Datastore Quality Score Introduction

What is a Data Quality Score?

A Data Quality Score is a measure from 0 to 100 that reflects recorded quality signals at the field, container, and datastore levels. Higher scores indicate stronger measured results under the applicable scoring settings. Qualytics recalculates scores when relevant Profile and Scan operations or quality events change scoring inputs, then records the results over time so you can review trends.

For the mechanics behind the score, see How Datastore Quality Scores Work.

AI-augmented panel

When AgentQ is configured for your workspace, the Quality Score side panel adds an AI-written summary of the score's main drivers, per-dimension AI explanations, and a Recommendations section for dimensions scoring below 50. A sparkle icon next to every Quality Score chip signals that these AI surfaces are available. See AI Explanations and Recommendations for the full anatomy of the panel and each indicator, and View AI Explanations and Recommendations for the step-by-step tutorial.

Where to See the Score

The quality score is visible across the platform without needing to navigate to a dedicated page:

• Source Datastores listing page: Each datastore row displays its current quality score.
• Datastore detail page: The score appears in the Totals panel at the top of the Overview tab.
• Tree view: Quality score indicators are shown next to each datastore in the sidebar.
• Container detail page: Each table or file displays its own container-level score.

How Scores Influence Your Datastore

The quality score gives data stewards a shared view of measured data health. Compare totals across assets only when their decay periods and dimension weights are comparable, and review the underlying dimensions and settings alongside each score. You can use it for:

• Prioritizing investigation: Compare scores, then drill down to the dimensions, containers, and fields contributing to a lower result.
• Tracking improvement: After addressing a data issue and running the relevant operation again, review how the newly recorded score changes the trend.
• Communicating data health: Use scores and dimension details to explain measured quality and changes to stakeholders.
• Reviewing recent results: Scores update when relevant operations or quality events change their inputs, so the latest score reflects the most recently recorded signals.

Next Steps