Are “Best-of-Breed” Point Solutions Enough, or Is Clinical Research Ready for a Unified Data Experience?

The clinical trial data landscape has changed — drastically and rapidly. Sponsors and CROs are no longer just collecting data; they’re wrestling with an explosion of it, sourced from multiple technologies, patients and geographies.

The clinical data management system market is projected to nearly triple by 2033, reflecting rising demand for speed, interoperability and digital-first clinical trial models.

In this Xtalks Spotlight interview, Wayne Walker, Senior Vice President of Data Experience at Medidata, walks us through what it will take to unify this complexity. With a background in transforming clinical data systems, Wayne offers a clear-eyed view into how sponsors can move from fragmented systems to integrated data experiences — and how AI may fundamentally shift operational execution in clinical research.

The Data Boom Is Here — But So Are the Bottlenecks

“In clinical research in general, the amount of data has exploded pretty much over the last six to seven years. And it’s all with a point of getting closer to the source. And that source could be directly from electronic systems that have already captured that data. It could be using technology to get closer to the patient.”

Wayne explained that the data explosion has led to challenges in multiple directions — particularly around the siloed nature of the data, the speed and scale at which it’s being collected and the growing difficulty of making sense of it all while trying to unify disconnected systems.

Not only that, but “the complexity of the study designs has increased over time and everybody is under pressure to do more with less and bring in timelines. It’s really caused a lot of challenges in that area.”

Wayne added that the growing use of innovative technologies has introduced new layers of regulatory complexity — making it even harder for the industry to manage modern clinical research demands.

When “Best-of-Breed” Isn’t Always Best

When asked about the biggest limitations of using multiple point solutions, he pointed out that it not only reintroduces data silos but also complicates the experience for all stakeholders involved, especially those working directly with patients.

Sites, in particular, are overwhelmed. “When you think about the sites that are actually engaged with patients, the number of systems that they’re having to deal with really increases and they’re already having great demands put on their time, and we want them to spend more time with the patient.”

He also noted that best-of-breed setups increase operational burden: “The other area is the reconciliation of all that data. When you’re looking at best-of-breed, that increases operational resources that are required to manage all of these best-of-breed capabilities.”

Wayne said there’s an opportunity to consolidate fragmented systems into a more unified structure — one that simplifies site use and reduces the heavy operational lift required for data reconciliation.

Defining a Unified Data Experience

When asked what a unified data experience means in practice, Wayne made an important distinction: “There’s two areas of integration. One is to reduce the duplication in data collection. So that’s more of a point-to-point type of integration.”

He elaborated, “If a site is entering data in an eCRF [electronic case report form], but they’re also having to randomize a patient, they’re often entering the same data in both of those systems. So there’s a duplication of effort there.”

While point-to-point integrations are useful, Wayne acknowledged that unless tech consolidation happens, sites will continue to face redundant workflows, requiring point-to-point integrations just to reclaim efficiency.

“But then, from the data experience perspective, what we see as truly being a data experience is putting technology to sort of integrate all of those data sources. And then as soon as that data is aggregated on a platform, treating that data as if it’s like a first-class citizen, whether it was organically collected in that platform or whether another technology was used.” That means treating it with the same functionality and accessibility as if it were natively generated on the platform.

Wayne described the unified data experience as a model where stakeholders who manage and integrate data — regardless of whether they’re using Medidata’s platform or a third-party system — can work from a centralized, integrated patient dataset. This setup, he said, should also allow users to carry out tasks like creating queries, regardless of the original source system.

AI’s Role in Study Execution

“We’ve long been trying to crack the nut of automating and leveraging AI to perform study build activities. That nut this year has been cracked at least on the Medidata platform.”

Wayne said automation now goes beyond basic elements like edit checks. Platforms can now consume the protocol, generate eCRFs, apply validations and test the study — all automatically.

AI is also streamlining routine trial operations. Medidata has built strong AI capabilities over the years, particularly in areas like medical coding, where reliable training data has enabled consistent automation. “We have a great training data set that can be used to automate that coding, whether it be in lesion detection, whether it be an anomaly detection, whether it be in things like transformation, whether it be in reconciliation that we’ve talked about, really looking at the sort of AE [adverse event] ConMed [concomitant medication] reconciliation and leveraging AI to automate those tasks,” he illustrated.

Looking ahead, Wayne said the next step is not just to apply AI to tasks people already perform, but to use it to conduct the operational execution of clinical research itself.

“And that’s going to be the great leap in leveraging AI and how we modify how we do things today. And really when we reach that point, that’s when we’ll really hit the ground running and be more streamlined and get true treatments and drugs to patients quicker.”

Future-Ready: What’s Next for Sponsors and CROs

Most sponsors and CROs are already rethinking how they manage data, noted Wayne. As reliance on eCRFs and lab data alone fades, organizations are being pushed to adopt more integrated, future-ready strategies.

Wayne suggests three guiding priorities for sponsors and CROs aiming to modernize their data strategy:

• Choose interoperable technology that reduces duplication in data collection and supports industrial-scale data aggregation — enabling a unified view of the patient dataset
• Maximize the use of AI and automation to fully unlock their potential in streamlining clinical research activities
• Work with long-term partners instead of transactional vendors — collaborators who can align with an organization’s broader data, operational and efficiency goals

Wayne’s vision reflects a shift already underway — from fragmented tools to integrated systems, and from data overload to cohesive strategy.

This article was created in collaboration with the sponsoring company and the Xtalks editorial team.