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.

Cloud computing changed the way enterprise IT works. Investments in public technologies are forecasted to grow by 21.7% to reach the $600 billion mark by the end of this year. The trend is driven by two major factors: Business organizations view these capabilities as an imperative for digital transformation — especially the domain-specific IT services that solve problems unique to their industry verticals.

Setting meaningful goals for your technology investment decisions requires an understanding of your requirements. Primarily, that’s… Measuring your IT maturity is one way to advance your IT performance — in a way that aligns with your organizational goals and minimizes the risk of failure. You can compare your current situation to a group of peers or competitors and also to industry benchmarks. Let’s take a look.

Predictive maintenance is a maintenance strategy that uses machine learning algorithms trained with Industrial Internet of Things (IIoT) data to make predictions about future outcomes, such as determining the likelihood of equipment and machinery breaking down. Using a combination of data, statistics, machine learning and modeling, predictive maintenance is able to optimize when and how to execute maintenance on industrial machine assets.