Mastering Fact & Dimension Tables with Medallion Architecture for BI

Every organisation collects transactional data by the thousands each day. Most of it stays unused because raw data on its own rarely tells a clear story. Business intelligence only works when the underlying data has been cleaned, structured, and prepared with intent.

This session traces that journey. It looks at how scattered, raw data becomes a functioning data warehouse through the Medallion Architecture, a layered method now common among data teams that need dependable reporting at scale.

Data typically enters from several source systems at once. It gets cleaned, standardised, and eventually organised into a star schema built on fact and dimension tables. That structure is what allows dashboards to load fast and numbers to stay trustworthy.

Technology leaders, DBAs, infrastructure architects, and business decision-makers will find the session useful if they are trying to figure out why their current reporting feels slow, inconsistent, or hard to trust.

• Reporting accuracy
• Query performance
• Cross-team data consistency
• Scalability across growing datasets

Skip the layering, and these are usually the first things to break.

• Why a centralised data warehouse matters for reporting that teams can actually rely on
• How the Bronze, Silver, and Gold layers each improve data quality along the way
• What fact and dimension tables actually do, and why BI tools depend on them
• How the same underlying data ends up supporting both day-to-day monitoring and longer-term business strategy

A layered approach to data warehousing pays off well beyond cleaner pipelines. It shows up directly in reporting teams can trust, in analytics that hold up as AI adoption grows, and in infrastructure that doesn't buckle when data volumes increase.