By Sam Karbaron, Senior Manager, and Toi Neibler, Senior Manager, Deloitte

In the rapidly evolving world of pharma research and development (R&D), data has increasingly been recognised as a strategic asset. A successful drug is a testament to the rigorous data management throughout its development. Despite the vast possibilities that clinical data holds, its usability remains a challenge that prevents pharma organisations from tapping into its full potential. This blog explores the significant advantages, including improved data quality, consistency, and enhanced analytical capabilities, of developing harmonised clinical data products and how evolving data management practices and modern platforms are breaking down silos, enabling accessible insights and faster, data-driven decisions.

Building a solid foundation: Clinical data products

Modern clinical trials generate vast amounts of data, with Phase III trials producing an average of 3.6 million data points.¹ However, this data is often stored in incompatible formats, limiting its use for insightful analysis.² In today’s technology-enabled R&D environment, without access to harmonised high-quality data, the full potential of AI and machine learning in life sciences cannot be realised.

Our previous blogs on clinical data management have explored the opportunities and challenges that organisations face in reusing their clinical data and the business value of harnessing data to enhance the digitalisation of R&D activities.^3,4  Underpinning the effective reuse of data and digitalisation of R&D is the creation of reusable, analysis-ready data products that bridge diverse sources, formats, and levels of detail, enabling comprehensive analysis

Unlocking opportunities: Driving value across R&D

Applying AI to harmonised clinical datasets, offers numerous opportunities in R&D, such as developing more targeted treatments, improving patient outcomes, and enhancing regulatory compliance. This can lead to increased productivity and cost-efficiency in drug development as demonstrated in Figure 1.

Figure 1. Opportunities unlocked through harmonised clinical data products

Source: Deloitte analysis, 2025

Overcoming key challenges: A pragmatic approach

To effectively implement clinical data harmonisation, a clear strategy and roadmap with defined objectives should be developed, ensuring alignment with organisational goals and maximising value.

Organisations should consider the following three factors as they approach their own data harmonisation journeys:

I. Business priorities

These priorities will vary depending on the organisation's pipeline, market competitiveness, and data usage across teams. For example, continuous glucose monitoring data collected in a diabetes trial can be valuable in other therapeutic areas where glucose levels are relevant, demonstrating the potential for data reuse across diverse clinical trials.

Prioritise therapeutic areas where data harmonisation can have the greatest impact, such as those where new insights could accelerate drug development or where harmonised data can support regulatory submissions or post-market surveillance activities. This prioritisation informs which specific data domains (e.g., medical history, lab results, adverse events) should be prioritised for harmonisation.

II. Data maturity

As data quality varies significantly, organisations should assess the maturity, completeness, and adherence of their data to data standards, enabling them to prioritise efforts and maximise impact.

Fortunately, established clinical data standards provide a foundation for harmonisation efforts. For example, the Clinical Data Interchange Standards Consortium’s (CDISC) Study Data Tabulation Model (SDTM), provides a standardised method for structuring and submitting data.⁵ Additional guidance on terminologies and methods, such as those from the Medical Dictionary for Regulatory Activities (MedDRA) and International Organisation for Standardisation (ISO), can also be used for items including dates and countries.

III. Data governance and privacy

Ensure datasets balance research value with data privacy and governance, and adhere to relevant international (GDPR, OECD Privacy Guidelines), regional (HIPAA), and sector-specific regulations (e.g., NHS Digital's Code of Practice) while employing a risk-benefit approach.

Charting a path forward

Harmonising clinical trial data is a complex and time-consuming process, highlighting the need for streamlined, standardised approaches and for industry leaders, to actively develop innovative solutions. However, establishing a universal harmonisation framework can be challenging due to the constantly evolving pharmaceutical landscape. Therefore, organisations need to be both logical and flexible, see Figure 2.

Figure 2. Overcoming challenges with a tailored approach to harmonisation

Source: Deloitte, 2025

Having a strategic transformation plan is crucial if organisations are to realise the full value of data harmonisation. Initiatives identified in the plan should be prioritised based on their value, technical feasibility and strategic alignment. Consideration also needs to be given to developing or acquiring the skills and talents needed for data harmonisation (a combination of data engineering, data science and clinical data knowledge).

Exploring opportunities for your organisation

Connecting disparate data points across therapeutic areas and disease indications paves the way for groundbreaking research, expanding treatment options and reaching wider patient populations. A structured approach to harmonisation, coupled with robust data management can empower R&D teams to better leverage cutting-edge technologies like AI and machine learning. Investing in the necessary capabilities, skills and talent, alongside strong leadership and governance, is crucial to unlocking the full potential of clinical data and, ultimately, delivering better patient outcomes.

Acknowledgements
The authors would like to thank Myrian Gaspar and Tanisha Patel for their contributions to the research and drafting of this blog.

Sam Karabon, Senior Manager

Sam is a Manager within Deloitte’s Technology and Transformation practice, specialising in AI & Data strategy and implementation within the Life Sciences industry. Proven ability to lead complex projects, manage global teams, and deliver innovative solutions that drive business growth.
Prior to Deloitte, Sam gained experience in the consumer goods sector, having previously spent a year working at Procter & Gamble in manufacturing and supply chain roles. He also holds a master’s degree in chemical engineering from the University of Leeds.

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Toi Neibler, Senior Manager

Toi is a Senior Manager in Deloitte’s Engineering, AI & Data Practice where she is focused on best practice delivery of large-scale patient engagement initiatives, from agile transformations and service design projects to designing and implementing operating models for pharmaceutical and life sciences clients. She has experience in clinical data, historical clinical trial data harmonisation and AI fluency in HCLS. Prior to coming to Deloitte, Toi specialised in translational and clinical research focused in rheumatology, neurology, and imaging.

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1 Tufts (2021). Rising Protocol Design Complexity Is Driving Rapid Growth in Clinical Trial Data Volume, According to Tufts Center for the Study of Drug Development. GlobeNewswire News Room.

2 Cheng, C., Messerschmidt, L., Bravo, I., Waldbauer, M., Rohan Bhavikatti, Schenk, C., Vanja Grujic, Model, T., Kubinec, R. and Barceló, J. (2024). A General Primer for Data Harmonization. Scientific data, 11(1).

3 Reusing clinical data to accelerate pharmaceutical R&D - Thoughts from the Centre | Deloitte UK

4 The value of efficient data management in digital drug development era - Thoughts from the Centre | Deloitte UK

5 Cdisc.org. (2024). SDTM | CDISC.