Posted: September 2, 2026

Insights from the “Data Infrastructure in Life Sciences” webinar series – episode two

When companies are on the digital GAMP journey, they discover that most roadmaps evolve incrementally. They begin with point solutions, each justified as a contained, technical sprint. Then they deploy the historian. Next, they tag the sensors. Now, it’s time to add a visualization layer. It’s often a series of checking off boxes before moving on to the next technical component.

In contrast, building a business case for data infrastructure is a fundamentally different exercise. A data historian is a sprint: a specific, time-boxed technical deliverable with a narrow capability footprint. Data infrastructure, on the other hand, is a marathon: a multi-stage, cumulative program where the ROI compounds over time and the architecture becomes the backbone of enterprise performance.

So, what happens when you rely on a series of sprints to complete a marathon?

It’s exhausting. The results are suboptimal. Organizations that do this have to confront fragmented data models, siloed investments, and a perpetual struggle to justify the "next phase" to leadership — because the value story was never written in the first place.

In episode two of our Data Infrastructure in Life Sciences webinar series, Joerg Triebnig presents a structured framework for translating data infrastructure investments into enterprise profitability language. He explains how a data infrastructure value roadmap is more than a Gantt chart of IT deliverables. The value roadmap needs to be a living document that answers four questions at every stage:

1. What is the business outcome we are targeting?
   OEE improvement, yield increase, energy cost reduction, compliance audit readiness, etc.
2. Who is the consumer of the data?
   Line operator, site quality manager, corporate finance, regulatory body, etc.
3. What does success look like — and when?
   Defined KPIs, timelines, and accountability
4. How does this sprint connect to the next?
   Architectural decisions that preserve optionality and reduce future integration cost

Note that the technical roadmap does not lead the value roadmap. It should be the other way around.

"You'll find the technical roadmap has to be tailored to the value roadmap. It's the outcome and the value roadmap that gets projects approved."
– Rory Sheehan

The conversation in episode two is ultimately about alignment with the following takeaways:

• Reframe the business case. Stop justifying data infrastructure as a technology investment. Start presenting it as a capability that drives product quality, yield, energy efficiency, and compliance—and quantify each outcome.
• Design sprints backward from outcomes. Every initiative in your data roadmap should be scoped around a named business outcome, not a technical deliverable. The technology is “the how.” The outcome is “the why.”
• Define the consumer before the architecture. Before deciding what data to unify or how to structure your namespace, identify who needs what data, in what context, and at what cadence.
• Protect architectural optionality. Each sprint should be designed so that the next sprint is cheaper and faster. Value-focused sprints are not isolated projects, but rather stages in a compounding infrastructure investment.
• Bridge the language gap. The technical community needs to get comfortable translating architecture decisions into profitability language: Operating cost, capital cost, yield, energy intensity. These are the terms that unlock executive sponsorship.

Missed the beginning of the journey?

Watch the first episode and learn the essential steps toward transforming operations in your company.