analytics

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Across
  1. 4. Analytics type that recommends optimal actions
  2. 7. Ability to understand context and relevance in question formulation
  3. 8. Analytics type focused on “what has already happened”
  4. 12. A trap where questions are obvious and add little value
  5. 13. Opposite of “trivial” insights — deep and revealing
  6. 14. The ultimate superpower in data science
  7. 15. A question that assumes an answer without evidence
Down
  1. 1. Stage where conclusions are drawn and shared
  2. 2. First step in the data scientific method
  3. 3. Analytics type that attempts to forecast future outcomes
  4. 5. A core activity in the analytical stage
  5. 6. Turning vague goals into clear objectives
  6. 9. Misconception that tools determine the questions
  7. 10. Often mistaken as the primary goal of data science, but it’s not
  8. 11. What may be required if a question is ill defined