Do You Treat Your Clinical Data Like Garbage?
How the CRO Industry Destroys the Core Asset of Life Science Companies
Intro
Most life sciences companies treat their clinical data like garbage.
Less than 1% of it ever gets used again — not for learning, not for improving future trials, not for making the next drug better.
That’s insane.
The root cause? Clinical operations are outsourced to CROs. Clinical data becomes part of a transaction, not part of the drug’s intellectual property.
The sponsor loses visibility. The CRO just executes. No one really owns the data — so no one learns from it. Not the company. Not the regulator. Not the patient.
But it doesn’t have to be this way.
Some companies are taking a radically different path. They're building data infrastructure in-house. They're treating clinical data as part of their core IP — just as vital as the molecule itself.
My guest, Fred Braga from Debiopharm, is doing exactly that. He’s leading a cultural shift — moving from PDFs and static reports to real-time, reusable data. He’s helping turn clinical operations into a strategic advantage.
This episode is about what happens when you treat your clinical data not as an SDTM file — but as a product.
Listen to my conversation with Fred Braga - the Debiopharm Data Fabric
In this Friday essay, I’ll ask (and answer 3 questions):
Do life sciences companies leverage their clinical trial data for learning?
Why isn’t clinical data shared — or even used internally?
How does CRO outsourcing erode data ownership and use?
We’ll close with a strategic idea:
What your customers really buy — what we all buy — is a better version of themselves. A better version of your customer is a company with vastly more valuable IP.
Let’s get into it.
Do Life Sciences Companies Leverage Their Clinical Trial Data for Learning?
Hypothesis: Few life sciences companies make use of their clinical data for learning once a trial is complete.
Evidence: The vast majority of clinical trial data is never revisited or shared for secondary analysis. As of 2025, over 400,000 studies were registered on ClinicalTrials.gov. Yet only a few hundred datasets have been made publicly available on platforms like Project Data Sphere (PDS).
PDS — a leading oncology data-sharing initiative — grew from just 9 datasets at launch to around 150 after six years. In 2021, the president of PDS noted that "less than 1%" of clinical trial data is reused for secondary analysis.
99.9875% of trial data sits idle after meeting primary objectives.
Despite the availability of sharing platforms like Vivli and PDS, the actual number of shared datasets remains minuscule — a clear sign that data is underutilized.
Implications: If companies truly leveraged data as a learning resource, we'd see widespread contribution to pooled knowledge bases. Instead, reluctance to share suggests companies prefer to keep data siloed — or fear exposing flaws that could harm competitive advantage.
Why Isn’t Clinical Data Shared — or Even Used Internally?
Companies worry that sharing raw data could expose failed endpoints, safety issues, or poorly performing subgroups. Transparency may hurt sales — or help competitors.
Internally, once trial results are published or submitted to regulators, the raw data is rarely revisited.
The culture is project-focused ("get the drug approved") rather than learning-focused.
This is starting to shift, thanks to data science. But most life sciences companies still don’t fully exploit the treasure trove of clinical data they generate. The tech exists — advanced analytics and cloud platforms — but mindset and incentives lag behind.
When datasets are aggregated across trials, the value is immense: rare adverse events emerge, biomarkers are validated, new subgroups identified. Yet secondary analysis remains the rare exception.
Conclusion: Clinical data is rarely used post-trial.
How CRO outsourcing erodes data ownership and use
Hypothesis: Outsourcing clinical operations to CROs reduces sponsor control and undercuts the strategic use of data.
Evidence: For decades, pharma and biotech companies have outsourced trial execution and data management to CROs. This created separation between companies and their data — both technically and psychologically.
Sponsors relied on CRO-owned systems for data capture, monitoring, and analysis. The result: data lived in CRO silos, not in unified internal databases.
Reports came back as PDFs and Word docs, detaching sponsors from the raw data.
Learning requires hands-on access and curiosity — both of which diminish when work is outsourced.
However, there is a life science industry that runs their own clinical trials.
Run your own trial - the Novo Nordisk model
The Novo Nordisk model is a clinical trial operating model— common in medical device and combination product trials—where the sponsor runs the trial in-house rather than outsourcing to a CRO.
The Novo Nordisk model is a strong counter-example to full CRO outsourcing. It prioritizes internal control, data ownership, and tight feedback loops—making it especially effective in device and combination product trials. This model is a real-world proof that in-house trial execution drives higher data quality, faster iteration, and deeper learning.
Key Characteristics of the Novo Nordisk Model:
Fully In-House Clinical Operations:
Novo Nordisk has historically built and retained internal clinical operations teams, including trial managers, data managers, statisticians, and monitors.
