# Quality
The Quality tab provides a detailed view of data quality across various dimensions, supported by different test types. Here’s what you can expect:
**Quality Dimensions and Test Types**
Our platform currently supports four quality dimensions, each associated by default with specific test types:
* **Accuracy:** Measures how close the data values are to the true values. Tests include “Regex” and “Value in.”
* **Completeness:** Measures the extent to which all required data elements are present. Tests include “Not Null.”
* **Uniqueness:** Checks each data record to ensure it is unique within the dataset. Tests include “Is Unique.”
* **Validity:** Ensures data conforms to acceptable standards, such as ranges and formats. Tests include “Is Email” and “Is UUID.”
* **Timeliness:** Measures how up-to-date the data is.
* **Consistency:** Measures reliability and uniformity of data within datasets.
* **Granularity:** Measures level of detail or the degree of aggregation present.
* **Others:** Any other tests not within the other categories.
You can customize the association of each Dimension to a [supported monitor that can output a Data Quality score](https://docs.decube.io/reports/asset-report-data-quality-scorecard#supported-monitors-with-default-quality-dimension).
{% hint style="info" %}
The dimension **Timeliness** will be introduced for Freshness monitors soon so that you can customize the scoring criteria for Timeliness. Do keep updated on our future releases.
{% endhint %}
Overview of Quality Dashboard
Decube calculates the Data Quality (DQ) score daily using this formula:
$$DQ Score = 1 - \left( \frac{Error Rows}{Total Rows} \right)$$
Where:
* Error Rows: Total number of rows that failed the data quality check
* Total Rows: Total number of rows scanned by the monitor
**For example**, if a “Not Null” test finds **10 null rows** out of **1000 total rows**, the score would be:
$$
\= 1-\[\frac{10}{1000}]=0.99 = 99
$$
#### Per Dimension (Health Score)
Each DQ dimension (e.g., Accuracy, Completeness, Validity) groups multiple monitors.
To calculate the score for a dimension:
1. Sum up error rows across all monitors under the dimension.
2. Sum up total scanned rows across those monitors.
3. Apply the same DQ score formula:
$$
\textbf{Dimension Score} = 1 - \left( \frac{\sum \text{Error Rows}}{\sum \text{Total Rows}} \right)
$$
This gives a weighted average, ensuring larger scans influence the score more than small ones.
#### Overall Data Health Score
The final DQ Health Score (shown on top of the dashboard) is:
$$
\textbf{Overall Data Health Score} = \frac{\sum \text{Dimension Scores}}{\text{No. of Dimensions}}
$$
* Dimensions without any scanned rows are excluded from the average.
{% hint style="info" %}
**Only a select few types of monitors can produce the health score. To know which monitors generate health scores,** [**read this article**](https://docs.decube.io/reports/asset-report-data-quality-scorecard#supported-monitors-with-default-quality-dimension)**.**
{% endhint %}
**Overall Data Health Score**
The Data Health Score represents the average score for all dimensions over the selected time period. The scores are color-coded for easy interpretation:
• **Green (> 98%)**: Excellent health
• **Yellow (95% - 98%)**: At risk
• **Red (< 95%)**: Poor health
**Custom Date Range and Filters**
The custom date range supports up to six months, allowing for in-depth analysis over a quarter. The Quality dashboard also includes various filters to help you narrow down your data view, such as:
• Data Domains
• Data Sources
• Data Owners
• Filter by tags
• Filter by classifications
• Monitor mode (Scheduled, On-demand)
• Row Creation Preferences (filter for 'All Records' scan only)
Overview of Apply filters modal
**Source/Domain Summary**
The Source/Domain Summary in the Quality tab provides results based on selected domains and shows scores for key quality metrics. This helps you gain a deeper understanding of your data’s health across different data sources and domains, making it easier to pinpoint areas for improvement.
