# Datastore-Level Quality Scoring

Source: https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/calculation/datastore-level-scoring/

*Requires the Data Observability (DO) module.*

The datastore quality score sits at the top of the score hierarchy. It combines the [container scores](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/calculation/container-level-scoring/) of a source datastore into a single number, so you can compare data health across datastores at a glance.

## How the Datastore Score Is Calculated

The datastore quality score is a **weighted average** of all scored containers:

```text
Datastore Score = SUM(container_score × container_weight) / SUM(container_weight)
```

**Only containers with a score participate.** Containers that have not been both profiled and scanned are excluded from both the numerator and the denominator. See [Which Containers Get a Score?](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/calculation/container-level-scoring/#which-containers-get-a-score) for every container state.

## How Tags Affect the Datastore Score

Each container's weight in the aggregate formula is derived from its tags:

```text
container weight = sum(tag weight modifiers on this container), normalized so the minimum is at least 1
```

- A container with no tags has weight = 1 (default)
- Tags with positive weight modifiers increase a container's influence on the datastore score
- Tags with negative weight modifiers decrease a container's influence

**Example:** If you tag your 2 most important tables with a `Critical (weight: +10)` tag, each gets weight ~11 while untagged tables remain at weight 1. Your important tables now dominate the aggregate.

For the modifier range and when container weights are recalculated, see [Tag Weight Modifiers](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/calculation/score-settings/#tag-weight-modifiers).

## Historical Daily Scores

Daily datastore scores use the last 10 unique dates with quality score activity. For each date, each container's **latest score up to that date** is used (not just scores from that exact day), providing a continuous trend even when not all containers are scored daily.

---

## See Also

- **Container-Level Scoring**

  Learn which containers get a score, how the 70-point baseline model combines the 8 dimensions, and how timeliness and volumetrics are measured.

  [Container-Level Scoring](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/calculation/container-level-scoring/)
- **Field-Level Scoring**

  See how each field is scored on completeness, coverage, conformity, consistency, precision, and accuracy, with the formula behind each dimension.

  [Field-Level Scoring](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/calculation/field-level-scoring/)
- **Score Settings**

  Learn how datastore settings flow to containers, and how the decay period, dimension weights, and tag weight modifiers change the score.

  [Score Settings](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/calculation/score-settings/)
- **Interpreting Scores**

  See where scores appear, how they support decisions, and how to read them as a measure of fitness for purpose.

  [Interpreting Scores](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/interpretation/interpreting-scores/)
- **AI Explanations**

  Learn how the AgentQ-powered summary, per-dimension explanations, and Recommendations section render on the Quality Score panel.

  [AI Explanations](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/interpretation/ai-explanations/)
- **Examples**

  Walk through real scenarios: a container held down by two dimensions, a datastore score lower than expected, weighting critical tables, and a settings override.

  [Examples](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/examples/)
- **Best Practices**

  Keep scores meaningful: what to profile and scan, how many checks to add, when to change settings, and how to read the trend.

  [Best Practices](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/best-practices/)
- **Troubleshooting**

  Resolve missing scores, unexpectedly low values, settings and tag changes that do not apply, and missing AI explanations.

  [Troubleshooting](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/troubleshooting/)
- **Permissions**

  See which roles can view quality scores and edit scoring settings.

  [Permissions](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/permissions/)
- **How It Works**

  Learn what the quality score is, the key concepts behind it, the levels it is calculated at, the 8 dimensions it combines, and what triggers a recalculation.

  [How It Works](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/how-it-works/)
- **Rule Types by Dimension**

  Look up which rule types feed the Conformity, Precision, Accuracy, Volumetrics, and Timeliness dimensions, with a link to each one.

  [Rule Types by Dimension](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/calculation/rule-types-by-dimension/)

*Last updated: November 9, 2026.*