Regulatory Information Systems & Data

The build-vs-buy proof point: When the library team needed a better way to handle journal lookups, I built the Journal Finder tool in-house instead of purchasing a vendor product — avoiding $10,000+ in external vendor cost. Treating a data-access problem as something to model and build, not just buy, is the habit this page is about.

Optimizing the Systems & Databases

I analyze and optimize Organon's regulatory information systems and databases to improve user experience and access. In practice that means looking at how the systems are structured and used — where researchers stall, where access breaks down — and improving the path between a researcher's question and the information that answers it.

The ORacLe collection as a data asset

I find, curate, and deliver regulatory intelligence and documentation for researchers across the organization using the ORacLe digital library's collection. Curation is data work: the collection is only useful if what enters it is the right material, organized so it can be found again.

Access is an architecture problem

Researchers can't use data they can't reach. Improving access — how systems are organized, how users get in, how collections connect — is treated as part of the data design, not an afterthought.

Journal Finder: A Data-Access Tool, Built In-House

Journal lookups are everyday, high-volume work for the library team. The vendor route meant paying for a product shaped around a generic feature list. I built Journal Finder in-house instead — a lookup tool modeled on the team's actual work — and the team avoided $10,000+ in external vendor cost while keeping a tool it can change on its own schedule. The judgment is the point: buy where a vendor genuinely adds value, build where the team understands the data best.

AI in the Documentation Workflow

Automating regulatory documentation workflows

I use AI to automate regulatory documentation workflows — the repetitive handling between a document arriving and it being usable — so skilled time goes to judgment instead of shuffling.

Finding the right AI use cases

Part of the role is identifying where AI genuinely improves research speed, accuracy, and efficacy within the library — and, just as important, where it doesn't belong. Use-case selection is a data-quality decision: automation amplifies whatever process it touches.

Data Integrity, With IT

I work with our IT partners to improve regulatory systems and data integrity, including SSO and onboarding applications. Identity and access sound like plumbing until a researcher is locked out of a system mid-project or an account outlives the person it belonged to — then they are the data story. Keeping who-can-see-what aligned with reality is foundational governance work.