Contributed Commentary by Diane Lacroix, eClinical Solutions 

July 18, 2025 | Imagine trying to piece together a puzzle with half the pieces missing—that’s what happens when data is siloed. Without integrated, centralized data across sources, data decisions are made using incomplete, fragmented datasets. Contextual data integration helps solve this, keeping every critical piece of the puzzle intact and bringing data together to form a complete picture. It involves merging diverse data sets while preserving and utilizing the context surrounding that data. 

In personalized medicine—which leverages data from wearables, omics data, genetic databases, medical records, and other sources—more data equals more complexity. As we incorporate more data, the more advanced statistical methods and robust data management systems become essential to process and interrogate it while maintaining data integrity and compliance with regulatory standards.  

Overcoming traditional data silos with modern, tech-driven integration achieves better data quality while also enabling biopharma companies to process and extract insights more quickly. Only then can these novel data types be translated into personalized medicines that benefit patients.  

Addressing Today’s Data Volume and Variety

There are many challenges today when integrating diverse datasets, from dealing with varied data formats and sources, to managing the sheer volume of data being collected in modern clinical trials. In the case of precision medicine, which requires high volumes of patient data to personalize outcomes, all of these factors are coming together to create an incredible pace of rising data volume and variety.  

Where data was once primarily flowing from electronic data capture (EDC), we are now seeing many other sources of data acquisition, as mentioned above—all of which must be harmonized to gain usable insights for decision making. As data volume and variety increase, processing these large-scale datasets and managing data flows has become much more complicated, as it involves more systems, stakeholders, and decision-makers to manage. And even with all these data challenges, there is added pressure on biopharma to perform data processes faster and more efficiently, often with fewer resources.  

To address these challenges and meet these demands, leveraging a foundational data infrastructure that incorporates artificial intelligence (AI) and machine learning (ML) can automate time-consuming tasks for data ingestion, integration, and standardization. For example, AI can support automated mapping of data to standards, surface outliers in data analytics and visualizations, and detect data discrepancies for human review. Biopharma companies are increasingly focused on building a future-proof clinical data infrastructure to gain efficiencies through process improvements and automation – and to establish a solid foundation for applications of advanced analytics.  

AI-Driven Integration Empowering Smarter Decision-Making

AI-driven data integration enables teams to quickly aggregate and analyze diverse datasets—including clinical data, genomics, and real-world data—to uncover patterns and correlations for an anticipatory approach to patient care. These data types provide a multi-dimensional view of a patient's health trajectory, which allows for earlier detection of risks and proactive management of potential issues. This is increasingly important in the development of targeted therapies, where clinical teams need holistic insight into the data to identify the right patient populations for specific therapies, optimize trial designs, and predict outcomes more accurately. 

However, to effectively and accurately utilize these data types, advanced data platforms need to support real-time data integration and analysis so that teams can continuously process this data, identify trends, and generate actionable insights. This grants stakeholders the ability to make informed decisions in real time, anticipate and address risks, and accurately leverage data for improved patient outcomes. 

By automating routine tasks such as data standardization, cleaning, review, and analysis, AI enables clinical and data teams to focus their domain expertise on critical decision-making instead of non-critical data processing activities. This kind of AI-supported data management not only enhances technical efficiency but also reduces manual fatigue and optimizes resources. Scaling data processes with AI in response to growing data volumes will result in a more efficient, quality data pipeline and the ability to generate insights faster—ultimately increasing the success rate of drug development. 

The Future of Precision Medicine Depends on Data Integration

The industry is on the brink of a shift where the value and potential of data will be increasingly unlocked by the next phase of tech advancement, particularly AI and ML driven opportunities. The next big leap in personalized medicine will rely on intelligent data solutions that do more than store and analyze information. One of the most significant changes will be the development of "living datasets" that continuously update in real time, pulling in new information from multiple sources like patient devices, lab results, and clinical trials without any lag. Datasets will not be static snapshots but evolving entities that adapt instantly to new findings, reshaping how we approach everything, from diagnostics to drug development. 

Data solutions that provide real-time data integration and analysis will power opportunities across the data ecosystem by providing a foundation of holistic, quality data for advanced applications—from accuracy of patient selection to optimization of clinical trial designs. Advancements in data modeling techniques will also help simulate patient populations, helping biopharma companies better predict treatment outcomes and tailor therapies. By leveraging these data models, researchers can accelerate clinical development, improve the precision of personalized medicine at scale, and ultimately reduce time-to-market for life-saving therapies for patients. 

Diane Lacroix is a Data Management professional with 20+ years in the Pharmaceutical/CRO industry. Currently, she is the Vice President – Clinical Data Management at eClinical Solutions, focusing on data integration, review, and cleaning so biopharma ultimately can make informed decisions in trials, and use those data assets across their research. She can be reached at dlacroix@eclinicalsol.com.