Well-known challenges in a scattered IT-structure
When Mike Kleemann from Boehringer Ingelheim took the stage, he shared an engaging story of transformation. Operating in the pharmaceutical industry, the company faced challenges that many organizations encounter when embarking on data-driven projects. Their tools were fragmented, their processes manual, and their people often left behind in the rush to adopt to Data & AI. But through a focused and strategic approach, they turn their data landscape into a foundation for innovation and measurable success.
Pay attention to the human factor
The journey started with a key insight: their data projects weren’t hitting the mark because they overlooked the human factor. For instance, an HR app meant to assess job profiles worked well technically and impressed leadership, but it didn’t catch on because it didn’t fully align with employees’ needs. Likewise, their business intelligence (BI) initiative offered self-service dashboards, but without clear governance, it led to inefficiencies like duplicate dashboards that slowed productivity.
These challenges were further complicated by a top-down approach to Data Analytics & Data Science, where initiatives like implementing large language models (LLMs) were introduced before fully addressing the organization’s foundational readiness. While the focus on lighthouse projects drove innovation, it also created gaps in ensuring all employees were brought along in the transformation journey.
“Data Value Offices” replaced Excel with an analytics platform
Recognizing these challenges, Boehringer Ingelheim set out to become a truly data-driven organization. Their vision was ambitious: to streamline processes, reduce inefficiencies, and lay the groundwork for AI-driven innovation. Central to this vision was the creation of "data value offices", which would provide strategic guidance and ensure that projects delivered measurable impact. The first step in their transformation was to replace their reliance on Excel files with an integrated analytics platform. This platform automated ETL processes, standardized analyses, and integrated local data, creating a scalable solution that could be applied across the organization.
From best practice to scaling across the organization
The results of this transformation were striking. When the board requested a report on travel monitoring, the integrated platform made it easy to identify outliers using simple statistical methods like averages and deviations. The company will also use the platform to uncover key drivers of voluntary turnover, providing HR with actionable insights for informed decision-making. By scaling best practices across the organization, the company ensured that every team benefited from the transformation, creating a culture of collaboration and alignment.
Key Lessons
Reflecting on their journey, Mike emphasized several key lessons.
- First, he highlighted the importance of having the right user interface for AI applications, noting that tools like ChatGPT succeed because they make AI approachable and user-friendly.
- He also stressed the value of prioritizing progress over perfection, warning against the tendency to spend too much time on a single lighthouse use case at the expense of broader transformation.
- Collaboration, he argued, is essential, and a single platform with automated data processing is key to fostering it.
- Mike also underscored the importance of cross-functional leadership, with data value offices playing a critical role in maximizing the impact of their initiatives.
- Finally, he noted that while technology is important, its integration into processes and workflows is what ultimately determines its success.
This presentation was more than just a case study—it was a roadmap for organizations looking to navigate the complexities of data-driven transformation. By focusing on collaboration, alignment, and foundational readiness, Boehringer Ingelheim doesn’t just solve their data problems; they set the stage for a future where AI isn’t just a buzzword but a driver of real, measurable value.
