Introduction
The recent 30% drop in Databricks' Delta Lake adoption among enterprise customers is a significant concern that requires immediate attention. As we delve into this issue, we'll employ a systematic approach to identify, validate, and address the root cause, considering both short-term and long-term implications for our product strategy.
Our analysis will follow a structured framework, beginning with clarifying questions to establish context, followed by a thorough examination of external factors, product understanding, metric breakdown, and hypothesis formation. We'll then conduct a root cause analysis, propose validation methods, and outline a comprehensive resolution plan.
Framework overview
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Ensures we're working with accurate data. Expected answer: No changes in measurement methodology. Impact on approach: If changed, we'd need to reassess our baseline metrics.
Why it matters: Helps identify if the issue is sector-specific or widespread. Expected answer: Varied impact across sectors. Impact on approach: Would guide our investigation towards sector-specific factors if applicable.
Why it matters: Pricing changes could directly impact adoption rates. Expected answer: No major changes implemented. Impact on approach: If yes, we'd focus on pricing elasticity and competitive positioning.
Why it matters: Longer onboarding could delay adoption metrics. Expected answer: No significant changes to onboarding process. Impact on approach: If changed, we'd investigate onboarding efficiency and support resources.
Why it matters: New features or changes could impact user experience and adoption. Expected answer: Some updates, but nothing considered major. Impact on approach: If yes, we'd scrutinize the impact of these changes on user behavior and satisfaction.
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