Sound familiar? “We're paying for data our teams don't use.”
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High-fidelity Figma prototype of a self-service data marketplace — including search and filter UI, trial access flows, user context-specific journeys, and data asset detail pages with licensing and reliability metadata.
Impact
17% increase in monetization of third-party data licenses, measured by the client after rollout
Prototype secured product, partner, and leadership buy-in within 2 weeks of engagement start — with no access to back-end systems
"I loved the way he worked on this with very little information to go on. We are going to have an outstanding V2." — Product Manager
Problem
A global strategy consulting firm needed a way to make proprietary and external data sets accessible and monetizable — but the existing internal platform lacked clarity, usability, and a viable pricing model. There was no way to preview data before purchasing, metadata around data usefulness and ROI was unclear, access was operationally expensive to grant or revoke, and pricing models were either rigid or nonexistent. Case teams were underusing assets the firm had paid for, and monetization of licensed data was significantly below potential.
Approach
Came into the engagement mid-project with limited context and no access to back-end systems — and delivered a validated prototype in 2 weeks
Drew on consumer e-commerce design patterns to create a shopping-like data discovery experience that matched how case teams actually think about data acquisition
Designed flows for three distinct user contexts: New Users, Active Users, and Trial Users
Prototyped the complete journey in Figma for stakeholder alignment and validation
Solution
A self-service data marketplace experience modeled on consumer e-commerce — with search and filter across asset categories, a 14-day free trial period for internal case teams, automated purchase prompts at trial completion, and visual affordances for data recency, reliability, and licensing terms. The design gave data scientists and case teams a way to find, evaluate, and access data assets independently — reducing operational overhead, increasing utilization, and establishing a scalable framework for monetizing the firm's licensed data investments.
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