cuebook/CueObserve

# Why CueObserve

CueObserve helps you monitor your metrics. It helps you know when, where, and why a metric isn't right, so that you can take action quickly.

## Monitoring Business metrics is different from monitoring System metrics

System metrics are typically monitored in real-time. The metrics are collected as timeseries data streams. To identify potential production issues, anomaly detection is also real-time.

On the other hand, Business metrics are monitored on a daily, weekly, or monthly basis. The data for business metrics resides in SQL databases and data warehouses. Instead of real-time anomaly detection, you need anomaly detection with variable granularity. This granularity depends on the metric and the user who's monitoring it.

To understand more, read our posts - [What is an anomaly?](https://cuebook.ai/blog/anomaly/) and [Anomaly detection: business vs. technical metrics](https://cuebook.ai/blog/anomaly-detection-business-metrics-vs-technical-metrics/).

## No-code and Cost-effective Anomaly Detection

CueObserve empowers data consumers to configure their own anomaly detection jobs, and not be dependent on the engineering team. This saves time for both.

Since the data consumer can choose which metrics to monitor and how deep to monitor, it reduces noise and infrastructure costs. To understand more, read [Why anomaly detection at scale is expensive and noisy](https://cuebook.ai/blog/why-anomaly-detection-at-scale-is-hard-expensive-and-noisy/).

## One-click Root Cause Analysis

Data consumers can do root cause analysis on anomalies with just one click. This reduces time to action.