In Development Sciences, digital transformation is no longer just about introducing new tools. It is about creating the foundations that allow scientific data to be captured, connected, understood and reused across teams.
For Christian Pilger, Director, Solution Architecture Lead within Development Sciences Data & Digital Strategy at AbbVie Deutschland, solution architecture sits at the intersection of systems, data and scientific process. His role focuses on two closely linked areas: building the right infrastructure for structured data capture, movement and storage, and shaping the data architecture through consolidated terminology, ontologies and effective data models.
A central challenge is that scientific data often sits in silos. These may be technical silos, where information is stored across different systems, or logical silos, where teams use different names for the same thing — or the same name for different things. This lack of harmonization makes it harder to analyse data interactively, automate reporting, and apply AI and machine learning effectively.
Rather than looking for one single platform to solve every problem, Pilger’s team has adopted a federated infrastructure approach. This means selecting fit-for-purpose platforms for dedicated needs, then connecting them through in-house infrastructure. The aim is not simply to digitize existing processes one by one, but to analyse current workflows, identify opportunities to standardize and consolidate them, and then design future-state processes that are more efficient.
One of the biggest opportunities lies in structured data capture. Moving away from free-text documentation, spreadsheets and fragmented lab practices toward structured templates and harmonized terminology can make data far more valuable. However, this is also a change-management challenge. Scientists are used to their own lab language and ways of working, so successful transformation requires deep understanding of scientific practice as well as technical expertise.
Pilger describes AbbVie’s approach as “grow as you go.” The team starts with specific use cases, prioritizes them with management, and then scales the resulting infrastructure horizontally across similar applications and vertically by adding new capabilities. One example is an electronic request system originally designed to standardize how formulation scientists ordered analytical tests. Over time, it was expanded to other teams and later used as the basis for a stability study planning tool.
For a modern Development Sciences organization, Pilger sees four essentials: structured data capture and sharing, globally consolidated terminology, central availability of logically integrated data, and preservation of context. Together, these foundations support the FAIR principles — making data findable, accessible, interoperable and reusable.
As Development Sciences teams look to the future, the goal is clear: create a digital and data foundation that supports today’s practical needs while enabling tomorrow’s AI-driven possibilities.
