> For the complete documentation index, see [llms.txt](https://academy.dnanexus.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://academy.dnanexus.com/data-quality-manager/data-monitor.md).

# Data Monitor

The Data Monitor provides visibility into remediation activity and data quality improvements over time. It helps you track how your data evolves across runs and assess the impact of your remediation efforts.

<img src="https://1979569080-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FPtCOm9rXoRi4P9rh1ET8%2Fuploads%2FhMB71D68TY5yNCWAvpwC%2Funknown.png?alt=media&amp;token=3675aa77-0ed3-4feb-b30a-5464e83b6a40" alt="" height="286" width="576">

The Data Monitor includes:

### Refinery Quality Pass Rate

The Refinery Quality Pass Rate trend chart shows how your data quality changes across multiple execution runs. You can hover over any point on the line graph to see a popup summary with exact statistics and rule configurations for that specific run.

#### Key Definitions

* **Total Data Entries**: The total number of rows across all tables in your dataset.
* **Flagged Data Entries**: Data rows that match the conditions of one or more rules in that run (e.g. anomalies caught for cleanup).
* **Clean Data Entries**: The healthy rows that did not trigger any rules, calculated as:\
  Clean Data Entries = Total Data Entries - Flagged Data Entries

#### How It is Calculated

The **Pass Rate** represents the percentage of your original dataset that is completely clean and required zero corrections during the run.

{% hint style="info" %}
Refinery Quality Pass Rate = Clean Data Entries/Total Data Entries \* 100
{% endhint %}

### Remediation History

A list of all remediation runs, including execution details such as dataset and run time. You can select any run to revisit that remediation run, allowing you to review remediation results and compare data quality before and after each run.