What is an analytics tool? Why are they important? Which do you need for a data-driven organization? Read on to find out.

The million-dollar question is: how can you reduce both false positives and false negatives, without constantly having to adjust your thresholds? Within the fast-growing field of AI analytics, anomaly detection is being enabled by machine learning algorithms to be significantly more accurate.

At the heart of AIOps is an effective observation system that surfaces and contextualizes anomalies to guide root cause analysis.

Proactive analytics is an IT term that refers to taking an active approach to active business monitoring in order to prevent incidents from escalating. This involves monitoring 100% of an organization’s data in real time, autonomously adjusting thresholds for and sending automatic alerts as early as possible so you can resolve problems before they negatively impact the bottom line.

In the last few years, IT monitoring and business analytics have shifted into a single methodology: business monitoring. Learn more here.

A time series database consists of state change information that is indexed by time. Learn more here.

Learn the fundamentals of time series metrics, explained with key examples and use cases from the business world, including time series analysis, forecasting, monitoring and databases.

Regardless of your industry, the countless metrics and KPIs at your disposal is currency you can use to purchase more accurate business forecasting. Machine learning is autonomously powering these forecasts, because of its superior abilities to be accurate, scale, adapt to fluctuating behavior and deliver results in real time.

Application Performance Monitoring (APM) is the management of software applications, typically from a high-level view, to ensure an expected level of availability and performance.