#PractitionerLevelConfidence
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24 Oct 2025
In pharma commercial landscape, every move must be faster, sharper, and more data-driven than ever before. With budgets tightening, engagement time with healthcare professionals and patients shrinking, and competition for the same therapeutic space intensifying, life sciences leaders need to act decisively. Yet, despite an explosion of healthcare data including claims, genomics, and lab records, digital engagement metrics, true real-time intelligence still feels out of reach.
At Indegene Digital Summit (IDS) 2025, Arnaub Chatterjee from Datavant, and Tanmay Jain from Indegene, came together to dissect this challenge and explore what it really takes to stay ahead of it.
Key challenges undermining real-time decisions in pharma commercial
The conversation highlighted how, despite having more data and advanced technology than ever before, pharma companies continue to face practical barriers in pharma commercial decision-making.
Data fragmentation and silos: Pharma has vast datasets across claims, genomics, EMRs, labs, and HCP engagement. But clinical, commercial, and medical data remain disconnected, which creates silos, limiting visibility and slowing action.
“Garbage in, garbage out” problem: AI models are only as good as the data behind them. Poor-quality, redundant, or unlinked data weakens output, reinforcing the rule that without good data, even advanced AI models cannot deliver results.
Procurement and ROI gaps: Data acquisition is long and expensive, with pharma organizations spending an estimated at $70–120 million annually. The return on these investments is often unclear, and value realization is delayed.
Budget and adoption barriers: Costly syndicated datasets that failed to deliver impact have made companies more cautious. This is pushing a shift toward fit-for-purpose data purchases with shorter validation cycles and clearer ROI.
Turning the corner in pharma commercial: what industry leaders need to do
Achieving real-time decision-making in pharma commercial is about rethinking how data is connected, managed, and acted upon. The solutions should be direct, pragmatic, and rooted in operational reality.
Establish a tokenized data layer
Clinical, commercial, and medical datasets often sit in silos, making it difficult to see the full patient journey. Tokenization directly addresses this gap. It is a privacy-preserving method that links records from different sources such as claims, EMRs, labs, or specialty pharmacy feeds, without exposing personally identifiable information. This linkage allows patient-level data to be followed securely across the entire product lifecycle, from clinical trial consent through to post-launch engagement.
For instance, a major cardiometabolic manufacturer demonstrated this by linking first-party date (home-care and pharmacy data) with third-party external claims and lab datasets. The result: cleaner data, fewer duplicates, and a foundation for more targeted omnichannel engagement and patient access programs. Similarly, by embedding tokenization at the point of patient consent in trials, companies can track safety, efficacy, and outcomes post-launch.
Redesign data procurement around speed and measurable ROI
Pharma’s traditional data buying models are slow and costly. Procurement cycles often stretch six to nine months, and broad syndicated datasets rarely prove their value. The speaker focus must shift to faster, fit-for-purpose purchases tied to specific questions. For example, predicting therapy transitions with clear ROI measures. This shortens time to insight and aligns spend with outcomes.
Activate tokenized data across ecosystems
Connectivity alone is not enough; it must translate into action. One of the strongest operational levers discussed was the activation of tokenized data within CRM and ad-tech ecosystems. By bridging tokenized datasets with platforms such as LiveRamp, DeepIntent, Swoop, and Veeva, companies can build precise audience segments.
This creates a continuous loop: the same token used to link data sources upstream is also used downstream for segmentation, targeting, and measurement. It closes the gap between strategy and execution, so commercial engagement reflects actual patient and HCP behavior rather than static audience lists.
Build for quality and turnkey interoperability
Data volume does not equal insight. The quality, readiness, and interoperability of data determine how fast it can be used to power pharma commercial decisions. Smaller, high-signal datasets, especially those representing rare or specialized patient populations, can be valuable when linked within a tokenized framework.
The speaker also highlighted how automation is starting to ease long-standing pain points in data cleanup and harmonization. As large language models mature, cleanup and harmonization will become LLM-enabled, turning data management into a more turnkey process and accelerating its commercial use.
Where does the pharma industry stands when it comes to real-world adoption
Cut to the heart of the issue: is tokenization and data interoperability in pharma commercial still a vision, or is it already real? The technology is mature, the early adopters are scaling, and the real differentiator now lies in organizational readiness.
Is tokenization commercially mature?
Yes. Tokenization is already being used at scale across the industry. Around 30 companies have implemented tokenized datasets on the commercial side, linking first- and third-party data into unified layers. These layers are integrated with CRM and ad-tech platforms, enabling direct activation and targeting.
How does tokenization connect to commercial engagement systems?
Tokenized datasets flow directly into existing commercial workflows. Bridges with platforms like LiveRamp, DeepIntent, Swoop, and others enable tokenized data to map to Ramp IDs and feed audience modeling, segmentation, and targeting without additional manual steps.
Will broad adoption take years?
No. The technology is already in place. What remains is organizational readiness; prioritizing usage, scaling adoption, and linking these capabilities to commercial ROI.
