Data Quality Dashboard¶

Alation Cloud Service Applies to Alation Cloud Service instances of Alation

For instructions on accessing the dashboard, see Access the Data Quality Features

Alation Data Quality dashboards gives a comprehensive view of the overall health of your data assets. The dashboards are categorized as:

Overview
Monitors
Assets
Incidents

Overview Dashboard¶

The Overview section of the Alation Data Quality dashboard displays the overall health scores of your monitored data assets based on the AI-recommended, manual checks, or custom-created checks either using SQL or common table expressions (CTE) logic.

The overview dashboard displays the following components:

Data Quality Score
Incidents
Monitoring Overview
Anomaly Metrics
Data Quality Score Card
Failing Monitors
Data Quality Checks

Data Quality Score¶

The Data Quality Score helps you prioritize remediation and build trust in your governed data assets. It reflects the overall quality of monitored data across your catalog. It provides a quick overview of how many quality checks are passing successfully and is updated daily or weekly as scheduled to reflect the most up-to-date quality status.

A higher score indicates better data quality and fewer issues detected, while a lower score highlights potential data reliability concerns that may require attention.

The Data Quality Score is designed to help you quickly assess whether data meets expectations across multiple quality dimensions like completeness, uniqueness, validity, and freshness.

The quality score appears on the following pages:

Catalog pages

Data Quality dashboards

Alation Chrome Extension

The health score is an indicator of the number of active checks that have passed over the total number of enabled active checks indicating the following:

Executed checks include only those with results of pass or fail.

All checks contribute equally.

Score Calculation¶

Health Score = (Number of Passed Active Checks / Total Number of Enabled Active Checks) x 100

Score Interpretation Guidelines¶

90-100%: Excellent data quality with minimal issues (Green)
70-89%: Good quality with some attention required (Yellow)
Below 70%: Poor quality requiring immediate attention (Red)

Example¶

Consider tables customer_profiles and orders with 5 data quality checks:

Table	Check	Result
customer_profiles	missing_percent(email) < 2%	Fail
customer_profiles	duplicate_count(customer_id) = 0	Pass
customer_profiles	freshness(updated_at) < 1d	Pass
orders	row_count > 1000	Pass
orders	invalid_count(phone) = 0	Pass

Data Quality Score = (4 / 5) x 100 = 80%

To determine action, consider the following:

Warning or Medium Score (75-89%): Some checks have failed

Failing or Low Score (<75%): Significant issues are detected in the monitored data

0% Score: All checks have failed and requires an immediate review

Incidents¶

Available from version 2025.3.2

This section displays active and closed incidents with an average time duration that all the incidents took.

Anomaly Metrics¶

Available from version 2025.3.2

This section displays the count of overall healthy assets, detected anomalies, and total count.

Another similar section displays the following supported anomalies at the table and column level:

Table-level Anomalies¶

Row Count
Freshness
Schema Drift

Column-level Anomalies¶

Duplicate Count
Missing Count
Maximum
Minimum
Average
Standard Deviation

You can create and review anomalies from the Monitors page. If you have added an anomaly during monitor creation, you can review or add more anomalies from the monitors page.

For more information, see Manage Monitors and Monitor Data Anomalies.

Monitoring Overview¶

This section displays how many data assets such as tables, columns, and BI reports are being monitored.

Data Quality Score Card¶

The Data Quality Score Card shows a breakdown of the overall data quality Health Score by metric. Each metric includes a pass rate of the relevant checks and a trend graph to show how score changes over time. This card helps you identify which data quality dimensions are weakest and how they’re improving.

You can view the data quality score card for all the assets on the dashboard.

This table lists all the metrics used in the score card with descriptions:

Metric	Description
Timeliness	Checks related to data freshness
Uniqueness	Duplicate or unique value validations
Accuracy	Logical/business rule compliance
Completeness	Non-null or required field validation
Validity	Format/pattern checks
Consistency	Stable schema or values across datasets
Custom	CTE or SQL checks tailored to business

View Failing Monitors¶

The Failing Monitors section provides a quick view of monitors that have one or more failed checks in their latest run. This summary helps in quickly identifying and prioritizing remediation of data quality issues.

