When selecting data for building dashboards, which factors should be considered to ensure relevance and usability? (Select all that apply)

Question

Options

• AEvaluating data based on business requirements
• BFiltering data based on irrelevant attributes
• CIgnoring data complexities for simplicity in visualization
• DIncluding all available data for comprehensive visualization

• A
  90% (19)
• C
  5% (1)
• D
  5% (1)

Explanation

Selecting data for dashboards requires intentional curation, not collection. Option A is correct because aligning data selection with business requirements ensures dashboards answer real questions stakeholders need answered - irrelevant data creates noise, not insight. Option B is correct (interpreted as filtering out data that carries irrelevant attributes) because removing noise from your dataset sharpens focus and improves dashboard usability.

Option C is wrong - ignoring data complexities leads to misleading visualizations; complexities like outliers, missing values, or categorical nuances must be handled, not hidden. Option D is wrong because including all available data overwhelms users, increases load time, and obscures the key metrics that drive decisions - more is not better in dashboard design.

Memory tip: Think of a good dashboard like a good summary report - a skilled analyst doesn't dump every spreadsheet into a slide deck. They align to the ask (A) and trim the fat (B). The wrong answers both represent laziness in opposite directions: D is "include everything," C is "oversimplify everything."

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