Turning fragmented data into products people can trust.

The analytics initiative initially supported four business teams who relied on fragmented reporting and manual consolidation. Metric definitions weren't always consistently understood across teams, and people needed reliable information before they could make business decisions.

The problem wasn't simply a lack of dashboards. Teams were spending too much time preparing information and repeating manual reporting work by hand. Metrics weren't consistently understood across teams, and it was often unclear which numbers could be trusted. That slowed decision-making, and even when data was on screen, it didn't always make the next decision clearer.

Early conversations kept producing requests for more reports and features. I changed the approach: instead of asking only what people wanted added, I asked what decision they were trying to make, how they made that decision today, where the process slowed down, and what happened when the information was late or unreliable. That surfaced decision-linked evidence instead of generic feature requests, and pointed toward the real value of the product: reducing manual reporting work and helping teams reach decisions with more confidence.

I led customer and stakeholder discovery, clarified the underlying problem and requirements, and helped define the outcomes the product needed to support. I owned prioritization by customer value and business impact, worked across business, engineering, data, and design, supported backlog and release conversations, helped validate data trust and product quality, and communicated the product's value to leadership.

The direction focused on consolidating fragmented information, creating clearer and more consistent metrics, and moving important report logic into a stronger shared data foundation. The goal was to improve data trust, reduce repeated manual reporting, directly support the decisions people needed to make, and build a reusable foundation other teams could adopt rather than a one-off dashboard.

Getting there took steady collaboration across business stakeholders, engineering, and data teams: aligning on shared metric definitions, sequencing which report logic moved into the shared foundation first, and keeping delivery paced against what the underlying data platform could reliably support.

Discovery covered four business teams, and each saved roughly 35 to 40 hours of manual reporting work per month, about 140 to 160 hours across those teams combined. Eight additional teams expressed interest, and the broader analytics platform grew to 14 dashboards across five operating units. A repeatable onboarding model brought new operating units onto the platform in about a week, and the evidence this generated supported a multimillion-dollar investment case for expanding the platform further.

Standardizing metrics meant balancing shared definitions against individual teams' specific needs, and expansion speed had to stay in step with data trust rather than outpace it. We also weighed new feature requests against strengthening the shared foundation, and executive-level visibility against the detailed operational usefulness individual teams needed, prioritizing the tradeoffs that protected long-term platform value over short-term requests.

Strong discovery is not about collecting feature requests. It's about understanding the decisions people need to make, the friction in their current workflow, and the outcome that would actually make the product valuable.