Data Strategy

Building the Data Strategy Behind an Emerging Biotech's Pediatric HAE Launch

Q: What is a data strategy engagement, and why did this biotech need one before launch?

A data strategy engagement assesses an organization's current data environment and builds a plan for closing the gap between what it has and what a launch requires. This biotech was pulling claims, lab, and EHR data from disconnected sources and making provider decisions case by case, with no structured way to compare vendors or prioritize the questions that mattered most against a fixed pediatric-launch date.

Q: How did Axtria prioritize more than 40 competing business questions?

Axtria interviewed the value, access, evidence, and medical teams who would rely on the data, then ranked the questions spanning patient identification, HCP engagement, market access, and HEOR/RWE against four data domains: claims, labs, EHR, and payer. The ranking gave the company a defensible sequence instead of forcing every question through at once.

Q: How does benchmarking nine data providers reduce risk for an emerging biotech?

An evaluation framework scored claims, lab, genetic, and EHR providers against the company's specific HAE requirements, then benchmarked nine providers side by side. That gives the team that owns the data budget an evidence base for every licensing decision, instead of paying for data that cannot answer launch-critical questions or coverage a pediatric population does not need.

Q: What target-state capabilities did the strategy recommend, and are they built yet?

The engagement recommended two target-state capabilities: an omnichannel data foundation and a market-access and payer capability. Both are design recommendations and a phased roadmap at this stage, not capabilities the company has built or stood up. The roadmap sequences a claims-data proof-of-concept through to mining clinical notes with GenAI.

Q: Why does stakeholder alignment matter as much as the data itself in a launch like this?

Six teams, value and access, RWE and HEOR, health outcomes and payer liaison, global market access and pricing, global medical affairs, and the medical science liaisons, each had a stake in the same patient data but had been pulling in different directions. A single prioritized question register lets leadership align spend and effort across all of them instead of each team optimizing for itself.

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How a fit-for-purpose data strategy benchmarked nine providers, prioritized 40+ business questions, and mapped the capability gaps ahead of a rare-disease pediatric launch

An emerging biotech was preparing to launch a pediatric indication for hereditary angioedema (HAE), a disease where many patients stay undiagnosed for years. Data came from disconnected sources, and provider decisions happened case by case with no structured comparison. Axtria's Life Sciences Data Strategy practice assessed the current state and built a strategy sized to the launch timeline.

The Challenge

Fragmented patient visibility: Undiagnosed and misdiagnosed HAE patients, including C1-Normal cases, left the addressable population understated.
Uncertain provider selection: No objective way to score vendors risked paying for data that couldn't answer launch-critical questions.
Underused clinical data: Unmined EHR and clinical notes left market-access and HEOR/RWE teams with an incomplete picture.
No prioritization: 40+ questions competed for attention with no defensible sequence against a fixed launch date.

The Approach

Assessment and prioritization: Interviewed the value, access, evidence, and medical teams, then ranked 40+ business questions across claims, labs, EHR, and payer data.
Provider-agnostic benchmarking: Scored and benchmarked nine data providers against the company's HAE requirements, producing five targeted recommendations.
Capability-gap analysis: Mapped current-state capabilities against launch demands and recommended target-state solutions, including an omnichannel data foundation and a market-access/payer capability.
Implementation roadmap: A sequenced plan from a claims-data proof-of-concept to mining clinical notes with GenAI.

For a small company against a fixed launch date, the value came from sequencing: which questions to answer first, and which data foundations to build before committing budget.

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