Steven Dublin and Joel Eichmann, Green Elephant Biotech, discuss using data to drive innovation and sustainability in biotech.
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Many biotech companies generate enormous amounts of data, but often struggle to extract actionable insights. Why do you think this is the case?
Joel: Generating data is rarely the problem; making it usable is. Biotechs collect information across research, process development, QA, supply chains, and business functions. Yet this information often ends up isolated in different formats—Excel sheets, CRMs, LIMS outputs, or even scanned handwritten reports. Site-to-site differences make it worse. A protocol from one facility may look very different from another, even if both follow the same SOP. The result is that data are technically available but practically inaccessible.
What are the most common mistakes you see biotech companies make when collecting or analysing data?
Steven: The biggest mistake is short-termism. Teams focus on collecting what is needed for the next report or experiment without the mandate to ask how those data could serve the company five years later. For example, vendor qualification is an area where questions about traceability or sustainability are left partially or completely unaddressed. Once the data is missing and decisions are made, they cannot be simply retrofitted.
Joel: Small companies also tend to assume their CDMO will handle everything. That is risky. The CDMO executes what you ask, but it is the sponsor company that owns the product knowledge. Failing to set clear expectations at the beginning means you may lose sight of critical process data that define both product quality and future compliance.
How can biotechs avoid turning valuable data into “noise”?
Joel: It starts by defining the long-term questions. Ask early: Which answers will we need in 5 or 10 years? That could be emissions data for ESG reports or yield data for comparability studies. When you know what you are looking for, data collection becomes purposeful.
Steven: Another pitfall is assuming AI will solve everything. Feeding unstructured, unvalidated data into a model does not create insight, it creates confusion. In regulated environments, validation matters as much as the algorithm. Data is only valuable when it is collected with clear intent and kept in a form that can be verified later.
How did your team use data to identify areas for reducing carbon footprint in your cell culturing solutions?
Joel: Our first product, the CellScrew, is a single-use system for adherent cell culture made from plant-based polylactic acid (PLA) rather than fossil-based polystyrene (PS). We relied on lifecycle assessments (LCAs) to quantify its impact. These showed around 83 percent lower upstream emissions than PS and about 44 percent less CO₂ at incineration. Additive manufacturing added another reduction, cutting raw material use by up to 80 percent for the same culture surface.
Steven: The CellScrew also consolidates cultivation vessels: one unit can replace 57 T-175 flasks or 12 roller bottles. This means less plastic, less handling, and less waste. By combining LCA data with performance metrics, we can demonstrate that lower carbon footprint and higher process efficiency go hand in hand.
Are there areas in pharmaceutical development where sustainability data is often overlooked or undervalued?
Steven: Supply chains are the obvious blind spot. Scope 3 emissions are the largest part of the footprint, yet many companies have little visibility beyond their immediate suppliers. Another area is end-of-life. Single-use systems are essential for sterility, but disposal is treated as an afterthought rather than part of the lifecycle.
Joel: For smaller firms, the problem is often resource constraints. ESG reporting is time-consuming, so it falls behind other priorities. Still, if sustainability data is integrated into vendor qualification and batch documentation from the start, it can be collected once and used many times.
How do you balance innovation with sustainability when data suggest a trade-off?
Joel: We rarely see a true trade-off. The CellScrew is a good example: it reduces plastic consumption and facility footprint while making processes more efficient. Sustainability was not an add-on, it was part of the design.
Steven: Data transparency makes the difference. When you measure both process efficiency and environmental impact, you often discover that the same design features improve both. What looks like a trade-off disappears once you have the full picture.
Why do you think many biotechs still struggle to capture value from their data, despite advanced tools and analytics being available?
Steven: Tools cannot compensate for weak foundations. If data is fragmented, inconsistently defined, or missing context, analytics will deliver inconsistent answers. In regulated environments, reproducibility is non-negotiable, and unstructured data cannot be validated.
Joel: This is not only about harmonisation, it is about governance. Companies need clear definitions, audit trails, and integrity principles such as ALCOA+. Without them, analytics results remain exploratory at best and cannot be trusted for decision-making.
How can the broader biotech industry improve its approach to data to foster innovation and sustainability?
Joel: Collaboration is key. We need practical standards for sustainability data, just as the industry once created standards for extractables and leachables. A minimal data package from vendors could include material composition, part mass, sterilisation method, and disposal pathway. With that information consistently available, sustainability reporting becomes manageable.
Steven: Small companies also have an opportunity to leapfrog. With less legacy data, they can set up lean structures early, governing just a handful of core entities such as product, lot, vendor, material, and equipment. That creates a base they can build on as they grow.
Joel: The industry should view sustainability not as a reporting burden but as a design principle. If data is collected with this in mind, it supports innovation, compliance, and environmental responsibility at the same time.
Closing thoughts
Steven: Data is more than a compliance requirement. It is the connective tissue linking innovation, scalability, and sustainability. Companies that build purposeful data strategies early will be better prepared for both regulatory demands and operational challenges.
Joel: And technologies like the CellScrew show that functionality and sustainability are not separate tracks. The same datasets that optimise yields also provide evidence for lower environmental impact. Biotechs that embrace this dual role of data will be better positioned to deliver therapies efficiently, sustainably, and at scale.