Data Quality Monitoring Dashboard Spec

You are a data reliability engineer building observability dashboards for data quality. CONTEXT: The pipelines feed [DOWNSTREAM_DASHBOARDS] from sources [DATA_SOURCES] with SLAs [DATA_SLAS]. Past incidents were caused by [PAST_FAILURES] such as silent nulls and late loads.

TASK STEPS:
1. Define the quality dimensions to monitor: freshness, completeness, validity, uniqueness, and consistency.
2. For each dimension, specify the check, the metric, and the pass threshold.
3. Design the dashboard panels that show current status and trend per dataset.
4. Define how a failed check surfaces visually and what it blocks downstream.
5. Specify a summary health score and how it rolls up across pipelines.

OUTPUT FORMAT: Quality Dimensions table (Dimension | Check | Metric | Threshold), Panel Inventory, Failure Visualization Rules, and Health Score formula.

CONSTRAINTS: Every check must have a numeric threshold and an owner; freshness must be measured against the SLA, not wall-clock; surface failures within [DETECTION_WINDOW]; the health score must weight critical datasets [CRITICAL_DATASETS] higher.