Data Strategy, Value and the Data Portfolio

Review questions

Data Strategy, Value and the Data Portfolio — Integrated Summary

This module integrates Translate enterprise outcomes into data theses, and Evaluate a portfolio of data domains and products, and Build the investment case and stage funding. Judgment starts with purpose and decision, then moves to rights, evidence and measurement. A high score cannot compensate for a mandatory constraint, and activity does not demonstrate value without an outcome and guardrails. The cases use the thesis map, portfolio decision record, confidence-bounded investment case to connect alternatives to risk and review. Transferable application states what would change the decision, who owns it, and when an exception ends or scope expands.

Review questions

Test your understanding with these questions. Marking and saved results are available with a free account.

  1. Question 1Translate enterprise outcomes into data theses: Which intervention addresses the cause rather than the symptom?
  2. Question 2Evaluate a portfolio of data domains and products: Which intervention addresses the cause rather than the symptom?
  3. Question 3Build the investment case and stage funding: How should the sample be interpreted before scaling?
  4. Question 4Separate governance from management and assign decision rights: Which intervention addresses the cause rather than the symptom?
  5. Question 5Design data ownership, stewardship and accountability: How should the sample be interpreted before scaling?
  6. Question 6Manage data products and contracts across the lifecycle: Which intervention addresses the cause rather than the symptom?
  7. Question 7Design fitness-for-use quality and defect economics: Which intervention addresses the cause rather than the symptom?
  8. Question 8Operationalize metadata, lineage and business terms: Which intervention addresses the cause rather than the symptom?
  9. Question 9Govern access, sharing and retention by purpose: How should the sample be interpreted before scaling?
  10. Question 10Choose a centralized or federated operating model: How should the sample be interpreted before scaling?
  11. Question 11Turn principles into testable policies and controls: Which intervention addresses the cause rather than the symptom?
  12. Question 12Build a capability roadmap and behavioral change: Which intervention addresses the cause rather than the symptom?
  13. Question 13Measure maturity and performance without cosmetic scores: How should the sample be interpreted before scaling?
  14. Question 14Demonstrate value and manage benefits and risk: Which intervention addresses the cause rather than the symptom?
  15. Question 15Lead an integrated data-council case: Which intervention addresses the cause rather than the symptom?