Introduction
Identifying the decline in usage of Azure is a critical issue that requires a systematic approach to uncover the root cause and develop effective solutions. As we analyze this product issue, we'll follow a structured framework to identify, validate, and address the underlying factors contributing to the decline while considering both immediate and long-term implications for Azure's performance and market position.
To tackle this problem, I'll begin by asking clarifying questions to establish context, then rule out basic external factors. We'll dive deep into product understanding, break down relevant metrics, gather and prioritize data, form hypotheses, conduct root cause analysis, and finally propose validation methods and next steps. This approach will ensure we comprehensively address the Azure usage decline while demonstrating strategic product thinking.
Framework overview
This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development to address the decline in Azure usage.
Step 1
Clarifying Questions (3 minutes)
Why it matters: Different services may have unique factors affecting their usage. Hypothetical answer: Compute and storage services are seeing the largest drops. Impact: We'll focus our analysis on these core services first.
Why it matters: Helps distinguish between short-term fluctuations and long-term trends. Hypothetical answer: The decline has been consistent over the past quarter. Impact: We'll look for changes or events that coincide with the start of this period.
Why it matters: Identifies whether the issue is widespread or segment-specific. Hypothetical answer: Enterprise customers show a more pronounced decline. Impact: We'll investigate factors unique to enterprise environments and needs.
Why it matters: External factors could be driving the usage decline. Hypothetical answer: A major competitor recently launched a new AI service. Impact: We'll assess how this competitive move might be affecting Azure's usage.
Why it matters: Ensures we're comparing apples to apples in our data analysis. Hypothetical answer: No changes to measurement methods in the past year. Impact: We can rule out measurement inconsistencies as a cause.
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