What is dimensional design?
A star schema organizes data around a business process, such as sales or inventory. A central fact table connects to descriptive dimension tables, giving people a clear way to explore business activity.
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Get the foundation right.
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DIMENSIONAL DESIGN
Get the foundation right.
Build analytics that last.
Don't build your data analytics on top of normalized schemas or operational data store (ODS)! Dimensional design (star schema) is the foundation of any BI solution. We analyze your business entities and processes, then design a structure that makes your data meaningful, consistent, and ready for analysis.
Dimensional Design
01
Understand the business
02
Design dimensions and facts
03
Validate before you build
HOW WE HELP
A sound schema. A stronger BI solution.
Design mistakes cascade into every layer: data integration, the data warehouse, semantic models, and reports. Getting the structure right early helps prevent incorrect totals, complex calculations, and costly rework.
Dimension tables: the business context
Dimensions describe the people, products, places, and dates involved in a business process. Attributes such as product category, customer region, and calendar year let users filter, group, and compare results.
Fact tables: the business activity
Facts record events or snapshots at a defined level of detail, called the grain. A sales fact table might hold one row per invoice line, with quantity and sales amount plus keys to the relevant dimensions.
Define the grain before the measures
Agree on exactly what each row represents before designing relationships or calculations. Mixing invoice totals with invoice lines can double-count sales; combining incompatible levels of detail can produce misleading results.
Design for consistency and history
Shared dimensions let teams compare processes using the same definitions of customer, product, and date. Plan how to track changes over time so historical results retain the business context they need.
Start focused. Build iteratively.
We work with business and source-system experts to identify core entities, clarify analytical requirements, and validate the schema against real questions. The design guides source mappings, integration, and the semantic model, then grows as new subject areas are added.
EXPERIENCE IN PRACTICE
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