How Quality Score Settings Work¶

DO Calculating quality scores

Quality score settings control how a score is calculated: how far back in time it looks, and how much each dimension and each container counts. Settings are defined on the datastore and can be overridden for individual containers. To change them, follow Quality Score Settings.

Settings Inheritance and Overrides¶

Datastore quality score settings define the decay period and dimension weights for the datastore score. They also act as the default for containers that do not have their own quality score settings. A container (table, file, computed table, computed file, or computed join) can override that inherited configuration with its own settings.

This means:

• Unconfigured containers inherit datastore settings: Changing the datastore decay period or dimension weights affects containers that do not have an override.
• Container overrides are local: Changing a container's settings affects that container, but it does not change the datastore settings or other containers.
• System defaults apply first: Until the datastore is configured, its score and containers without overrides use the system defaults (180-day decay, all dimensions at weight 1.0).
• Use overrides for exceptions: Configure the datastore once for the common policy, then add container settings only where the data needs a different decay period or dimension weighting.

graph TD
    DS["<b>Datastore Settings</b><br/>Decay: 90 days, Accuracy: 1.5"] --> DS_SCORE["Datastore Score<br/>(weighted avg of container scores)"]
    DS --> C1_SCORE["Container A Score<br/>(inherits datastore settings)"]
    C2["<b>Container B Override</b><br/>Decay: 30 days, Timeliness: 0"] --> C2_SCORE["Container B Score"]
    C1_SCORE --> DS_SCORE
    C2_SCORE --> DS_SCORE

In this example, the datastore uses a 90-day decay with a higher Accuracy weight. Container A inherits that configuration because it has no override. Container B uses its own 30-day decay with Timeliness disabled. Both container scores still contribute to the datastore's weighted average.

Decay Period¶

The decay period controls how far back in time Qualytics looks when calculating scores. By default, only anomalies, profiles, and scans from the last 180 days are included.

The decay period can be set from 7 to 180 days, with a default of 180 days.

This means:

• Old issues naturally age out: If an anomaly was detected 6 months ago and has not recurred, it no longer impacts the score.
• Recent issues weigh fully: Any anomaly within the decay window is fully counted.
• Shorter window (for example, 7 days): Focuses the score on recent quality activity. Use it when recent changes matter most.
• Longer window (for example, 180 days): Provides a broader historical perspective for trend analysis.

Dimension Weights¶

Each of the 8 dimensions has a configurable weight that controls its impact on the total container score:

• A weight of 1.0 (default) gives the dimension its full impact.
• A weight of 0 excludes the dimension from scoring: it is treated as fully healthy and cannot pull the score down.
• Weights between 0 and 1 proportionally reduce the dimension's influence.

Weights can be set from 0 to 2.0 (in 0.1 increments), with a default of 1.0.

This allows you to align the scoring system with your organization's data governance priorities. For example, if timeliness is critical for your use case, increase its weight. If volumetrics is less relevant, reduce it.

Tag Weight Modifiers¶

A tag can carry a weight modifier (-10 to 10). Each container's weight in the datastore score comes from the sum of the modifiers of the tags on that container, so containers with higher tag weights have more impact on the datastore-level quality score. See How Tags Affect the Datastore Score for the aggregate formula.

See Also¶