This table lists monitors with failed checks from their most recent execution.

Column Name	Description
Monitor Name	Clickable link to the specific monitor’s detail page
Assets Monitored	Number of tables included in this monitor
Checks	Total number of active checks in the monitor
Status	Result of the latest monitor run (e.g., Fail)
Failure Rate	Percentage of failing checks from the last run
Last Run	Timestamp of when the monitor was last executed
Next Run	Timestamp of the next scheduled execution

Interpret Failure Rate¶

The Failure Rate represents the proportion of checks that failed in the most recent monitor run.

This is calculated as:

Failure Rate = (Number of Failed Checks / Total Checks in Monitor) x 100

Example:

A monitor with 6 total checks and 4 failures will show:

Failure Rate = 66.67%

View Data Quality Checks¶

Each check returns one of three results over a scheduled period:

Pass: Click to see which values fall within configured thresholds.

Fail: Click to see which values exceed thresholds

Error: Click to see which checks could not execute

View All Monitors¶

The monitors dashboard provides access to a comprehensive monitor administration with a searchable and filterable view. At the beginning, you can view the total assets monitored such as table, column, and BI reports.

Navigate to the Alation Data Quality application and click Monitors.
Filter monitors by Status using the dropdown to view All Monitors or only Monitors with Failures.
(Optional) Search by a monitor name or sort by quality score, failure count, or last execution time.
To understand which monitors require the most attention, review the following options based on the data quality score for each monitor:

Field	Description
Monitor Name	A unique name identifying the monitor.
Tables Monitored	The number of tables included in the monitor.
Data Quality Score	The calculated health score based on active check outcomes on each monitor.
Checks Count	Total number of checks configured in the monitor.
Checks Failed	Number of checks that failed in the most recent run.
Status	Overall status based on check failures such as pass or fail.
Last Run / Next Run	Timestamps of the previous and upcoming scheduled monitor executions
Created By	The user who created the monitor.

For more information, see Manage Monitors.

View All Assets¶

The assets dashboard provides a centralized view of all data assets currently under quality monitoring.

Navigate to the Alation Data Quality application and click Assets.
Search for specific tables using names or sort either by data quality score or by number of failed checks.
To understand which assets require the most attention, review the following options based on the data quality score for table:

Field	Description
Table	Name and source path of the monitored table
Data Quality Score	Health score based on all checks across the table
Columns Monitored	Number of columns in the table with at least one data quality check
Monitors (Count)	Number of different monitors the table is part of
Checks (Count)	Total number of checks applied to this table across all monitors
View Details	Link to access full table detail page with check results, history, and configuration

Click View Details to get detailed information about the object names, the type of checks applied, the check definition, observed values against each check, status, their last runtime, and status messages.

View All Incidents¶

The incident dashboard allows you to track incident status, priority, and resolution, with built-in links to external ticketing systems like Atlassian Jira.

Navigate to the Alation Data Quality application and click Incidents.
Filter incidents by their status using the dropdown to view Show All, Open Incidents, or Closed Incidents.
To triage incidents, review the following details:

Field	Description
Incident Name	A unique, brief title or identifier for the data quality incident.
Description	A brief summary providing more context about the incident or the data issue.
Status	The current stage of the incident in the resolution workflow (e.g., “Open,” “Resolved,” “Waiting For Triage”).
Priority	The assigned urgency level to help prioritize work (e.g., “Critical,” “High,” “Minor”).
Date Created	The timestamp indicates when the incident was first created or detected.
Days Open	A calculated number of days the incident has been active (since “Date Created”) to track its age.
Ticket Link	A direct link to an associated ticket in an external system, such as Jira, for issue tracking.
Link to Monitor	A direct link to the specific data quality monitor that triggered this incident.

Click on an incident name to view details about it.

For more information, see Manage Incidents.