In today’s data-driven world, corporate data is one of the most valuable assets for the pharmaceutical industry. The challenge, however, lies in unlocking its full potential. A data strategy outlines why data are important, makes clear how to get the most value out of it, and gives direction on how to achieve this.[1] Data can be used for many purposes, including for business intelligence, advanced analytics, or cutting-edge artificial intelligence (AI) applications. [2]
While generative AI with the use of large language models (LLM’s) are already omnipresent today, other AI concepts are still emerging and many more will likely develop in future. As an example, agentic AI has been hailed as a future extension of the workforce to drive performance gains and change decision-making. [3]
As these AI trends gather momentum, there will likely be new use cases for the data that are generated in pharmaceutical research and development, since these contain many numerical and categorical scientific or clinical measurement results. Such data could be instrumental in driving further scientific discovery and research, but this relies on good data quality and stewardship. [4]
The journey toward leveraging data for innovation, therefore, begins with making the data available and interoperable. Integral to this is a corresponding data- and IT-architecture consisting of many components. Data integration has become strategically important for companies to gain an edge in areas like research, development, and clinical operations. [5] It is no longer “just data plumbing”.
There are many implementation options for a platform that provides data that are captured in different source systems. Examples are repositories, data marts, data warehouses, a data lakehouse, data fabric, and more. These options are beyond the scope of this blog post so any reference to such systems will simply use the term platform or data provisioning platform.
The need: Access to corporate data
Pharmaceutical companies capture data across a plethora of systems, designed for specific purposes. To gain insights, companies need a unified platform that consolidates data from diverse sources and provides seamless access. Many organizations already have such platforms in place or are in the process of designing or implementing them.[1]
However, as systems interface more with other systems and capture more data, the complexity grows. This underscores the importance of well-defined data management and data governance practices. [6] As data and IT architects design these platforms, critical questions arise:
- What kind of IT architecture is needed?
- Are there existing patterns or reference architectures that can guide the design?
- What capabilities and requirements must the platform deliver?
The three requirement layers of data integration
When designing a data integration platform, requirements can be categorized into three types: requirements for general capabilities, AI-specific requirements, and implementation-specific requirements. Let’s explore these layers in detail.
1. General requirements of a data platform
These are the foundational capabilities required for any data platform. They ensure that the platform can consolidate, transform, and deliver data efficiently. Key requirements include:
- Data availability: Ensuring that data from disparate systems is accessible when needed.
- Data consolidation: Combining data from multiple sources, often using different technologies.
- Data transformation: Converting diverse data formats into a common, unified structure.
- Data movement and storage: Leveraging tools to move data and apply transformations, coupled with storage solutions (physical or virtual) to house the data.
- Data modelling or cataloging: Structuring the data with a catalog or model to make it clear and usable.
- Continuous updates: Using mechanisms to retrieve new data from source systems in real-time or near-real-time.
- Handling big data: Managing large-scale data types, such as images or continuous sensor data, when applicable.
These general capabilities provide the backbone for any data integration platform and enable companies to create a single source of truth.
2. Requirements for predictive modeling, machine learning, and AI [7]
AI and machine learning (ML) use cases have advanced requirements for data platforms beyond simple integration. To have computers learn from data without human intervention, higher standards are needed on data quality and data structure. The quality of the AI is determined by the quality of what it is trained on. To support AI-driven innovation, additional requirements include:
- High-quality data: Harmonized and data sets to ensure accuracy and reliability.
- Master data management: Using reference and master data to standardize terminology across systems.
- Metadata enrichment: Adding context to data, such as time stamps, source identifiers, and more.
- Duplicate removal: Eliminating redundancies and addressing missing data points.
- Consistency in transformations: Ensuring data transformations remain identical for both training and application datasets.
These capabilities are critical for training AI and ML models, as they allow algorithms to identify patterns and generate good results.
3. Special requirements
The third layer of capabilities focuses on designing a concrete solution architecture from a schematic data architecture. In this process, specific “Special Requirements” come from facts regarding the company’s existing IT landscape, data strategy, and operational guidelines. Key considerations include:
- Strategic guidelines: For example, treating each use case as a “data product.”
