A data mart is a subject-area-focused analytical data store, scoped to a specific business domain or user community — a finance data mart containing GL actuals, budget, and consolidation data; a sales data mart containing pipeline, order, and revenue recognition data; an HR data mart containing headcount, payroll cost, and attrition data. It may be a physically separate database from the enterprise data warehouse, or a logically partitioned schema within it. The defining characteristic is not the physical implementation but the scope: a data mart is narrower than a full enterprise data warehouse, serves a defined consumer group, and contains the dimensional model and semantic layer designed specifically for that domain’s reporting requirements.
How It Works
Data marts can be constructed in two architectures. In the dependent data mart pattern — the recommended approach — the enterprise data warehouse is the single source of integrated, conformed data; data marts are read from the warehouse and shaped for specific consumer needs. In the independent data mart pattern, each data mart is populated directly from source systems without a central warehouse layer. Independent data marts are faster to build and are common in organisations that started automation with departmental BI before enterprise BI strategy was established. They produce divergent definitions of shared metrics — finance and sales calculating revenue differently, HR and finance calculating headcount cost differently — that undermine cross-domain reporting.
Design Decisions and Trade-offs
The finance data mart design must resolve three organisational tensions. First, speed versus consistency: a finance-only mart built directly from the ERP can be delivered faster than waiting for the enterprise warehouse to be populated with conformed dimensions, but it creates a data silo that is expensive to integrate later. Second, granularity: the finance mart’s fact table grain (journal line versus monthly total versus entity-period summary) determines what drill-through is available to the finance consumer — a trade-off against storage and query cost at very fine grain. Third, security: the finance data mart frequently contains information that must not be accessible to other business domain consumers — detailed payroll costs, management fee allocations, uncommitted budget scenarios. The data mart’s access control model must be designed to provide the right consumers access to the right data without exposing sensitive finance data to wider BI consumer populations.
Common Implementation Errors
The implementation error most specific to finance data marts is inconsistent conformed dimension definitions between the finance mart and other domain marts. When the finance mart’s Entity dimension uses a different key structure and naming convention from the HR mart’s Entity dimension, cross-mart analysis — comparing headcount cost in HR data to operating expense in finance data, at the entity level — requires a mapping table and manual reconciliation. Conformed dimensions — shared dimension definitions that are identical across all data marts consuming from the same enterprise warehouse — are the architectural component that enables cross-domain analysis. Building a finance data mart without enforcing conformed dimension alignment with other domain marts creates an integration debt that is expensive to pay back when the business wants cross-domain reports.
How Loop Wise Solutions Designs for This
We design the finance data mart as a dependent mart from the enterprise warehouse where one exists, and we design shared conformed dimensions — Entity, Time, and Cost Centre at minimum — as warehouse-layer objects that all domain marts consume, rather than allowing each mart to define its own version. Where the enterprise warehouse does not yet exist and the finance mart must be built independently, we document the conformed dimension design as the target state for future integration.
