Future Proofing Clinical Data Infrastructure & Analytics: Challenges & Opportunities

The clinical trial landscape – driven by demands for speed and efficiency – is evolving, and the ways in which trials are conducted continues to transform. Advancements in clinical development have led to greater trial complexity and have intensified the challenges of managing an exponential amount of clinical trial data, which is now being captured from diversified sources, at an increasingly fast pace. As developments such as Decentralized Clinical Trials (DCTs) and novel trial approaches have become mainstream, traditional approaches to data acquisition, aggregation, cleaning, and analysis must be revisited to meet the demands of modern clinical trials.

It has become a universally accepted notion that data is the currency of the life sciences industry, and how we aggregate and analyze clinical data plays a critical role in getting treatments to patients faster. In turn, data technology architectures are evolving to support the deluge of data and complexity of today’s clinical trials while aiming to reduce cycle times.

Historically, “Data warehouse” and “Data Lake” architectures have played a significant role in clinical data aggregation and analytics in their respective ways. While these architectures have their benefits, there are also disadvantages when it comes to data quality, accessibility, scalability, and cost.

A “Data Lakehouse” aims to address the limitations and challenges associated with traditional Data Warehouses and Data Lakes. Unlike Data Warehouses, which rely on structured and processed data, and Data Lakes, which store raw and unprocessed data, a Data Lakehouse allows organizations to store both structured and unstructured data in its raw form. The Data Lakehouse architecture provides a single repository for diverse data types, such as relational, semi-structured, and unstructured data.

By combining the strengths of Data Warehouses and Data Lakes, a Data Lakehouse offers a more comprehensive and flexible data management solution, empowering organizations to derive valuable insights from diverse data sources while addressing the challenges associated with data storage, processing, and analytics. Additionally, a Lakehouse architecture supports the use of modern techniques like Artificial Intelligence and Machine Learning to accelerate Data Science and secondary use of data, beyond the submission pipeline.

Because a Lakehouse architecture reduces data silos, and increases performance, flexibility, and scalability, it is designed to better support modern clinical trials while ensuring a solid foundation for the future of digitization.

The on-demand webinar, Future-Proof Your Digital Trials with a Data Lakehouse Architecture, further examines what is driving the need for a digital data fabric and the technology infrastructure required to support clinical trial digitization. Additionally, you will gain insight into the blueprint for a Unified Clinical Data Lakehouse and how the elluminate^® Clinical Data Cloud, built on this blueprint, delivers the Lakehouse promise resulting in faster time to value and decreased cycle times.