In Online Analytical Processing (OLAP), what are analysts primarily interested in when performing multidimensional analysis across cubes, hierarchies, and aggregation levels? Select the single best answer.

Introduction / Context:
OLAP (Online Analytical Processing) supports interactive, multidimensional exploration of business data. Analysts slice, dice, drill down, and roll up to answer questions such as revenue by product, region, and time. Two foundational concepts drive OLAP: measures (numeric facts such as sales, cost, margin) and dimensions (business perspectives like time, product, geography, and customer) organized into hierarchies and levels.

Given Data / Assumptions:

• Environment: OLAP cubes or tabular models.
• Measures: additive/semi-additive facts aggregated across dimensions.
• Dimensions: descriptive attributes with hierarchies (e.g., Year > Quarter > Month).
• User goal: meaningful analysis that combines numbers with context.

Concept / Approach:

OLAP analysis requires both the quantitative side (measures) and the qualitative context (dimensions). Measures deliver magnitude; dimensions provide the “by what/where/when/which/who.” Levels (e.g., month vs. day) are simply positions within a dimension hierarchy—useful, but not the standalone focus.

Step-by-Step Solution:

Verification / Alternative check:

Any OLAP report grid (pivot table) displays measures in cells and dimensions on rows/columns/filters. Remove dimensions and the numbers lose context; remove measures and there is nothing to aggregate.

Why Other Options Are Wrong:

Levels only: levels are part of dimensions, not the full scope. Dimensions only: context without numbers yields no analysis. Measures only: numbers without breakdown lack meaning.

Common Pitfalls:

Confusing “levels” with the entire analysis; thinking a single dimension (like Time) is sufficient for all insights; ignoring measure aggregation behavior (additive vs. semi-additive).

Final Answer:

measures and dimensions