When selecting and implementing an effective data model, what considerations are crucial for ensuring its suitability for BI requirements? (Select all that apply)

Question

Options

• AScalability and flexibility
• BConformity to specific database standards only
• CExtensive data denormalization
• DPerformance and ease of maintenance

• A
  88% (42)
• B
  8% (4)
• C
  4% (2)

Explanation

Scalability and flexibility (A) are essential because BI systems must handle growing data volumes and evolving business requirements without requiring complete redesigns. Performance and ease of maintenance (D) are equally critical since BI workloads involve complex queries across large datasets, and models that are difficult to maintain become bottlenecks as reporting needs change.

Why the distractors fail:

• B is wrong because effective BI data models should prioritize fitness-for-purpose over conformity to any single database standard - portability and adaptability matter more than vendor-specific compliance.
• C is wrong because extensive denormalization is not universally appropriate; while some denormalization improves query performance in dimensional models (e.g., star schemas), excessive denormalization creates redundancy, update anomalies, and maintenance nightmares.

Memory tip: Think "SP" - Scalable systems and Performant models win in BI. Anything that locks you into a single standard or blindly denormalizes everything will eventually break under real-world data growth or business change.

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