My Role as a Leader

I served as UX Lead on the initiative and was responsible for shaping the experience from a workflow, systems, and stakeholder alignment perspective.

My role included:

• leading UX strategy
• defining information architecture
• creating workflow and drill-down models
• designing dashboards and interactions
• conducting research
• facilitating stakeholder workshops
• influencing product direction and roadmap
• aligning merchandising, finance, and leadership stakeholders
• managing designers
• partnering closely with engineering, data teams, and data scientists

A key part of my contribution was helping teams view the problem as an ecosystem challenge, not a dashboard challenge. The platform needed to solve for trust, actionability, role-based complexity, and performance—not just visualizing data. I helped shape an experience that made insight discovery intuitive, role-relevant, and easy to act on without requiring users to learn a complicated reporting tool.

Stakeholder Alignment

The platform supported a wide range of users across merchandising, finance, supply chain, category teams, international teams, business leadership, and executive leaders. Each audience needed different levels of abstraction, different views of the business, and different paths to action.

This made alignment especially important. One of the biggest organizational pain points was inconsistency: different teams were bringing different versions of the truth into leadership discussions because each group had its own way of extracting and interpreting data. That created confusion, slowed decision-making, and reduced confidence in the underlying numbers.

I helped align stakeholders around a different model: one shared platform, one semantic foundation, and one role-aware experience that could serve varied business needs without compromising trust or consistency. By connecting user needs, workflow models, and system behavior, I helped drive a shared understanding of what the platform needed to achieve:

• unify reporting into a single trusted source of truth
• reduce dependence on analysts and manual spreadsheets
• surface machine-generated insights at the right level for the right role
• make drill-down exploration intuitive
• improve speed from issue detection to business action

This alignment was especially important as the platform matured from multiple Power BI reports to its own consumption layer and then into a more scalable semantic-model-based architecture.

Approach

The design effort focused on making a highly complex data ecosystem understandable, actionable, and efficient for different business roles.

The work required close collaboration across UX, product, engineering, and data science. Because the platform was both data-heavy and performance-sensitive, the experience design had to account not only for usability, but also for how insights were generated, stored, surfaced, and refreshed in ways that could support near-real-time decision-making.

A major design principle was preserving the exploratory freedom users valued in Excel while removing the inconsistency, manual effort, and cognitive burden that came with it. Instead of expecting users to derive meaning from raw tables or separate reports, the platform was designed to surface relevant insights proactively and then support deeper exploration through flexible drill-down pathways and filters.

The result was an experience that balanced:

• complexity and clarity
• flexibility and consistency
• high-level oversight and deep investigation
• AI/ML-generated signals and user-controlled exploration

Key Design Moves

Several design decisions were central to the success of the platform:

Unified fragmented reporting into one platform

We replaced a disconnected ecosystem of reports, consumption layers, and manual spreadsheets with a more coherent platform that served as a single destination for trusted insights and exploration.

Created a single source of truth

One of the most important shifts was semantic consistency. The platform brought users onto a more unified data model so that different teams were no longer presenting different answers to the same business question.

Introduced role-based personalization

Different users saw different views, priorities, and levels of abstraction based on their role. This made the platform more relevant for merchants, finance teams, leadership, and other business stakeholders without forcing everyone into the same reporting experience.

Designed AI/ML-generated insights into the workflow

The platform did more than visualize raw data. It surfaced machine-generated insights including anomaly detection, trend identification, recommendations, forecasts, prioritized actions, and role-relevant signals so users could focus on what mattered most.

Built intuitive drill-down architecture

Users could move from enterprise-level signals to increasingly granular views. One drill-down path helped leaders trace patterns from company level to state, market, county, and store level to determine whether an issue was localized or systemic. Another path supported item-level exploration, helping teams understand whether anomalies were tied to specific products, categories, or broader patterns.

Made insights more actionable

Instead of static reports, the experience emphasized insight-to-action workflows through summaries, explanations, comparisons, and prioritized signals that helped users move faster from observation to decision.

Improved navigation and exploratory filtering

The platform supported flexible filtering and exploration without overwhelming users, helping them “play with the data” in a more guided and reliable way than traditional spreadsheet-based workflows.