How can Looker enhance its data modeling capabilities to simplify complex data relationships?

Introduction

Enhancing Looker's data modeling capabilities to simplify complex data relationships is a critical challenge that could significantly impact our users' ability to derive insights from their data. This improvement could streamline workflows, reduce time-to-insight, and ultimately drive better decision-making for our customers. I'll approach this problem by first clarifying our objectives, then analyzing our user segments and their pain points, before proposing and evaluating potential solutions.

Step 1

Clarifying Questions

• Looking at Looker's position in the BI market, I'm thinking about the scale and complexity of data our users typically work with. Could you help me understand the average size and complexity of datasets our users are modeling, and how this has evolved over the past year?

Why it matters: Determines the scope of improvements needed and potential technical constraints. Expected answer: Increasing complexity with datasets growing 50% year-over-year, more diverse data sources. Impact on approach: Would focus on scalability and flexibility in modeling capabilities.

• Considering the evolving data landscape, I'm curious about the types of data relationships our users are struggling with most. Are we seeing more challenges with hierarchical data, many-to-many relationships, or time-series data?

Why it matters: Helps prioritize which aspects of data modeling to focus on improving. Expected answer: Increasing difficulty with many-to-many relationships and time-series data. Impact on approach: Would emphasize solutions for these specific relationship types.

• Given the competitive landscape in BI tools, I'm wondering about our users' cross-platform usage. Are we seeing users frequently switch between Looker and other BI tools for different parts of their workflow?

Why it matters: Indicates potential gaps in our offering and areas where we can differentiate. Expected answer: Some users leveraging other tools for advanced statistical analysis or specific visualizations. Impact on approach: Would consider integration capabilities and feature parity in key areas.

• Thinking about our product lifecycle, I'm interested in understanding where we are in terms of our data modeling capabilities. Are we looking to refine existing features, or is there a push for innovative new approaches?

Why it matters: Guides the balance between incremental improvements and transformative changes. Expected answer: Mix of refinement and innovation, with a focus on staying ahead of emerging data trends. Impact on approach: Would propose a combination of short-term optimizations and longer-term innovative solutions.