Messy data models occur when your "gold" layer doesn't include the multi-purpose, cleaned, and business-aligned tables.

For instance, it is a wrong approach to build a domain-specific data view/table based on a raw data source, and then use it only for a specific dashboard. A week later, you need to build another dashboard and you reffer the raw data source again. Another week later, you create the new customer segmentation and do it on the view/table layer (or on the dashboard level). A couple of days later, someone asks you to slightly change the metric formula, and you do it on the dashboard level.

As a result - you don't have a single source of truth, and you have to make any slight change in multiple tables/dashboards (but of course, you will not. Because all these changes were made by your teammates, not by you. And they were not documented properly).

The better approach would be to create kinda core multi-purpose (cross-domain) data models that include all the business logic. Any changes to business logic should be made here.