In D365 Commerce, product attributes are commonly used to store information that is shown or used for filtering in eCommerce. These attributes are linked to the product (not the released product) and can support multiple languages.

For background on attributes, see Microsoft documentation.

How Attribute Values Are Stored

The data model behind attributes is quite complex. It follows a normalized database structure (Third Normal Form), which ensures clean and consistent data.

For text-based attributes, two main tables are involved:

• EcoResValue → stores the actual attribute value
• EcoResTextValueTranslation → stores translations of that value

How Search Uses Attribute Values

Azure AI Search primarily uses values from:

• EcoResTextValueTranslation

If no translation exists, it falls back to the system language (typically en-us).

However:

• If no translation record exists at all, the attribute value may not be indexed or searchable

The Core Issue

There is a method in the system intended to create a default translation:

But in practice:

• This method is never automatically called
• The EcoResTextValue table does not even have an insert method

This means that when a new attribute value is created:

• No translation record is created
• Azure AI Search may therefore not pick up the value

Manual Workaround

When opening attribute values from a product, you will see a “Translate” button:

Using this button:

• Creates the required record in EcoResTextValueTranslation
• Ensures the value is available for search

Important detail:

• If you update an existing attribute value, the translation record is updated correctly
• The issue only occurs during initial creation

Status

This behavior has been reported to Microsoft. A fix is expected, but no confirmed timeline is available.

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Summary – Customer Impact

This issue affects how product attributes appear in search and filtering:

• Attribute values may not be searchable in eCommerce
• Filters based on attributes may not work as expected
• Missing translations can lead to inconsistent or incomplete product data in storefront

What this means for customers:

• Products may be harder to find
• Filtering and navigation can be unreliable
• Additional manual steps (translations) are required to ensure correct behavior