Setup

Creating a conditional metric

Add the metric

Navigate to Metric in the top navigation.
Click the Add button (⊕).
Enter a Metric Name.
Select Subcategory: Conditional from the dropdown.
Enter a Purpose description.
Click Create.

Open the configuration view

Click the metric name to open its detail page, and provide the following details

Asset — the table the metric evaluates
Metadata tabs — Description, Purpose, Rationale
Conditional Metric Configuration — three tabs: Configuration, Total Records, Failed Rows
Threshold
Semantic context and Owners

Set the asset and metadata

Asset: Select the table or view this metric will evaluate. Use the AI-assisted asset selection button to let Prizm suggest an asset based on the metric name and purpose.The Downstream Impact counter shows how many other assets depend on this asset — useful for understanding blast radius.Fill in the metadata tabs as needed:

Tab	Content
Description	What this metric measures. Prizm AI can suggest a description based on the metric name.
Purpose	Business justification — why this metric exists.
Rationale	The reasoning behind the specific rule chosen.

Configure the condition rules (Configuration tab)

The Configuration tab is the core of a conditional metric. It has three sub-sections.

Select criteria

Expand Select criteria to scope which rows are evaluated. By default this uses SELECT * (all rows). Choose between:

All columns — evaluate all columns in the asset
Selected columns — restrict evaluation to specific columns

Leave Select criteria empty to evaluate every row in the asset.

Condition rules

Define what “valid” means for each row. Use the logical operators NOT / AND / OR to combine multiple rules. Click + Add rule to add a rule or + Add group to create a nested rule group.Select a Rule type from the toolbar, then click + Add rule. Each rule type exposes different fields:

Row check
Col vs col
Aggregate
If–then
Expression

Per-row condition on a single column. Validates that each record’s column value meets a specific condition — e.g., QTY_SOLD Between 10 and 20.

Click Row check in the rule type toolbar.
Click + Add rule.
Select column — choose the column to evaluate.
Select operator — choose the comparison (e.g., =, !=, Between, Is Null, Is Not Null).
For range operators (e.g., Between), enter a Start value and End value.
For single-value operators, enter the comparison Value.

Compares one column to another. Enforces cross-column consistency — e.g., end_date >= start_date.

Click Col vs col in the rule type toolbar.
Click + Add rule.
Select column — choose the first (left-hand) column.
Select operator — choose the comparison operator.
A second column picker appears — select the comparison column.

Set-level assertion — evaluates across all rows. Enforces dataset-wide constraints — e.g., no duplicate values in a column.

Click Aggregate in the rule type toolbar.
Click + Add rule.
Function — select the aggregate function from the dropdown (e.g., No duplicates on).
Select columns — choose one or more columns the function applies to.

Conditional logic — condition only fires when the guard clause is true. Use when a rule should apply only to rows that satisfy a prior condition — e.g., if order_type = 'express' then carrier_tracking_number is not null.

Click If–then in the rule type toolbar.
Click + Add rule.
In the IF section — define the guard clause:
- Select column — the guard column.
- Select operator — the guard condition.
In the THEN section — define the required outcome:
- Select column — the column that must satisfy the consequence.
- Select operator — the consequence condition.

Build a WHERE predicate — rows must satisfy this condition. Use for complex expressions that cannot be captured by the other rule types. The expression builder combines columns, functions, and operators into a custom SQL WHERE clause.

Click Expression in the rule type toolbar.
Click + Add rule.
In the COLUMNS panel on the left, click a column name to insert it into the expression area.
Switch to the FUNCTIONS tab to insert SQL functions.
Use the operator buttons to build the full condition: AND, OR, NOT, =, !=, <, >, <=, >=, BETWEEN, IN, IS NULL, IS NOT NULL.
The completed expression becomes the WHERE predicate evaluated row-by-row. Rows satisfying it are counted as valid.

Rule options

Option	Description
Valid	Count only rows that satisfy the rule as valid records
Scoring	Include this rule in the asset quality score
Weightage%	Relative weight of this metric in the overall score (default 100)
Pass Criteria	Score percentage above which the rule passes (default `>= 100`)
Export	Include rule results in data exports

Click Validate to test the rule against current data and preview Valid / Invalid counts. Click View prepared query to inspect the underlying SQL Prizm generates.

Define Total Records and Failed Rows queries (optional)

The Total Records and Failed Rows tabs let you override how Prizm counts the denominator and numerator of the score — useful when the default row counts don’t reflect your business logic.

Tab	Description
Total Records	Custom SQL query that defines what counts as the total record population. Leave empty to use the full row count from Select criteria.
Failed Rows	Custom SQL query that defines which records are considered failed/invalid. Leave empty to use the rows that fail the Condition rules.

Both tabs support Prizm AI suggestions — click the AI icon to generate an appropriate query based on the metric context.

Set the threshold

Choose how Prizm determines when to raise an alert:Automated Threshold — Prizm learns statistical baselines from historical data and alerts when invalid row counts deviate by a configurable number of standard deviations:

Priority	Deviation range	Default
Low	0σ – 1σ	Off — within expected variation
Medium	1σ – 2σ	Off — monitor for drift
High	2σ – 3σ	On — investigation recommended
Critical	≥ 3σ	On — immediate action required

Custom Threshold — Set explicit upper and/or lower bounds on the invalid row count or score to trigger alerts.

Set semantic context and owners

Scroll down past the Threshold section to complete:

Semantic context: Assign Domain, Application, Product, Tag, and Dimension to make the metric discoverable and contextually relevant.
Owners: Set Business owner, Technical owner, and Steward.
Audience: Restrict who can view this metric’s results.

Click Save Changes when done.

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