When building a goal layer, do you follow any data modeling standards? How would you design diagrams to illustrate correlations between tables, and what is your typical approach to the goal layer?

A goal layer is the top‑level representation of business metrics that end users consume. I follow dimensional modeling standards such as star or snowflake schemas, and I use a data vault or lakehouse approach when the data volume and variety are large. First, I identify the key business facts (sales, revenue, user activity) and define fact tables that capture these measures. Then I create dimension tables (customer, product, time) that provide context. I use ER diagrams or data lineage tools to map relationships and ensure referential integrity. For the goal layer, I aggregate the fact tables into a gold layer, applying business logic, calculations, and data quality rules. I document the model with a data dictionary and expose it through a semantic layer (e.g., Power BI, Looker) so analysts can query without touching raw tables. This approach keeps the goal layer stable, reusable, and aligned with business KPIs.