Description: Currently, the dashboard filtering for text-based dimensions (such as Order Tags) is limited to "is any of", "is none of", "contains", and "does not contain". While these cover basic needs, they lack the precision required for managing structured data. For instance, when using prefixes like saleschannel_, users cannot efficiently isolate groups of tags that share a common starting string without manually selecting every variation or risking "false positives" that might occur with a broad "contains" filter. Key Requirement: Expand the available filtering operators to include: Starts with: To capture tags sharing a common prefix (e.g., saleschannel_). Ends with: To capture tags with common suffixes. Is empty / Is not empty: To identify records where tags are missing or present, regardless of the text value. Matches exactly: To ensure a 1:1 match without including partial strings. Value: This increases the speed and accuracy of segmenting data. It allows users to build dynamic reports that automatically include new tags as long as they follow a defined naming convention. It eliminates manual maintenance of filters and ensures that dashboard views remain scalable as the business's tagging taxonomy grows.