If a feed is generating a low DQS for product name, for example, the platform will recognise the problem and will recommend ways to fix it. If the product name is missing colour, for example, the data quality tab will offer to add colour to the beginning of each product name (where it doesn't already exist).
The DQS of a feed is based on the health of attributes that are specific to the industry in which it operates: in fashion, that's colours, materials and sizes; in electronics energy efficiency ratings and mpn values; and in furniture, it's more about fabrics and size.
The DQS recalculates the quality of every attribute, every day, and displays it in an easy-to-access panel at the top of the UI. The accompanying data quality tab provides an analysis of any data health issues and recommends a way to fix them automatically. For example, by inserting a missing attribute.
The DQS lets you view the health of every individual attribute. When a data quality issue arises, the data quality wizard will offer to fix the problem by applying a rule to every product in the feed. For example, by adding colour to the beginning or end of every product title where the colour is missing.