This contrasts with the dominant pharma model of outsourcing to CROs.
Especially prevalent in device or combo product trials, where regulatory and human factors requirements demand tighter control and integration between R&D, clinical, and regulatory teams.
Strong Site Relationships:
Novo builds long-term relationships with trial sites, treating them as partners rather than vendors.
Internal CRAs (clinical research associates) directly manage site communication and monitoring, improving data quality and enrollment efficiency.
Integration Across Functions:
Clinical trials are deeply integrated with device engineering, human factors, regulatory, and market access teams.
This tight loop is crucial for combination products (like insulin pens or smart injectors) where user experience, safety, and design feedback are clinical endpoints.
Control Over Data and IP:
Running trials in-house gives Novo full ownership and real-time access to data, which improves both operational agility and long-term reuse.
This is especially important for iterative device development, where post-market feedback loops and real-world data collection matter.
High Internal Capability and Investment:
The model demands strong internal infrastructure—including EDC systems, SOPs, regulatory know-how, and training.
Novo Nordisk has been willing to invest in these capabilities rather than relying on CROs.
Why this model is common in device trials
Human factors validation and usability testing are FDA-mandated for devices—hard to delegate to third parties.
Design iteration cycles are tighter than in traditional drug development.
Device trials often require custom protocols, user training, and real-time feedback.
Data is often proprietary not just for safety/efficacy, but for product design, IP defense, and market differentiation.
And what about drug trials?
A McKinsey report recently observed that the pendulum is swinging to insourcing in drug trials. Companies like AbbVie, ImmunityBio, Debiopharm and BeiGene are reclaiming core functions like data management and medical monitoring.
BeiGene now runs 90% of its trials in-house, reporting faster enrollment, lower costs, and a better patient experience.
AbbVie built its own bioanalytics and data lab, improving quality, reducing costs, and enabling real-time learning.
Fred Braga’s team at Debiopharm has done the same, building a "data fabric" to unify and access clinical data in real time.
Conclusion: The outsourcing model sidelined data. You don’t outsource core IP and clinical data is core IP.
Life science companies are built on core IP, and data
Hypothesis: Clinical data is — or should be — part of a company’s core intellectual property.
Evidence: Pharma companies build value around protected IP. Historically, this meant patented compounds. But clinical data is inseparable from innovation.
Regulators require clinical evidence to prove safety and efficacy.
M&A deals value the data as much as the product.
AI tools increasingly mine trial data to uncover new biomarkers or repurpose assets.
Regulatory Proof: In the EU, Regulatory Data Protection (RDP) gives exclusivity to trial data — a legal acknowledgment that clinical data is IP.
Trade Secret Value: Trial outcomes, patient-level insights, and failed approaches are guarded like trade secrets. They offer competitive advantage, even if the drug doesn’t make it to market.
Conclusion: The culture is shifting. Companies are beginning to treat data as a strategic asset, not just regulatory baggage.
Outro
We obsess over gathering data about our customers. We segment them into tidy personas — age, ethnicity, location, gender.
Then we layer on labels: where they live, what music they listen to, what clothes they buy. It helps us understand who they are, so we can target them with offers.
But people don’t buy because of who they are.
They buy because of who they want to become.
What your customers really buy — what we all buy — is a better version of themselves.
I don’t care if you sell clinical trial outcomes, data, AI models, AI agents, or infrastructure.
A better version of your customer is a company with vastly more valuable IP.
When you start selling that — your customers will start buying.
About Me
I’m a former pharma-tech founder who bootstrapped to exit.
Now I run a private community with 900+ life science leaders helping them maximize their revenue with the right partners.
📈Every Friday: sharp no-BS insights on how real techbio companies grow revenue—plus candid interviews with the builders.
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About Debiopharm
Debiopharm is an independent biopharmaceutical company based in Switzerland with an ongoing commitment to develop tomorrow’s standard of care to cure cancer & infectious diseases and improve patient quality of life.
Fred Braga is a hands-on technology executive. Fred and his team are building the “Debiopharm Data Fabric”; a comprehensive data pipeline system for the company. They focus on the 4 P’s: Payer, Provider, Physician and Patient. Their key metrics for measuring value are reduction in cycle times, risk reduction and cost reduction. Expect implementation of the vision by 2026-2027.
🔗 Visit Debiopharm
Sources:
Methia J. Applied Clinical Trials (2023) – Insourcing to increase data ownership.
McKinsey (2022) – Trend back to insourcing from full CRO outsourcing.
Trade Secrets Law Blog (2020) – Pharma trade secrets include trial data.
EFPIA (European pharma association) – Regulatory data protection overview.