Overview
Abstra is the internal data cataloguing platform I designed from scratch at Owkin. It took three years, 52 interviews, and one consequential pivot to turn chronic institutional friction into something researchers actually trusted and used every day.
Working alongside CPO, PM, and three squads, I led discovery, facilitated workshops, designed the full product, and built the design system from the ground up — all concurrently.
“We want to unite healthcare researchers and data experts, powering collaboration to maximise the value of their data and accelerate scientific discovery.”
Thomas Clozel — CEO, Owkin
The situation
Researchers couldn't find the data they already had
Imagine you're a researcher at a biomedical AI company and you need data. Not a concept — actual research data. A specific dataset that might already exist somewhere in the company, processed by a colleague last quarter, sitting in a folder you don't have access to, under a name you don't know.
So you do what everyone at Owkin did: you search a bit, identify a potential dataset on the web, look at whatever documentation exists, find nothing useful, then open Slack and post in #data-questions. You wait. Maybe someone replies. Maybe they point you to a Google Sheet last updated eight months ago. Maybe it's the wrong dataset anyway.
This was happening dozens of times a week, across 180 researchers, across five squads. The problem wasn't that Owkin had bad data. The problem was that no one could find it — and no one could trust it once they did.
Four issues kept surfacing
Discovery
52 interviews and a few workshops later, we had 603 findings
When we started this initiative, there was no product and no brief — just a sentence: “researchers need to find data better, and better data.” My first task was understanding what “better” actually meant.
Over two months, I conducted 52 interviews with 19 distinct users: Data Engineers, ML Researchers, Business Developers, and Project Leads. Using an Atomic UX Research methodology, every observation was clustered into insights, then mapped to opportunities. The result: 603 structured findings, each fully traceable to evidence. Nothing floated on intuition alone.
To synthesise that volume of input, we used a custom template combining Atomic UX Research with the Opportunity Solution Tree framework — breaking feedback into facts, insights, and opportunities. This moved us beyond anecdotes and toward recurring, high-impact patterns.
To bridge research and delivery, we ran a User Story Mapping workshop with the PM and development leads. The exercise forced a shared language across disciplines. Mapping the full journey — from receiving a project request to qualifying results — revealed not just what was broken, but where in the workflow each friction lived.
Criticality levels weren't assigned by gut feel. They were derived directly from the frequency and severity of friction patterns surfaced in research. When we entered prioritisation conversations with engineering, we weren't debating opinions — we were reading from a common document everyone had helped build.
Personas
Two personas, one shared problem
The research surfaced two distinct profiles operating within the same broken system — but failing in different ways, at different stages, for different reasons. Building for one at the expense of the other would have solved half the problem and created new ones.
“When I start a new project, I want to quickly identify reliable datasets so I can spend less time validating data and more time on bioinformatic analysis.”
“I need access to comprehensive, current datasets without manual hunting — and standardised formats to reduce the integration friction that slows every project.”
The strategic decision
The original brief was wrong. So we pivoted.
The initial vision for Abstra was ambitious: an open research network connecting the entire biomedical ecosystem — a kind of LinkedIn for research data. It sounded compelling in a deck.
The research told a different story. The problem wasn't connecting Owkin to the outside world. The problem was that Owkin's own researchers couldn't connect with each other. The pain was internal, not external — and solving for the wrong scope would have built the wrong thing.
This was the most consequential design decision of the entire project — and it happened before a single screen was drawn. It required aligning leadership on a smaller, sharper scope, which meant presenting the research clearly enough that the trade-off felt obvious rather than controversial. Narrowing the scope didn't shrink the ambition. It focused it.
Foundation
One visual language for five squads shipping simultaneously
A 0→1 product at Owkin meant building the foundation as the building went up. Five squads were shipping features concurrently — without a shared system, visual drift wasn't a risk. It was a certainty.
I built the Abstra design system alongside the product, starting from Owkin's existing brand palette and extending it into a component library every squad could use. Owkin's core colours weren't accessible out of the box, so I spent time refining them against WCAG standards before a single component was built on top of them.
The result: zero visual regression across five squads over 18 months. Engineers stopped rebuilding components from scratch. The system held — not because it was enforced, but because it made the right thing the easiest thing. When the foundation is solid, product velocity compounds on top of it.
Four principles for one platform
Every solution traces back to a documented pain
The four principles — Trust, Reusability, Efficiency, Collaboration — mapped directly to the four recurring failure modes identified in research. Nothing was added because it seemed like a good idea. Everything was added because someone had told us, in their own words, that its absence was costing them time.
From blindness to awareness
A dataset page that makes trust legible at a glance
Interviews revealed that researchers weren't just unable to find datasets — they didn't know whether to trust them once they did. Ownership, freshness, and provenance were the three recurring unknowns. The research reframed the design question entirely: it stopped being about displaying a dataset and became about what a researcher needs to know before deciding to use one.
We mapped what researchers ask in the first five seconds versus what they need to dig into. That sequence became the page architecture: availability above the fold, detail in tabs, context in a persistent column.
“Trust isn’t claimed, it’s demonstrated. Every update. Every version. Every person who touched it and when.”
From searching to finding
Built around scientific vocabulary — not file system logic
The problem wasn't a missing search bar. It was that the mental model of researchers and the data model of the system had no shared language. Across 52 interviews, the same terms appeared repeatedly: therapeutic area, indication, modality, cohort size. We catalogued that vocabulary before designing a single filter.
Each of the seven filter dimensions maps to a criterion that appeared in at least 40% of interviews. Nothing was added because it seemed useful — only because researchers said its absence was blocking them. Moving aggregate metrics above the results fundamentally changed the research workflow: researchers assess fit before they explore.
From scattering to integrating
The hidden cost was leaving the platform to do the actual work
Context-switching wasn't visible in the interview data — it emerged from observation. Researchers had normalised it. Mapping their actual workflows revealed how much time was lost between discovery and use. Rather than building a full analysis environment, we asked: what is the smallest integration that eliminates context-switching for the most frequent cases?
For histology teams, slide inspection was the clear answer. Routing the viewer through the file tree kept discovery and analysis separated while making the tooling accessible. The catalogue's purpose stayed intact. The workflow improved.
From isolated to collaborative
Extending the foundation — not duplicating it
After the catalogue shipped, a new friction emerged: researchers were finding datasets but losing the shared context around them — working hypotheses, prior attempts, who else was involved. The decision to scope Projects out of the initial release was as consequential as the decision to build them. Sequencing was itself a design decision.
Extension, not duplication. Projects had to reference the catalogue's trusted data — not copy it, not replace it. That constraint kept the product coherent and the catalogue's integrity intact. The bidirectional model emerged from watching how different researchers actually started their work: neither flow was more "correct", and both needed to be first-class.
Impact
The impact was measurable, and more importantly, felt
Within six months of launch, Abstra had become the default entry point for data discovery across all five research squads. Adoption wasn't driven by mandate — it spread because the researchers who used it first told the ones who hadn't.
The metrics confirmed what we already knew from qualitative feedback: the friction hadn't just been reduced. For most workflows, it had been eliminated. Abstra was described internally as “an unexpected success.”
“The friction hadn’t just been reduced. For most workflows, it had been eliminated.”
Tools used
- Figma
- Atomic UX Research
- Opportunity Solution Tree
- Storybook
- Notion
- Airtable
- Fullstory
- Miro