Data in life sciences is more than a mere byproduct of research; it is a driving force behind innovation. Rohit Dayama, global client partner, Life Sciences, UK&I, Cognizant explores how by harnessing extensive datasets, organisations can speed up drug discovery, refine precision medicine, and improve operational efficiency.
Cognizant
This shift has transformed data management into an essential cornerstone rather than just a technical tool. Although artificial intelligence (AI) continues to captivate the industry, the true cornerstone of successful digital initiatives lies in mastering data control.
Yet, without proper oversight, the risks are substantial. For instance, the EU’s AI Act imposes severe penalties on life sciences companies deploying non-compliant systems. In this context, data is no longer merely a resource but a core strategic asset requiring active protection and management.
The growing data opportunity
The scale of data in life sciences is staggering. Pharmaceutical firms partner with thousands of study sites and tens of thousands of trial participants. A study by Tufts University found that Phase III clinical trials now generate an average of 3.6 million data points, tripling the volume collected a decade earlier. Amid this deluge of information, ensuring timely access to the right data is crucial to optimise R&D efforts, while reducing the time spent on data preparation and management.
Fragmentation compounds the challenge. A 2024 survey by Informatica shows that 41% of organisations have 1,000 or more data sources and nearly 60% use an average of five tools to manage them. As data increases in volume and becomes more fragmented, the need for a single consolidated solution to manage it becomes even clearer.
By consolidating data, companies can unlock numerous benefits. Sales teams can better understand trends and factors influencing their target accounts, procurement can leverage improved data visibility to collaborate more effectively with vendors and partners, and corporate M&A teams can achieve faster time to value by integrating an acquired company’s data, systems and applications more efficiently. As a result, drug discovery becomes more efficient, outcomes become more predictable and the insights from analytics become more reliable. At its core, success depends on robust data management practices that prioritise quality, accessibility, governance and protection.
Overcoming regulatory challenges
Life sciences is a highly regulated industry, requiring compliance with international standards, such as the CIOMS International Ethical Guidelines, ICH E6 Guideline for Good Clinical Practice and PhRMA Principles for Clinical Trials and Communication of Results. The introduction of the European Union’s AI Act, which came into force on 1st August 2024, adds another layer of complexity. Managing requirements across overlapping regimes makes establishing a single source of truth for patient data not just best practice but a business imperative. Effective data management is especially crucial to meeting the AI Act's stringent governance and traceability requirements.
Consolidating data management under one platform allows life sciences firms to define clear responsibilities, policies and processes while streamlining workflows. A unified approach to metadata management ensures a consistent application of data protection and privacy measures, enabling companies to automate compliance and navigate the complexities of evolving regulations with confidence.
Preparing for AI adoption
Research from Cognizant and Oxford Economics forecasts that enterprise AI projects will leap from today's experimentation to a period of confident adoption by 2026. The life sciences industry must prepare for this. Proper stewardship of patient and intellectual property data is a foundational element of any digital healthcare initiative, particularly when using AI, where transparency and reliability of outputs remain ongoing concerns.
AI can also play a transformative role in data management by automating complex tasks such as data extraction, classification and validation. This improves accuracy and accelerates compliance. In this sense, AI and data are interdependent—AI needs high-quality data, and modern data management increasingly relies on AI-driven efficiencies.
Scalable software with integrated AI engines can take on the heavy lifting of data ingestion, integration, governance and quality management. Advanced tools also allow non-technical users to access data products tailored for specific use cases like drug discovery, fostering greater collaboration and innovation.
For example, we worked with a global pharmaceutical brand who needed to advance its multi-year strategy to migrate its on-premises solutions to the cloud. Moving to the next phase required a single, trusted source of data for analytics that could be leveraged across the enterprise. The firm implemented an AI-powered SaaS data management and governance solution to consolidate data management and shift their legacy supply chain analytics to the cloud. As a result, development processes were simplified, delivering notable reductions in cost and time. Five million records could be loaded in 4 hours, vs. 19 hours required by the previous system. Since deployment, the new cloud-based data management solution has helped cut the time to manage new orders by 50%.
Establishing trust and taking control
Life sciences is no stranger to innovation, but today its eureka moments depend on data — data that is high-quality, compliant, secure and governed. Data management in a highly regulated global industry remains complex and costly. Companies must break down silos, integrate external data and comply with evolving regulatory landscapes. They also need to handle growing complexity in data types, formats and storage, while ensuring integrity and security. This is essential to foster trust and an environment of innovation.
Achieving this requires modernising data management to empower R&D teams to unlock new possibilities. From streamlining drug discovery processes to precisely targeting discreet patient populations, robust data systems can drive impactful change. As advanced analytics and generative AI drive a new wave of innovation, it's essential that data management systems rise to meet the industry’s rising demands for speed, quality, compliance and innovation.