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Tutorial: Build Data Quality Visualizations

This tutorial walks you through creating visualizations for data quality metrics in ONE Reports. You’ll learn how to visualize overall quality, attribute-level quality, and rule-level quality using the built-in data quality datasets.

Before you start

When creating a visualization, you can select from pre-built data quality datasets under Data Quality results:

  • Catalog Items - Overall quality: Quality metrics aggregated at the catalog item level.

  • Catalog Items - Attribute quality: Quality metrics broken down by attribute within catalog items.

  • Catalog Items - Rule quality: Quality metrics based on specific rules applied to your data.

To ensure your visualizations always show the most recent evaluation results, use Display results by date.

Visualize overall quality

Use overall quality visualizations to monitor the health of your catalog items at a glance and identify which areas require attention.

This view is ideal for:

  • Executive dashboards showing data health across the organization

  • Tracking quality trends over time

  • Identifying catalog items that need remediation

  • Comparing quality metrics across different data domains

Create the visualization

  1. Go to ONE Reports > Visualizations and select Create visualization.

  2. In Select datasets, choose Data Quality results > Catalog Items - Overall quality.

  3. Select your collection and select Create a new visualization.

Configure dimensions and metrics

For a basic overall quality view:

Dimensions:

  • Catalog item name: Shows which catalog items are included.

  • Processing start time (optional): Enables tracking changes over time.

Metrics:

  • Failed records: Number of records that failed quality checks.

  • Quality ratio: Percentage of records passing quality checks.

  • Total records: Total number of records evaluated.

To track how quality changes across evaluation runs:

  1. Add Catalog item name and Processing start time to Dimensions.

  2. Add Failed records to Metrics.

  3. Select Bar Multiseries or Line Multiseries as the chart type.

This configuration shows the number of failed records per catalog item over time, making it easy to spot improvements or regressions.

Visualize attribute-level quality

Use attribute-level quality visualizations to assess data quality within specific catalog items and identify which attributes require remediation.

This view is ideal for:

  • Identifying problematic attributes within a catalog item

  • Comparing quality across attributes in the same dataset

  • Prioritizing data cleansing efforts

  • Detailed reporting for data stewards

Create the visualization

  1. Go to ONE Reports > Visualizations and select Create visualization.

  2. In Select datasets, choose Data Quality results > Catalog Items - Attribute quality.

  3. Select your collection and select Create a new visualization.

Configure dimensions and metrics

For a basic attribute quality view:

Dimensions:

  • Attribute name: Lists attributes within the catalog item.

Metrics:

  • Passed records: Number of records passing quality checks.

  • Failed records: Number of records failing quality checks.

  • Quality ratio: Percentage of records passing.

A Category chart works well for this view, displaying attributes as horizontal bars with color-coded passed and failed counts.

Example: Passed vs failed by attribute

To create a clear comparison of quality across attributes:

  1. Add Attribute name to Dimensions.

  2. Add both Passed records and Failed records to Metrics.

  3. Select Category chart as the chart type.

  4. In Visualization properties, enable Stacked to show passed and failed as portions of the total.

Use color coding to highlight the results: green for passed records and red for failed records.

Filter your visualization to focus on a single catalog item using visualization filters.

Visualize rule-level quality

Use rule-level quality visualizations to evaluate how specific data quality rules perform and identify which rules catch the most issues.

This view is ideal for:

  • Analyzing the effectiveness of individual DQ rules

  • Identifying rules that flag the most failed records

  • Understanding which rules apply to which attributes

  • Fine-tuning your data quality ruleset

Create the visualization

  1. Go to ONE Reports > Visualizations and select Create visualization.

  2. In Select datasets, choose Data Quality results > Catalog Items - Rule quality.

  3. Select your collection and select Create a new visualization.

Configure dimensions and metrics

For a basic rule quality view:

Dimensions:

  • Rule instance name: The specific rule being evaluated.

  • Attribute name (optional): Shows which attribute the rule applies to.

Metrics:

  • Failed records: Number of records failing this rule.

  • Total records: Total records evaluated by this rule.

A Category chart works well for displaying rule performance, with color coding to indicate the volume of failures per rule.

Example: Failed records by rule and attribute

To see which rules catch the most issues and on which attributes:

  1. Add Rule instance name and Attribute name to Dimensions.

  2. Add Failed records to Metrics.

  3. Select Category chart as the chart type.

This configuration shows each rule-attribute combination with the corresponding failure count, making it easy to identify problem areas.

Use Top/Bottom values in the Dimensions settings to focus on the rules with the highest failure counts.

Next steps

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