Pharmaceutical R&D has a data problem that has nothing to do with a shortage of data.
Drug developers sit on enormous volumes of information: clinical trial registries, regulatory filings, scientific literature, competitor disclosures, pipeline announcements. The problem is that it lives in disconnected silos, built on inconsistent taxonomies, in formats that resist machine-readable analysis. A portfolio manager trying to assess whether a program is worth continuing has to reconcile data that does not speak the same language across sources.
That is the gap OZMOSI and Planview are closing.
On April 23, 2026, the two companies announced a strategic partnership integrating OZMOSI’s structured pharmaceutical intelligence directly into Planview’s AI-driven portfolio planning platform. External clinical reality, what competitors are doing, where regulatory activity is heading, what the trial landscape looks like, connects to internal R&D planning in one system.
What each company brings
OZMOSI has spent twelve years building a single thing: structured, machine-readable pharmaceutical intelligence. Its dataset spans more than 800,000 clinical trials, over 35,000 drugs, and 4,000 diseases and conditions. The underlying architecture is a proprietary taxonomy that standardizes how data connects across the pharmaceutical ecosystem, so that a trial result in one registry links correctly to a regulatory filing, a company disclosure, and a published paper about the same drug.
That standardization is harder than it sounds. Clinical trial registries use different terminology. Regulatory filings describe the same compound through different lenses. Scientific literature applies inconsistent nomenclature across journals. Getting all of it into a unified, coherent structure that AI systems can actually reason over is foundational infrastructure work that took over a decade.
Planview does the planning side. Its platform is used by more than 3,000 organizations and 3.1 million users globally for strategic portfolio management, investment scenario modeling, and resource allocation. Its strength is letting organizations ask hard questions about where to put capital, how to sequence programs, and how to adapt when conditions change.
The integration makes OZMOSI’s external intelligence a live input into those planning conversations.
Why pharma R&D planning has been flying partially blind
A drug development portfolio decision is one of the most capital-intensive choices a company can make. A Phase III trial can cost hundreds of millions of dollars. The decision to advance, pause, or kill a program should be informed by the full competitive landscape: who else is pursuing the same target, what the trial data shows across the field, where regulatory agencies are signaling concern or interest, what patient populations look like in real-world studies.
Most portfolio teams do not have that picture in real time. They have analysts pulling data from multiple sources on different update cycles, normalizing it manually, and presenting it in formats that do not connect directly to the planning tools where investment decisions get made. The analysis and the decision happen in different systems with a manual handoff between them.
According to the Deloitte Centre for Health Solutions, the average cost of bringing a new drug to market has risen to approximately $2.3 billion, with failure rates in late-stage development above 50%. The single largest driver of that cost is late-stage program failures that could have been anticipated earlier with better competitive and clinical landscape data.
Connecting OZMOSI’s structured dataset to Planview’s planning engine is an attempt to move that anticipation earlier in the process.
The data quality argument
OZMOSI founder Beau Bush put it plainly: AI is only as powerful as the data that fuels it. That framing applies directly to what this partnership is building. Planview’s AI-driven analysis is sophisticated. What it needs to produce reliable outputs is clean, standardized, consistently structured input data. Garbage in, garbage out is not a cliché in this context, it is a description of how most pharmaceutical AI initiatives have failed.
The OZMOSI taxonomy was built specifically to solve that input problem. Every data point enters through a standardized semantic layer that connects it correctly to everything else in the dataset. When that feeds into Planview’s scenario modeling, the outputs are as reliable as the underlying data structure allows.
For a portfolio team running a simulation of what a market looks like in five years if two competitor programs succeed and one fails, the quality of that simulation depends entirely on whether the underlying competitive intelligence is accurate, current, and correctly structured. OZMOSI’s 800,000-trial dataset, updated continuously from live registries and filings, provides that foundation.
Who this is for
Global pharmaceutical companies managing dozens of active development programs across multiple therapeutic areas need this infrastructure at enterprise scale. But the partnership is arguably most impactful for mid-size biotech and pharma companies that cannot afford a dedicated competitive intelligence team of the size that would be required to replicate OZMOSI’s dataset manually.
A company running three to five clinical programs that needs to make portfolio prioritization decisions against a field of fifty competitors is exactly the customer this integration was built for. Access to structured intelligence across 800,000 trials and 35,000 drugs, connected directly to planning tools that model investment scenarios, gives a mid-size team analytical capability that was previously available only to the largest players.
According to IQVIA’s Global Oncology Trends report, oncology alone now accounts for over 40% of the global clinical pipeline by indication, with therapeutic areas including neurology, immunology, and rare disease also seeing significant pipeline expansion. Navigating that landscape without structured competitive intelligence is increasingly untenable.
Sources
- Deloitte Centre for Health Solutions — Pharmaceutical R&D Returns
- IQVIA — Global Oncology Trends 2025
- OZMOSI — Official Website
- Planview — Official Website
Editorial disclosure
This article is based on a press release issued by OZMOSI and has been independently rewritten and editorially expanded. It covers a strategic partnership between OZMOSI and Planview for AI-driven pharmaceutical R&D portfolio planning. Both companies are privately held. Market context is sourced from Deloitte and IQVIA. Commentary reflects the author’s own assessment. The information provided on this website is for informational and educational purposes only. Our content is derived strictly from verified online sources to ensure accuracy and objectivity. This analysis does not constitute financial, investment, or professional advice. Readers are encouraged to consult with qualified professionals before making decisions based on this information. For more information, please see our full DISCLAIMER.
