---
title: "Data Quality Studio: View Data Quality Status in Lineage"
url: "https://atlan.com/demos/data-quality-studio-view-data-quality-status-in-lineage/"
description: "Diagnose data quality issues faster! Trace unexpected dashboard values upstream to pinpoint failing tables and access full rule details with Atlan's lineage."
format: "Video"
video: "https://videos.ctfassets.net/nwa1c00rtgxb/rzs6sbrW1bAOjzaTnL94H/71f43acacb77016647cd09811005ed8d/View_Data_Quality_Status_in_Lineage.mp4"
target_persona: ["General", "Data Engineer", "Data Analyst", "Data Scientist", "IT Administrator", "Data Steward", "Data Governance Lead"]
journey_stage: ["S2 - Discovery", "C1 - Onboarding", "C3 - Adoption", "C4 - Expansion"]
use_case_context: ["Training"]
product: ["Data Marketplace - Data Quality"]
content_type: "video transcript"
transcript_source: "contentful"
---

# Data Quality Studio: View Data Quality Status in Lineage

Transcript of the video at https://atlan.com/demos/data-quality-studio-view-data-quality-status-in-lineage/

<!-- Body: machine transcript stored on the Contentful entry (field contentBody), timestamps kept, product names corrected. Not yet edited by a person. -->

## Viewing Data Quality Status in Lineage

Data quality is essential for building trust—but if it’s hidden away, it loses its impact. With Atlan’s Data Quality Studio and native lineage, you get full visibility across your data estate. Whether you’re upstream or downstream, data producers and consumers alike can trace lineage and see quality status right in the UI—no digging required.

Let’s see this from a common perspective, where we, an analyst, are reviewing some order information in a dashboard in Tableau. We notice that our buyer numbers seem to be off, as we know that our top buyer has over 155 purchases, as we saw last week, but now it is reporting as only 150. We open the Atlan Companion sidebar to get more information about the dashboard using Atlan’s Chrome extension, and see that Gene has put an announcement on the dashboard that there was a pipeline failure, but are unsure if it is impacting the data we are using. Clicking on the lineage tab and view graph takes us to Atlan and the Lineage UI. We use the lineage to expand up to see if any of the upstream tables also have an alert, and sure enough, there is a data source that has an issue. We click expand all to get all the upstream assets and see that there are two tables in Databricks feeding this data source that have rule failures, the Stock Dimension and Customer Dimension. Since our data issue is with customer purchasing, we focus first on the customer dimension table. We click on the view columns and immediately see that there is a failure, indicated by the red dot, on the buying group name field, possibly the source of our issue. Hovering over the red dot shows us the type of rule that is failing, which is a null count. If buying records and customer records have null counts, this may be what is throwing off our dashboard. We click on the view rule to be taken to the data quality page, where we see all the current failures, and sure enough, there are over 200 rows that are impacted by this null count. We click on the overview tab and see that Razi is the owner of the table. Now all we have to do is reach out to them to inquire on the impact and if that is what is causing our issue and the remediation plan. We could write an email, or through utilizing the native integrations with Slack and Teams, immediately contact them about the failure on the table, along with giving them the context of the issue and metadata around the table itself.

With Atlan’s Data Quality Studio and its automatically generated lineage, tracing the upstream source and downstream impact of a data quality issue is just a few clicks away. It’s a simple way to understand context and take action faster.