When evaluating and selecting data for building dashboards, what factors should be considered for ensuring data relevance and usefulness? (Select all that apply)

Question

Options

• AIgnoring data complexities for simplicity in visualization
• BEvaluating data based on business requirements
• CIncluding all available data for comprehensive visualization
• DFiltering data based on irrelevant attributes

• B
  94% (15)
• C
  6% (1)

Explanation

Selecting data for dashboards requires deliberate evaluation: B is correct because data must align with specific business requirements - dashboards exist to answer business questions, so only data that serves those goals belongs in the visualization. D is correct because filtering out irrelevant attributes (i.e., excluding fields that don't contribute to the analysis) keeps dashboards focused, reduces noise, and improves interpretability.

A is wrong because ignoring data complexities doesn't make them disappear - it leads to misleading visualizations and poor decisions. C is wrong because including all available data creates clutter, slows performance, and obscures the insights users actually need; more data is not inherently better.

Memory tip: Think of building a dashboard like packing for a trip - you evaluate what you need (B, matching requirements), leave out the junk (D, filter irrelevance), don't pretend your suitcase isn't heavy (reject A), and definitely don't pack everything you own (reject C).

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