
Data is the backbone of clinical trials, driving decision-making from design to submission. Traditionally, the data management life cycle includes database creation, data entry, and database lock – all culminating in regulatory submissions. This foundational process ensures that trial data is of high quality and fit for analysis, the landscape has evolved significantly, however, in recent years. New data sources, including wearables, synthetic data, and real-world evidence, have emerged, adding both opportunities and challenges to the equation. Despite these advances, the persistent issue of siloed operations within and across organisations hampers progress. Data management teams, biostatisticians, and external vendors often work independently, with limited integration of processes or technology.
This isolation is compounded by the increasing complexity of trial designs and the sheer volume of data being generated.
The silo effect in clinical trials arises from organisational structures, varied technological capabilities, and the inherent complexity of managing diverse data types. “Data management is a very critical component of the trial life cycle,” explains Bazgha Qutab, who leads drug development in Europe as principal at ZS Associates, a management consulting and technology firm that partners with companies to bring together data, science, technology, and innovation for better outcomes. “The data management space itself in the past 10 years has been inundated from how do we actually build a robust data management infrastructure to how we do data management in an automated way and in scale.”
Data management focuses on primary trial data collection and preparation for submission, while biostatisticians work on analysis and interpretation. Meanwhile, external vendors such as Contract Research Organisations (CROs) often operate using their proprietary systems, which may not easily integrate with sponsor systems. These independent operations create barriers to collaboration and hinder the seamless flow of information.
Adding to the challenge is the increasing reliance on innovative trial designs. Adaptive trials, synthetic data integration, and decentralised trials require the collection and analysis of more diverse data types. The current infrastructure struggles to support these designs, further highlighting the need for transformation.
Breaking down silos: The path forward
To bridge these silos, the industry must address both cultural and technological barriers. Collaboration between data management, biostatistics, and external vendors should be underpinned by robust data standards, modernised platforms, and streamlined processes. “There’s a lot more data modalities coming in now as you start to integrate digital endpoints, real world data, omics, and eventually programs experimenting with synthetic control data arms. As you get that pulled in, there’s just a quantity of data and then continuing to figure out the quality of that data,” explains Mike Martin, who leads global drug development practise area as principal at ZS Associates.
Standardisation is a cornerstone of efficient data management. Initiatives like Clinical Data Interchange Standards Consortium (CDISC) standards, including ADaM and SDTM, have brought significant progress in harmonising data for regulatory submissions. “Data standards take a pretty critical role now; of course, data standards also get vague as new data types come in, especially the digital data, synthetic data and simulated data, as well as we see pharma companies maintain multiple versions of data standards so governance needs to play a critical role in data management” adds Qutab. However, as these new data types become integral to trials, these standards must evolve. Ensuring that emerging data types align with established frameworks will facilitate integration and maintain data quality across the trial life cycle.
At the same time, digital transformation is revolutionising clinical trials, providing opportunities to enhance transparency and efficiency. Advanced platforms consolidate multiple functions – data collection, querying, analysis, and submission – into a unified system. This eliminates the need for disparate systems and reduces the risk of errors and data loss during transitions.
“Digital comes in the early design side of the development life cycle and helps us create the whole experience around the design,” says Qutab. Platforms such as Medidata, Veeva, and modernised electronic data capture (EDC) systems are leading the charge. By enabling seamless integration of electronic health records (EHR) and EDC systems, automated data capture and data collection, these platforms support end-to-end data management and promote collaboration. For example, patient data collected through wearables can be directly integrated into trial databases, ensuring real-time insights and minimising manual data handling. “That is where digital with the patient interaction is going to become a game changer and where device data, wearables, digital data collection, essentially, is changing the whole space of trials and how we run trials,” she continues.
