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Analytics Leadership & Innovation Flashcards

7 cards from real CDO practice questions. Tap to flip, then mark Knew It or Still Learning โ€” missed cards come back until you master them.

Read the first 7 Analytics Leadership & Innovation flashcards as text
  1. Which concept treats curated datasets as reusable assets with defined owners, SLAs, and consumers?

    Answer: Data product

    Data products are managed like products, with ownership, quality guarantees, and a defined user base.

  2. A CDO is choosing between building a custom forecasting platform and buying a SaaS tool. Which factor most strongly favors building?

    Answer: The capability is a core competitive differentiator

    Building is justified when the capability creates unique, defensible competitive advantage.

  3. Which process model's phases are business understanding, data understanding, data preparation, modeling, evaluation, and deployment?

    Answer: CRISP-DM

    CRISP-DM is the widely used cross-industry standard process for data mining projects.

  4. What is the main purpose of an analytics sandbox environment?

    Answer: To let analysts experiment safely without affecting production systems

    Sandboxes give a safe space for exploration and prototyping isolated from production.

  5. An analytics team uses uplift modeling for a marketing campaign. What does uplift modeling estimate?

    Answer: The incremental effect of a treatment on each customer's behavior

    Uplift modeling predicts the difference in outcome caused by treatment versus no treatment for each individual.

  6. Which leadership action most effectively builds a data-driven culture?

    Answer: Executives visibly using data in decisions and rewarding evidence-based choices

    Culture change is driven by leaders modeling the behavior and aligning incentives with it.

  7. Which term describes business users who build analytic models with low-code tools but are not trained data scientists?

    Answer: Citizen data scientists

    Citizen data scientists use accessible tools to create models outside formal data science roles.

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