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
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Introduction
Get an overview of the Data Quality Score, where it appears in the platform, and how it influences decisions.
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How It Works
See how the score composes across field, container, and datastore levels, the 8 quality dimensions, decay period, dimension weights, and what triggers a recalculation.
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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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Weighting
Understand how rule type, anomaly, and tag weights combine to determine check importance.
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Permissions
See which roles can view quality scores and edit scoring settings.
How-tos
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Settings
Configure the decay period and dimension weights for a datastore.
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View AI Explanations
Step-by-step tutorial for opening the AI-augmented Quality Score panel and acting on its Recommendations.