What factors should be considered when evaluating which transformations are required in data discovery? (Select all that apply)

Question

Options

• AData consistency
• BBusiness use cases
• CData redundancy
• DData normalization

• A
  88% (28)
• C
  13% (4)

Explanation

Evaluating required transformations in data discovery means assessing what changes are needed to make raw data usable and fit for purpose. Data consistency (A) must be considered because inconsistent formats, naming conventions, or data types signal exactly which standardization transformations are needed. Business use cases (B) are essential because the intended purpose of the data directly dictates what shape, format, or structure transformations must produce. Data normalization (D) is itself a transformation category - evaluating whether data needs to be normalized (e.g., standardized structures, eliminating anomalies) is core to the discovery process.

Data redundancy (C) is the distractor - while redundancy is a data quality symptom, it is addressed by normalization (D) rather than being an independent factor that guides transformation decisions. Including it would be double-counting what normalization already covers.

Memory tip: Think "CBD minus R" - Consistency, Business use cases, and normalization Determine transformations; Redundancy is the result normalization fixes, not a separate evaluation factor.

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