While a consolidated and clearly defined system landscape is an important prerequisite for data quality, appropriate processes, testing and validation rules are needed in operations and in everyday work with data to enforce data quality specifications.
Automation plays a key role in helping employees control the complexity of creating and managing product content while delivering the required quality at all times – across all channels. Appropriate rules could, for example, alert when product images do not meet the required formats in the desired output channel. Validation processes can also inform the responsible employees when certain data records are not complete or duplicates have been found that need to be resolved manually.
When creating data records, there is another set of checking mechanisms that help contributors to maintain the quality of the data created from the very beginning. The systems can define a certain value range for filling the attributes and prevent inadmissible number formats or descriptions in order to ensure data consistency and avoid costly cleanup at a later stage.
For such processes to be established, it is important to consider what cross-system data flows will look like at the beginning, who will be involved, and who will be responsible for the quality of the data. In addition, it is advisable to consider not only the current output channels and organizational structures, but also to keep in mind that changes in the process landscape might occur in the future.