Root Cause Analysis in Claude Code

Hosted by Shane Butler, Sravya Madipalli, and Hai Guan

Wed, May 20, 2026

7:00 PM UTC (1 hour)

Virtual (Zoom)

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237 students

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Build AI Analysts in Claude Code
Shane Butler, Sravya Madipalli, and Hai Guan
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What you'll learn

Decompose metric drops into root causes

Use Level 0-to-N breakdown to go from 'revenue dropped' to the specific segment and cause driving the change.

Run a full decomposition in Claude Code

Watch Claude Code peel back layers of a metric drop to find the exact driver. No SQL, no pivot tables.

Build a root cause template for your metrics

Leave with a reusable decomposition prompt that works on any metric drop you need to investigate.

Why this topic matters

When a metric drops, most people either panic or guess. They check a few dashboards, pick whatever looks off, and report that. This teaches a structured decomposition: start at the top, break by dimension, drill into the segment that moved, repeat until you find the specific cause. Claude Code runs the full drill-down live.

You'll learn from

Shane Butler

Principal Data Scientist, AI Evaluations at Ontra

Shane Butler is a Principal Data Scientist at Ontra, where he leads evaluation strategy for AI product development in the legal tech domain. He has more than ten years of experience in product data science and causal inference, with prior roles at Stripe, Nextdoor, and PwC. His current work focuses on practical, end-to-end methods for evaluating AI features in production. Shane is also the co-host of the AI podcast Data Neighbor, where he interviews product, data, and engineering leaders who are pioneering the next generation of data science and analytics in an AI-driven landscape.

Sravya Madipalli

Senior DS Leader (Ex-Microsoft)

Sravya Madipalli is a Senior Manager of Data Science with 14+ years of experience helping teams make better decisions with data. She has built and led data science and product analytics teams at Microsoft, eBay, Nextdoor, and Superhuman (prev. Grammarly), working closely with product, engineering, marketing, and leadership. Her expertise spans experimentation, metrics design, modeling, analytics, and translating complex user behavior into clear, actionable insights.

Hai Guan

Head of Data at Ontra, Ex-LinkedIn

Hai Guan leads the data organization at Ontra, the leading legal tech AI solutions for private markets. He previously led Data Science & Analytics at LinkedIn, Nextdoor, Pinterest, and Meta. He's spent a decade teaching product development teams how to ask questions that actually drive decisions—and now teaches how to combine that judgment with AI to move 10x faster.

Previously at Stripe, Nextdoor, PwC

Stripe
Nextdoor
PwC India
Ontra
AppFolio

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