Every pharma and biotech organization today wants AI that works at scale to accelerate drug discovery and optimize clinical trials. While 54% of companies have recently completed or are currently in the middle of a major data modernization initiative, many of these investments are failing to deliver a positive ROI.
Traditional "build it and they will come" approaches often result in programs that drag on for years, while data silos persist and accessibility remains restricted. This whitepaper explores a different path: a product-oriented approach that rewires data foundations to align technology, architecture, process, and culture for rapid value creation.
The pitfalls of infrastructure-first modernization: Why focusing solely on cloud migration without evolving engineering practices leads to replicating old inefficiencies in new environments.
The power of Data as a Product (DaaP): How treating data as self-contained, high-quality assets ensures it is discoverable, interoperable, and AI-ready by design.
Industry-specific impact: How life sciences-specific domains, from early discovery to regulatory affairs, can reduce redundant testing and shorten submission preparation cycles.
Real-world success stories: A look at how leaders like Gilead, Roche, and Bayer have achieved up to 4x faster decision-making and 30% lower infrastructure costs.
Pooja is a Healthcare & Life Sciences leader at Thoughtworks with over 15 years of experience in technology consulting. Specializing in AI-driven solutions and strategic initiatives that enhance patient care across the value chain, she has a unique background in both bioinformatics and product management. Passionate about navigating ambiguity and solving customer problems, Pooja’s current focus is on leveraging emerging technologies to accelerate innovation in healthcare and life sciences.
Most efforts fail because they take a "build it and they will come" approach, assuming a complete enterprise repository will eventually satisfy stakeholders. In reality, these programs often drag on too long, causing the business to lose patience while failing to address the underlying data silos and engineering inefficiencies.
Data products are curated data sets packaged as self-contained units with their own lifecycle and service level agreements. This approach makes data quality a priority for the domain experts closest to the information, ensuring it is ready for use in complex AI and ML cases.
In a regulated industry, this mindset shifts the focus from infrastructure deployment to measurable outcomes, such as reduced redundant experimentation and the potential to eliminate rounds of animal testing through AI-ready datasets for simulation.
