How can Goodera balance the simplicity of its CSR reporting dashboard with the depth of data analysis required by large enterprises?

Introduction

Balancing simplicity and depth in Goodera's CSR reporting dashboard presents a critical trade-off. We need to provide an intuitive interface for quick insights while offering the robust analysis capabilities large enterprises require. I'll outline my approach to addressing this challenge, focusing on user needs, data architecture, and iterative product development.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm assuming Goodera serves both SMBs and large enterprises. Could you confirm if there's a specific segment we're prioritizing for this dashboard enhancement?

Why it matters: Helps focus our solution on the most impactful user group Expected answer: Large enterprises are the priority Impact on approach: Would emphasize advanced analytics and customization options

• Business Context: Based on the focus on CSR reporting, I'm thinking this might be tied to regulatory compliance or stakeholder transparency initiatives. How does this dashboard fit into Goodera's overall value proposition and revenue model?

Why it matters: Aligns solution with core business objectives Expected answer: Critical for customer retention and upselling advanced features Impact on approach: Would prioritize scalability and integration with other Goodera products

• User Impact: I'm assuming we have a mix of casual users needing quick insights and power users requiring deep dives. Can you share the breakdown of user types and their primary use cases?

Why it matters: Informs UI/UX decisions and feature prioritization Expected answer: 20% power users, 80% casual users, with power users driving most value Impact on approach: Would consider a tiered interface with progressive disclosure

• Technical: Given the need for in-depth analysis, I'm curious about our current data architecture. What are the main limitations or bottlenecks in handling complex queries for large datasets?

Why it matters: Determines feasibility of advanced analytics features Expected answer: Current system struggles with real-time analysis of large datasets Impact on approach: Would explore options for optimizing data processing and caching

• Resource: Considering the potential scope, I'm wondering about our team's capacity. Do we have dedicated data scientists or BI specialists who can support advanced analytics features?

Why it matters: Influences the complexity of solutions we can realistically implement Expected answer: Limited data science resources available Impact on approach: Would focus on out-of-the-box analytics tools with some customization options