> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dqlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> What a Conditional metric is in Prizm — a user-defined, row-level quality rule that every record in an asset must satisfy.

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## What is a Conditional metric?

Conditional metrics define **row-level quality rules** that every record in an asset must satisfy. They answer the question: *Do the values in this dataset meet the business conditions we require?*

**Type:** User-defined — Conditional metrics are created manually. Navigate to **Metric → Add** and select **Subcategory: Conditional**.

You can optionally scope the rule to a subset of rows using **Select criteria**. When no criteria is set, the rule applies to all records in the asset.

Prizm scores the metric as `(Valid records / Total records) × 100`. Records that fail the condition rules count as invalid and contribute to alert generation and the asset's overall quality score.

### Metrics

| Metric      | Category | Sub Category | Asset Level | Description                                                                                                          |
| ----------- | -------- | ------------ | ----------- | -------------------------------------------------------------------------------------------------------------------- |
| Conditional | Business | Validation   | Table       | A user-defined quality rule evaluated row-by-row against an asset. Records that fail the condition count as invalid. |

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    Step-by-step guide to creating and configuring a conditional metric.
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    Real-world examples — range enforcement, completeness, cross-column consistency.
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