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Version: 2026.2

Data Quality Visualization

Once a data quality data type is added to a Data Object class, the computed score for each Data Object is visible:

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The screenshots on this page use the Car class's data quality rules from the reference demo. The class defines one data quality type per editor panel, each built from Symfony Expression Checks:

  • Texts Available (textsAvailable, Basedata panel): one Symfony Expression Check per localized text field, six in total (Name and Description, each in en, de and fr). The "Name (en)" check uses the expression object.getName('en') and the suggestion "Fill in name in English". A precondition, object.getObjectType() === 'actual-car', limits the rule to Data Objects of type actual-car, so it only scores objects representing an actual car.
  • Images Available (imagesAvailable, Media panel): verifies that images are present.
  • Attributes Available (attributesAvailable, Attributes panel): verifies that attributes are filled.
  • Sale Information Available (saleInformationAvailable, Sale Information panel): verifies that sale information is filled.

Data quality rules on the Car classData quality rules on the Car classData quality rules on the Car class

Data Object Editor​

Depending on the class definition, the computed data quality score(s) for a Data Object appear directly in its editor.

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Filling in new fields updates the scores only after reloading the Data Object. Changing the rules used to compute a score in the class definition likewise requires reloading the Data Object to see the updated score.

Each data quality type renders as a score bar at the top of its panel, showing the mark and the completion percentage, for example Texts Available: C, 33% completed at the top of the Basedata panel. Each panel's score bar is computed only from the rule definitions configured for that data quality type, so panels can show different marks on the same Data Object.

Data Quality Scores in EditorData Quality Scores in EditorData Quality Scores in Editor

In this example the Texts Available score is lowered because only its two English checks pass, while the German and French ones fail. The percentage is the sum of the passing checks' weights divided by the sum of all of them, so every failing check pulls it down and the mark drops below A.

The Data Quality Details tab lists every data quality score computed for the current Data Object, and the suggestion configured for each check next to a pass or fail marker (here, "Fill in name in German" for the missing German name).

Data Quality Details TabData Quality Details TabData Quality Details Tab

Data Object Grid​

Add data quality scores as columns in a Data Object grid through the grid's column configuration.

Data Quality Visualization Grid OptionsData Quality Visualization Grid OptionsData Quality Visualization Grid Options

Each added score becomes a read-only grid column. Sort by the column header, and filter by adding the column as a field filter in the Search & Filter panel, where each mark is a separate checkbox.

Data Score Filtering in GridData Score Filtering in GridData Score Filtering in Grid

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CSV and XLSX exports include only the scores, without color.

Filtering by data quality via the grid API​

Data quality columns also support the grid's general columnFilter mechanism (see the Studio Backend Grid documentation for the full filter contract). The dataQuality filter type restricts results to one or more score marks:

TypefilterValueOptionskey required
dataQualityarray of stringsA, B, C, Dtrue

key is the name of the data quality field to filter on. For example, to filter a textsAvailable data quality column for scores A and B:

...
"columnFilters": [
  {
    "key": "textsAvailable",
    "type": "dataQuality",
    "filterValue": ["A", "B"]
  }
]
...

Data Object Tree​

Enable Show in Tree in the class definition to display the available data quality scores (that is, scores meeting their precondition, if any) next to the Data Object name in the tree.

Data Quality Score in TreeData Quality Score in TreeData Quality Score in Tree

Objects that do not meet the data quality type's precondition carry no badge at all, which is why the model and variant nodes in the example above are unmarked while the individual cars below them are graded. Pimcore Studio dims the node icon of unpublished objects, but renders their score badges the same as published ones.

Style score badges with CSS classes; see Custom Score Badges.

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After changing the rules used to compute a score in the class definition, reload the tree (Refresh in the context menu) to show the updated scores.

Studio Dashboards​

Data quality scores can also be visualized in aggregate, across all objects of a class, using dashboard widgets. See Studio Dashboards.