress, and improve their performance. Let's dive deep into understanding the nuanced differences between these three concepts.

Simply put: Observability is the ability to measure the internal states of a system by examining its outputs. A system is considered “observable” if the current state can be estimated by only using information from outputs, namely sensor data. More than just a buzzword, the term “observability” originated decades ago with control theory (which is about describing and understanding self-regulating systems).

Availability is the amount of time a device, service or other piece of IT infrastructure is usable — or if it’s available at all. Because availability, or system availability, identifies whether a system is operating normally and how effectively it can recover from a crash, attack or some other type of failure, availability is considered one of the most essential metrics in information technology management. It is a constant concern.

At the most basic level, application programming interface (API) monitoring checks to see if API-connected resources are available, working properly and responding to calls. API monitoring has become even more important (and complicated) as more elements are added to the network and the environment evolves, including multiple types of devices, microservices as a key part of application delivery, and, of course, the widespread move to the cloud.

Infrastructure as Code, or IaC, is the practice of automatically provisioning and configuring infrastructure using code and scripts. IaC allows developers to automate the creation of environments to generate infrastructure components rather than setting up the necessary systems and devices manually.

Businesses need to stay agile and make data-driven decisions in real time to outperform their competitors. Real-time analytics is emerging as a game-changer, with 80% of companies showing an increase in revenue due to real-time data analytics as companies can gain valuable insights on the fly. This blog post will explore the concept of real-time analytics, its examples, and some challenges faced when implementing it. Read on for a detailed explanation of this exciting area in data analytics.