Setup

Creating a behavioral metric

Add the metric

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

Open the configuration view

Click the metric name to open its detail page. The behavioral metric configuration view exposes:

Asset — the table the metric runs against
Metadata tabs — Description, Purpose, Rationale
Behavioral Metric Configuration — metric/grain, grouping dimensions, comparison context, and detection settings
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.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 and what behavior it monitors.
Rationale	The reasoning behind the aggregate function, grain, and comparison context chosen.

Set the metric and time grain

The Metric & Grain section defines what aggregate to compute and at what frequency.

Aggregate function

Select how to aggregate the target column:

Function	Description
Sum	Total of all values in the column per time grain
Average	Mean value across all records per time grain
Median	Middle value of the distribution per time grain
Percentile (P90 / P95)	90th or 95th percentile value per time grain
Count	Row count per time grain
Ratio	Ratio of two aggregate values

Select the Column the aggregate applies to from the asset’s schema.

Time grain

Grain	Description
Daily	Aggregate and evaluate values once per day
Weekly	Aggregate and evaluate values once per week

Set grouping dimensions

Grouping dimensions segment the metric by one or more attributes — allowing Prizm to detect anomalies at the segment level rather than just the aggregate total.Select dimensions using the chip selector. No SQL is required — Prizm uses the asset’s schema to populate available dimension columns.Example: Adding region and product_category as grouping dimensions allows Prizm to detect that a volume drop is isolated to one region, rather than being masked in the global total.Leave grouping dimensions empty to evaluate the aggregate at the full asset level.

Set comparison context

The comparison context defines what the current value is compared against to determine whether it is anomalous.

Context	Description
Historical Baseline	Compare the current period’s value against a rolling window of historical values (7 / 14 / 30 / 90 days). Best for detecting absolute deviations from past behavior.
Peer Group	Compare one segment’s value against the values of peer segments with the same grouping dimensions. Best for detecting relative underperformance — e.g., one region declining while others hold steady.
Period-over-Period	Compare the current period against the same period in a prior cycle (last week, last month, last year). Best for seasonally adjusted metrics with predictable cycle patterns.

Set detection mode and sensitivity

Detection mode

Choose how Prizm evaluates anomalies across grouping dimensions:

Mode	Description
Independent	Each dimension combination is evaluated independently using its own time-series baseline. Use when segments behave differently and should be assessed on their own track record.
Correlated	Detects anomalies in how dimensions behave in relation to each other. Catches correlation breaks that are invisible when segments are assessed in isolation — e.g., revenue and volume diverging unexpectedly.

Sensitivity

Control how aggressively Prizm flags deviations:

Setting	Description
Low	Only flags large, obvious deviations — fewer alerts, higher confidence
Medium	Balanced detection — default for most use cases
High	Flags subtle deviations — more alerts, better coverage for critical metrics

Advanced sensitivity allows explicit configuration of Z-score thresholds, percentage deviation bounds, and minimum sample size requirements.

Behavioral Detection Mode and Sensitivity

Set the threshold

Choose how Prizm determines when to raise an alert:Automated Threshold — Prizm uses the adaptive baseline and configured sensitivity to determine the alert boundary automatically:

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 aggregate value 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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