- Privacy and confidentiality: Ensuring compliance with data privacy regulations and controlling access.
- Cloud vs. on-premise: Deciding whether to host the platform on-premise or in the cloud.
- Existing systems: Integrating with existing systems such as data catalogs, governance tools, and master data management systems.
- User roles: Defining the division of labor between IT and business experts and determining whether user-friendly interfaces are needed.
- Flexibility: Planning for small-, medium-, and large-scale changes to the platform as business needs evolve.
These specific requirements allow the platform to be tailored to the organization’s unique needs and to be adapted to future challenges.
Key guidelines for pharma data integration
Designing and implementing a data provisioning platform in the pharmaceutical industry is no small feat. It requires a deep understanding of both high-level capabilities and specific organizational needs. Below are some key guidelines for success:
- Understand the current state: Conduct a thorough assessment of the existing IT landscape, organizational structure, and strategic guidelines before starting the platform design.
- Invest in data quality: While improving data quality requires significant effort, it is a long-term investment that ensures sustainable use of data for future applications.
- Balance generic and specific needs: Incorporate both common capabilities and organization-specific requirements into the platform design.
- Adopt a phased approach: Implement the platform in phases, focusing on use cases to drive adoption and deliver measurable results. Given the scale of these projects, a multi-year roadmap is often necessary.
Conclusion
In the pharmaceutical industry, data integration is more than just IT infrastructure—it is a strategic foundation for innovation [8]. Whether an organization is visualizing data for business intelligence, making predictions through analytics, or leveraging AI, an effective data integration platform is the key to unlocking value. By addressing foundational, AI-specific, and implementation-specific requirements, companies can build platforms that not only meet today’s needs but also scale for tomorrow’s opportunities.
The future of pharma depends on how well we manage, govern, and utilize our data. Let’s ensure we’re building platforms that are ready for the challenges—and opportunities—ahead.
About the authors:
Dr. Christian Ikier is Associate Director, Principal Consultant for Clinical Services at PharmaLex. He supports life sciences companies with their digital transformation and with making scientific data more available, findable, and manageable. A chemist by training, Christian has spent his career in IT in the life sciences, including more than 15 years as a consultant for Osthus, now part of Cencora.
Mark de Graaf is Senior Consultant for Clinical Services at PharmaLex. With his expertise in life science informatics and information architecture, he helps clients with their data strategy and leads digital transformation projects.
References:
[1). Crafting a Robust Data Strategy in Pharma: A Prescription for Success, Philipp-Andrin Sgier, 2024
https://www.pwc.ch/en/insights/data-analytics/pharma-data-strategy.html
[2] Modern Business Intelligence: Big Data Analytics and Artificial Intelligence for Creating the Data-Driven Value | IntechOpen, Ahmed A.A. Gad-Elrab, 2021
[3] Top Strategic Technology Trends for 2025: Agentic AI, Tom Coshow, Arnold Gao, Lawrence Pingree, Anushree Verma, Don Scheibenreif, Haritha Khandabattu, Gary Olliffe, 2024
https://www.gartner.com/doc/reprints?id=1-2K8Y7LEY&ct=250212&st=sb
[4] Scientific discovery in the age of artificial Intelligence, Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao GaoKexin Huang,Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli18, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai,Debora Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Veličković, Max Welling, Linfeng Zhang, Connor W. Coley,Yoshua Bengio & Marinka Zitnik, 2023,
https://www.nature.com/articles/s41586-023-06221-2
[5] The Transformative Power of Data Analytics in Clinical Trials, Melissa Hutchens, 2025
[6] Data integration dogged by complexity and vital to business, Stephen Pritchard
, 2022,
https://www.computerweekly.com/feature/Data-integration-dogged-by-complexity-and-vital-to-business
[7] What Is AI-Ready Data? And How to Get Yours There , Rita Sallam, 2024
https://www.gartner.com/en/articles/ai-ready-data
[8] Modern data management: the foundation for life sciences innovation, Rohit Dayama, 2025
