Should Databricks prioritize enhancing our SQL analytics features or expanding our machine learning capabilities?

Introduction

The trade-off we're examining today is whether Databricks should prioritize enhancing our SQL analytics features or expanding our machine learning capabilities. This decision is crucial for our product strategy and will significantly impact our market position, user base, and revenue streams. I'll approach this analysis by first asking clarifying questions, then diving into the trade-off specifics, metrics, experimentation, and finally, providing a recommendation with next steps.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm thinking about our current market position. Could you share how our SQL analytics and ML capabilities currently compare to our main competitors?

Why it matters: Helps understand our competitive advantage and areas for improvement Expected answer: We're strong in SQL analytics but lagging in ML capabilities Impact on approach: Would influence which area needs more immediate attention

• Business Context: Based on our revenue model, I assume both SQL analytics and ML contribute significantly. Can you provide a rough breakdown of revenue attribution between these two areas?

Why it matters: Aligns decision with financial impact Expected answer: 60% SQL analytics, 40% ML, with ML growing faster Impact on approach: Higher growth in ML might justify more investment there

• User Impact: I'm considering our user segments. What percentage of our users actively use both SQL analytics and ML features?

Why it matters: Identifies potential for cross-selling or feature integration Expected answer: About 30% use both regularly Impact on approach: Low overlap might suggest focusing on one area to drive adoption

• Technical: Regarding our current architecture, how intertwined are our SQL and ML systems?

Why it matters: Assesses feasibility and potential synergies of enhancements Expected answer: Moderately integrated, with some shared components Impact on approach: High integration might favor a balanced approach to improvements

• Resource: Considering our team structure, do we have separate teams for SQL and ML, or is it a unified data team?

Why it matters: Influences resource allocation and implementation strategy Expected answer: Separate teams with some overlap Impact on approach: Might need to consider team restructuring based on priority