Start with the decision. Then define the dashboard.

The business problem

A dashboard is only useful if the team agrees on what its metrics mean and can access the data behind them. In my work, product health, usage, revenue and migrations need to be interpreted together before they can support a decision.

My contribution

I work across product, sales, finance, analytics and partner teams to define KPIs, develop reporting and clarify business questions. For a product-usage dashboard, that includes specifying what visibility is needed and coordinating with the teams responsible for making the underlying data available.

A concrete decision point

In a usage-reporting initiative, the source data was not accessible to the business intelligence tool. I brought the relevant teams together around how and where that data could be made available. The immediate work was to resolve the data dependency before dashboard development could proceed.

That separates two problems that can otherwise get conflated: defining a useful metric and establishing a reliable way to supply it.

What this demonstrates

• Translating a product visibility gap into a specific cross-functional request.
• Identifying a dependency before treating visualization as the solution.
• Connecting metric definitions and data access to the decision the reporting should support.

Scope of the evidence

This example describes my contribution to requirements and coordination. It does not claim that the dashboard subsequently shipped, or attribute revenue, adoption or efficiency gains to it. Those outcomes are not established in this public account.

The connection to AI product management

The same questions shape how I approach my independent products: what decision needs support, what evidence can the system actually access, and what should happen when that evidence is missing? I’m extending my strategy and operations practice through hands-on product development.