AI and automation hold immense potential to streamline data management processes as well. Tasks such as data cleaning, query resolution, and statistical analysis setup can be automated, freeing resources for higher-value activities. Real-time data validation, supported by AI, ensures data quality at the point of collection, reducing the reliance on manual interventions during database lock. “I do believe that AI and these technologies will help us move that to scale, and not make these one-day improvements that we’ve been seeing in the past years – it’s going to have a massive impact on the [drug development life cycle] timelines, in areas of data automation, data risk flagging, and content writing we’re seeing AI POCs ready to scale. AI will also begin to play a big role in synthetic control arms and digital twin simulations,” adds Qutab.
Moreover, AI-powered analytics enable sponsors and biostatisticians to identify patterns and insights more quickly, improving the speed and quality of decision making. This capability is particularly critical in adaptive trials, where rapid adjustments based on interim results are essential.
Breaking down silos requires more than technological solutions, however; it demands a cultural shift. Stakeholders must recognise the interdependence of their roles and commit to collaboration. Sponsors can take the lead by creating centralised Centers of Excellence (CoEs) for data management and biostatistics. These CoEs can function as hubs for best practices, fostering communication and shared objectives across teams.
Additionally, sponsors must establish clear expectations and standards for external vendors. Insourcing key data functions, or requiring vendors to use sponsor systems, can improve data transparency and consistency. As Martin notes, sponsors increasingly seek to retain control of data, enabling their teams to work more efficiently and effectively. “We’re seeing sponsors have more and more of a driver’s seat at the table where they’re requiring vendors to conform to certain quality standards,” agrees Qutab.
The benefits of bridging silos
The integration of data management, biostatistics, and external vendors offers far-reaching benefits for clinical trials and, ultimately, patients. By fostering collaboration and leveraging modern technology, the industry can significantly reduce trial timelines and costs. “At the end of the day, we’re doing it for the patients, right?” says Qutab. “The better quality, the more [we] speed up the data management life cycle, this means that the patients get therapies faster, they get results faster, the submissions happen faster.”
Enhanced data quality and integrity, driven by real-time validation and centralised management, minimises errors and discrepancies, ensuring that data remains accurate and trustworthy. “We’re seeing a lot of sponsors focus on ‘how can we clean that data closer to when we actually acquire it versus at the end when we go to database lock’ and so that allows you to be ahead of the game and to get better quality data throughout the whole process,” explains Martin. Simultaneously, increased efficiency through automation and streamlined workflows eliminates redundancies, enabling teams to focus on strategic priorities and advancing trials at a faster pace. This acceleration not only enhances operational performance but also contributes to faster time-to-market for lifesaving therapies, ensuring patients gain access to critical treatments more quickly. Moreover, improved decision-making is facilitated by access to integrated, high-quality data, empowering biostatisticians to conduct rigorous analyses that inform trial outcomes and guide future drug development strategies.
“Data standards take a pretty critical role now; of course, data standards also get vague as new data types come in, especially the digital data, synthetic data and simulated data.”
Bazgha Qutab
While the benefits are clear, achieving this vision is not without its challenges. Transitioning to modern platforms and processes requires significant investment and organisational change. Sponsors must balance the need for innovation with the realities of regulatory compliance and resource constraints. “I think the next two to three years are going to be very critical for data management transformation, with a big transformation wave towards data automation, technology modernisation and use of AI across the data life cycle,” says Qutab.
Moreover, not all organisations will move at the same pace, explains Martin. Early adopters will set the stage, but the broader industry must follow suit to achieve widespread impact. Collaboration among stakeholders, including regulators, will be essential to standardise new practices and technologies.
The next few years will be pivotal for the transformation of data management in clinical trials. Advances in technology, coupled with a shift towards collaboration, promise to unlock new levels of efficiency and innovation. As Qutab also observes, the convergence of traditional data management with modern AI and digital platforms will initially be disruptive, but ultimately transformative.
Sponsors, vendors, and regulators must, strive to work together to build a cohesive ecosystem that effectively supports the complexities of modern trials. By breaking down silos and embracing innovation, the industry can then deliver on its ultimate goal: improving patient outcomes through faster, more effective drug development.