Which statement most accurately describes the purpose of data modeling for OLTP and OLAP?

• A. It supports data integrity, accurate reporting, and scalable querying
• B. It solely optimizes storage space
• C. It eliminates the need for normalization
• D. It is unnecessary for OLAP

Answer: (A)

Effective data modeling for OLTP and OLAP is about building structures that keep data accurate and usable as the system grows. It ensures data integrity by enforcing rules and constraints so related data stays consistent across the database. It supports accurate reporting by organizing data in a way that reports pull the right facts and measures without ambiguity. It also enables scalable querying: OLTP uses normalized designs to minimize redundancy and keep transactions fast and reliable, while OLAP uses dimensional models (like star or snowflake schemas) to speed up complex analytical queries on large data volumes. Together, these goals cover both trustworthy transactional processing and efficient analytics, which is why this option best describes the purpose.

The other ideas fall short because they focus narrowly on storage optimization, suggest eliminating normalization, or imply OLAP doesn’t need data modeling, all of which contradict how OLTP and OLAP rely on well-structured schemas to ensure data quality and performant access.