# Datastore Quality Score Troubleshooting Source: https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/troubleshooting/ *Requires the Data Observability (DO) module.* This page documents common quality score problems and the steps to resolve them. Each section describes a symptom, its cause, and the resolution. ## Missing Scores ### The Datastore Has No Quality Score A datastore shows no quality score on the Datastores listing page or its Overview tab. **Cause:** None of its containers has a score yet, or the datastore is an enrichment datastore. Datastore-level quality scores are offered only for source datastores. **Resolution:** For a source datastore, run **Sync**, then **Profile**, then **Scan** on the tables you want to monitor. Enrichment datastores do not get a datastore quality score. ### A Container Shows `-` Instead of a Score A table or file displays `-` where its quality score should be. **Cause:** A container receives a score only after it has a completed profile and a completed scan. Containers showing `-` are excluded from the datastore score, so they do not pull it down. **Resolution:** Run a Profile and a Scan that include the container. 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). --- ## Unexpected Values ### The Datastore Score Is Much Lower Than Expected The datastore score is far below the scores of the tables you monitor. **Cause:** Tables that were profiled and scanned but have no checks receive very low scores, and every scored table counts in the datastore average. This usually follows a datastore-wide Profile and Scan during setup. **Resolution:** Run operations only on the tables you intend to monitor, or use tag weights so those tables dominate the average. See the [low datastore score example](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/examples/) for every option. ### The Score Does Not Improve After Fixing the Data You fixed the source data, but the score stays the same. **Cause:** Qualytics does not know the data is fixed until a scan confirms it, and old anomalies count until they leave the decay window. Another dimension, or a dimension with a high weight, may also be holding the score down. **Resolution:** 1. Run a Scan so the anomalies are re-evaluated. 2. Open the dimension breakdown on the container detail page and look for the dimensions that still score low. 3. Review the [dimension weights](https://userguide.qualytics.io/source-datastore/data-quality-score/how-tos/quality-score-settings/) for a dimension that dominates the score. ### Coverage Keeps Dropping The Coverage dimension of a container goes down over time even though no checks were removed. **Cause:** Coverage rewards checks that scans assert often and recently. Checks left out of your scans, for example when scans assert only [selected checks](https://userguide.qualytics.io/operations/scan/how-tos/select-check-categories/), count less as time passes. **Resolution:** Pair targeted scans with a regular scan of all active checks. --- ## Settings and Tags ### A Settings Change Did Not Change the Score You changed the decay period or dimension weights, but the displayed score is the same. **Cause:** Settings changes apply to new scores only. Already-recorded scores keep the settings that were in effect when they were measured. **Resolution:** Wait for the next scan, profile, or other event that recalculates the score. See [What Triggers a Recalculation?](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/how-it-works/#what-triggers-a-recalculation). ### Tagging a Container Did Not Change Its Weight You added a weighted tag to a container, but the datastore score did not shift. **Cause:** Container weights are recalculated only when tags are added to or removed from the **datastore**, or when you use **Move to**, **Merge**, or **Remove** on a tag's page. A tag assigned directly to a container does not change its weight until one of those happens. **Resolution:** Change the datastore's tags, or use one of those actions on the tag's page, to recalculate the weights. See [Tag Weight Modifiers](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/calculation/score-settings/#tag-weight-modifiers). --- ## AI Explanations ### The Sparkle Icon and AI Explanations Are Missing The Quality Score panel opens without the AI summary, and no sparkle icon appears next to the score. **Cause:** AgentQ is not configured for the deployment. Without it, the panel shows the score, dimensions, and static descriptions, but none of the AI-written content. **Resolution:** Ask a user who can manage integrations to configure AgentQ. See [When AgentQ is not configured](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/interpretation/ai-explanations/#when-agentq-is-not-configured). --- ## See Also - **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/) - **Datastore-Level Scoring** See how container scores roll up into the datastore score, how tags change each container's weight, and how daily history is built. [Datastore-Level Scoring](https://userguide.qualytics.io/source-datastore/data-quality-score/deep-dive/calculation/datastore-level-scoring/) - **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/) *Last updated: November 9, 2026.*