Biotech is one of the most data-rich industries in the world. Every experiment generates numbers, every process development run produces a trail of records, and every QA/QC activity adds to the archive. On top of this come regulatory submissions and supply chain documentation. Despite this abundance, many companies still struggle to extract value from their data.
The consequences are real. Process engineers lack visibility into efficiency bottlenecks, QA teams face headaches during audits, and executives cannot rely on consistent metrics for decision-making. Beyond compliance and performance, the absence of usable data also holds back progress in sustainability, where robust metrics are needed to track emissions, resource consumption, and waste streams.
Biotech’s struggles with data are not unique to the laboratory manager. They affect professionals across functions, from engineers to quality managers to the C-suite. Understanding why data value is so difficult to capture can help the industry avoid waste, cut costs, and meet rising sustainability expectations.
Why biotech struggles with data
The first barrier is fragmentation. Data are scattered across Excel spreadsheets, CRMs, LIMS, and even scanned handwritten protocols. Different sites or departments often follow their own formats. A validation protocol from one facility may look very different from a sister site, even if both operate under the same SOP. When information is stored in inconsistent ways, retrieval becomes slow and connections across datasets become nearly impossible.
The second barrier is short-termism. Many companies collect data to satisfy immediate needs, such as a batch record or regulatory filing, without asking how the same information might serve future purposes. If ESG reporting becomes mandatory, or if comparability data are requested during a later clinical phase, the missing context cannot be recreated.
A third issue is overreliance on external partners. Smaller biotechs often depend heavily on CDMOs to run development and production. While outsourcing is necessary, it can lead to a loss of product knowledge if the sponsor company does not set clear expectations about data collection and access. Ultimately, it is the sponsor that bears responsibility for quality and compliance, and losing visibility is a strategic risk.
Finally, there is a cultural gap. Industries such as banking, automotive, and insurance companies have developed strong “data-native” habits. Biotech, especially at the smaller scale, has been slower to integrate data science into everyday laboratory practice. Interactions with data technology companies are still not natural for many biotechs. As a result, scientists and data specialists work in parallel rather than together, and the potential value is lost.
The risk of turning data into noise
More data does not automatically lead to better insights. In fact, unmanaged data can overwhelm. Large datasets without context create more confusion than clarity.
Artificial intelligence is often presented as the solution, but applying AI to unstructured datasets rarely delivers results that are reliable in a regulated setting. Models may detect patterns, but without proper validation and reproducibility, these patterns cannot inform critical decisions.
The lesson is that data must be collected with purpose. Labs and companies need to ask early: what answers will be required in five or 10 years? If regulators are likely to request sustainability metrics, emissions data must be collected now. If later clinical stages will demand comparability, early process data must be structured so they can be traced back. Purpose turns data from noise into a strategic asset.
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Lessons for biotech professionals
Each role within a biotech company interacts with data differently, but the challenges and lessons are shared. For process engineers, the temptation is to focus narrowly on yields and product quality. Yet the real opportunities lie in collecting a broader set of metrics, such as resource inputs, efficiency losses, and material usage. These numbers not only improve day-to-day process optimization but also provide the backbone for sustainability reporting.
For QA and quality management teams, the problem often appears as a lack of governance. Fragmented records, inconsistent metadata, and missing traceability create stress during audits and slow down compliance work. When data are structured around clear vocabularies and supported by principles such as ALCOA+, the burden of validation and inspection becomes lighter, and quality systems operate more smoothly.
Executives face a different perspective. Data ownership is strategic, not just operational. Relying entirely on CDMOs or consultants to manage data may seem efficient in the short term, but it can leave companies blind to their own cost drivers, risk exposures, and sustainability performance. Leaders who invest early in lightweight but robust data strategies gain resilience and credibility as their organizations scale.
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These perspectives converge on a common theme: the need to make collaboration with data professionals a natural part of biotech culture. Smaller companies do not need an enterprise-scale data warehouse on day one. They can start lean by standardizing a handful of core entities, such as product, lot, vendor, material, and equipment, and building from there. What matters is not the size of the system but the mindset of treating data as a shared resource that connects engineers, quality teams, and decision-makers.
Data as a lever for sustainability
Sustainability is the area where missing data becomes most visible. Without supplier data, Scope 3 emissions cannot be tracked. Without detailed records on materials and disposal routes, life cycle impact assessments are impossible.
Life cycle assessments (LCAs) provide one example of how structured data can unlock value. By comparing fossil-based plastics with renewable alternatives such as polylactic acid, LCAs quantify carbon savings and support design decisions. Additive manufacturing can then be assessed for its impact on material consumption. These are not just sustainability reports; they are decision tools that influence how equipment is designed and how supply chains are managed.
The lesson extends beyond bioprocessing. For any laboratory or manufacturing site, data on resource consumption, waste, and energy can double as both sustainability metrics and efficiency indicators. A record of how much plastic is used, how much media is discarded, or how much energy is consumed by an incubator is more than an environmental report. It is also a source of process improvement.
For professionals at every level, integrating sustainability data into everyday operations avoids duplication of effort. It means that when regulators, investors, or leadership request reports, the information is already available. More importantly, it ensures that sustainability becomes a routine part of decision-making rather than an afterthought.
Toward a data-native biotech industry
Biotech does not suffer from a lack of data. It suffers from data that are inaccessible, inconsistent, and disconnected from long-term needs. The result is frustration for engineers, added workload for QA staff, and limited visibility for executives.
The path forward is not about collecting more but about collecting better. That means asking long-term questions from the start, treating sustainability data as part of everyday operations, and building bridges between scientific and data disciplines by providing training programs or incentives. Other industries already live this way. For biotech, becoming more data-native is a mindset shift rather than a software upgrade.
Companies that embrace this shift will be well-positioned to enhance compliance, reduce costs, and expedite decision-making. Just as importantly, they will be prepared to meet rising sustainability expectations without scrambling to collect missing information.
Ultimately, data is more than just compliance checkboxes. They are the connective tissue linking innovation, efficiency, and environmental responsibility. Biotech professionals who treat data as such—whether they sit at the bench, in quality management, or in the executive suite—will be better equipped to build organizations that are both competitive and sustainable.
