Enhancing the Data Link Platform Experience

The existing Data Link platform made exploring and purchasing datasets challenging. Users struggled to find relevant datasets, understand pricing, and manage their subscriptions due to fragmented navigation and inconsistent patterns. This created friction, lowered conversions, and increased dependency on customer support.

The original dashboard buried key actions and offered little visibility into purchased datasets or usage limits. I introduced a centralized dashboard that surfaced recent activity, subscription status, and quick links to dataset management. This improved orientation and gave users a clear sense of control over their data and purchases.

I redesigned the dataset marketplace to make it easier for users to browse, compare, and purchase financial datasets. The new layout prioritized clear categorization, pricing transparency, and scannable dataset details, helping users quickly find offerings that matched their needs and improving overall discoverability.

Previously, users could only purchase one dataset at a time, which created unnecessary friction and slowed conversions. I proposed and designed a shopping cart experience that allowed users to select multiple datasets, review pricing upfront, and complete purchases in a single, streamlined checkout. This not only improved efficiency for power users but also increased overall purchase volume by supporting bulk transactions.

Finding relevant datasets was a common pain point. I worked with engineers and product managers to streamline search and filtering, introducing intuitive filters, predictive search suggestions, and clearer metadata. These updates helped users discover datasets faster and reduced reliance on customer support for dataset selection guidance.

To improve the developer experience, I designed an interactive API playground where users could explore endpoints, test queries, and view real-time responses directly within the platform. This reduced onboarding friction for technical users, empowering them to self-serve and integrate datasets more confidently.