When implementing data processing solutions, how does aggregating and enriching data contribute to the overall data analysis process?

Question

Options

• ALimits data correlation possibilities
• BEnhances data granularity
• CIncreases data redundancy
• DSlows down data retrieval speed

• A
  4% (2)
• B
  91% (41)
• C
  2% (1)
• D
  2% (1)

Explanation

Aggregating and enriching data enhances data granularity (B) because these processes transform raw, fragmented data into more meaningful, detailed, and contextualized insights - combining records from multiple sources adds depth and precision that enables finer-grained analysis. Option A is wrong because aggregation and enrichment actually expand correlation possibilities by linking related data points across datasets. Option C is incorrect because the goal is to produce richer, more useful data, not duplicate it - redundancy implies storing the same data repeatedly without added value. Option D is also wrong; while enrichment adds processing overhead upfront, the result is typically faster and more efficient retrieval since pre-processed, consolidated data reduces the need for complex queries at read time.

Memory tip: Think of enrichment like marinating meat - you add ingredients (extra data) to make the result richer and more detailed (granular), not to copy or slow anything down.

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