Getting Started with Datastore Quality Score¶
Data Quality Scores give data stewards an at-a-glance measure, from 0 to 100, of data health across datastores, containers, and fields in Qualytics. Scores are recorded over time and recalculated as relevant Profile and Scan operations complete or other quality inputs change. Eight quality dimensions show where data is meeting expectations and where it needs attention, helping you prioritize investigation and communicate changes to stakeholders.
In this section you will learn how scores are calculated, how to configure scoring settings, how the AI-augmented panel surfaces explanations and recommendations, and how to use scores to monitor data quality. Any user with the Member role and at least the Reporter team permission on the datastore can view scores; editing score settings requires the Editor team permission.
Deep Dive¶
The concepts behind the score, grouped by theme.
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Introduction
See what the deep dive covers and where to start, from the score's concepts to its calculation, interpretation, and settings.
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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.
Calculation¶
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Rule Types by Dimension
Look up which rule types feed the Conformity, Precision, Accuracy, Volumetrics, and Timeliness dimensions, with a link to each one.
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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.
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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.
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Field-Level Scoring
See how each field is scored on completeness, coverage, conformity, consistency, precision, and accuracy, with the formula behind each dimension.
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Score Settings
Learn how datastore settings flow to containers, and how the decay period, dimension weights, and tag weight modifiers change the score.
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Tag Weights
Understand how tag weight modifiers decide how much each container and field counts in the score.
Interpretation¶
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Interpreting Scores
See where scores appear, how they support decisions, and how to read them as a measure of fitness for purpose.
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AI Explanations
Learn how the AgentQ-powered summary, per-dimension explanations, and Recommendations section render on the Quality Score panel.
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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.
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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.
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Troubleshooting
Resolve missing scores, unexpectedly low values, settings and tag changes that do not apply, and missing AI explanations.
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Permissions
See which roles can view quality scores and edit scoring settings.
How-tos¶
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Configure Score Settings
Configure the decay period and dimension weights for a datastore or one of its containers.
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View AI Explanations
Step-by-step tutorial for opening the AI-augmented Quality Score panel and acting on its Recommendations.