A data mart is smaller than a data warehouse but is intended to serve a specific department or subject area, not the entire enterprise. Is this statement accurate?

Introduction / Context:
Data marts are targeted analytical stores that address a specific business function such as Sales, Finance, or Supply Chain. They are typically subsets of an enterprise data warehouse or curated directly from trusted sources to meet focused analytical needs.

Given Data / Assumptions:

• Scope is limited to a subject area or department.
• Size is smaller than an enterprise-wide warehouse.
• They may be physical or virtual (semantic views over a warehouse).

Concept / Approach:
Data marts simplify user access and improve performance by tailoring models and aggregations to specific workloads. They commonly use star schemas and may include precomputed measures aligned with departmental KPIs. While they can be standalone, modern practice encourages conformance to enterprise dimensions to maintain consistency.

Step-by-Step Solution:
Identify departmental analytics needs and KPIs.Conform dimensions with enterprise definitions to ensure cross-mart consistency.Build a subject-specific schema and pre-aggregations.Expose the mart via BI tools for dashboards and self-service analysis.

Verification / Alternative check:
Review governance documentation: data marts map to business domains rather than the entire enterprise scope, which is the data warehouse’s role.

Why Other Options Are Wrong:
Claiming marts cover the whole enterprise contradicts their purpose (option b). Virtualization does not change their scope (option c). “Transactional data mart” is a misnomer (option d). Staging areas are upstream, not consumer-facing analytics stores (option e).

Common Pitfalls:
Creating siloed marts with conflicting definitions; skipping conformed dimensions; building marts directly from OLTP without necessary cleansing or harmonization.

Final Answer:
Accurate — data marts focus on a department/subject