Skip to main content
Criticality indicates how important an asset is to the business — used to prioritize monitoring, alerting, and governance effort toward the assets that matter most.
Use Manual mode when business context outweighs system-derived signals — for example, a regulatory reporting table that’s rarely queried but mission-critical.

​
Where criticality appears

The asset’s criticality shows as a colored chip at the top right of its detail header, for example MEDIUM. Select the chip to open the list of levels: CRITICAL, HIGH, MEDIUM, and LOW. The asset list also has a Criticality column, and filters on it.
Asset header with the MEDIUM criticality chip open to show the Critical, High, Medium and Low options

The criticality chip and its list of levels in the asset header

​
How Auto Criticality Is Derived

Criticality is an automatically derived score representing how important an asset is to the business, based on five factors: Usage carries the most weight, combining how many unique users access the asset with how many read queries it receives — assets used by many people, frequently, score higher. Dependency is equally weighted with Usage, capturing how many upstream and downstream assets rely on it — the more connected an asset is, the bigger the impact if it breaks. Metadata completeness contributes a moderate amount, driven mainly by whether the asset has linked business terms and domain/app/product classification, with smaller contributions from tags (whether DQLabs-detected or sourced) and a written description — well-governed assets are treated as more deliberately important. Freshness and Attribute Criticality play a smaller, supporting role — freshness reflects how recently the asset was updated, while attribute criticality reflects whether the asset contains sensitive or business-critical columns. Volume has the smallest influence, based simply on row count — larger datasets nudge the score up slightly, but size alone isn’t a strong indicator of importance. Together, Usage and Dependency dominate the score, which is exactly why low-usage but compliance-critical assets (like a rarely-queried regulatory table) often need a Manual override — the automatic calculation won’t fully capture business-mandated importance on its own. This works well for most assets because criticality often does correlate with how connected or heavily used something is.

​
When to Use Manual Override

Auto-calculated criticality can miss business context that usage data alone doesn’t capture. Switch to Manual when:
  • Regulatory or compliance tables — rarely queried, but legally mandated and high-risk if incorrect (e.g., financial reporting source tables).
  • Low-traffic but high-stakes assets — used only during specific events like month-end close, audits, or annual reporting.
  • New or recently launched assets — not enough usage history yet for Auto to reflect true importance.
  • Strategic initiatives — assets supporting a new business priority that hasn’t generated significant query volume yet.

​
Why This Matters

Criticality classification drives:
  • Alert prioritization — critical assets typically trigger higher-severity alerts on quality issues.
  • Review urgency — issues on critical assets get flagged for faster resolution.
  • Governance focus — stewards and owners are encouraged to prioritize documentation and monitoring for critical assets first.
Manual override ensures these downstream behaviors reflect actual business risk — not just system-observed activity.