Roland Rosenau, SE Director at EMEA, discusses the evolution of data management in life sciences organizations.
When discussing artificial intelligence (AI) in business, industries that are common at the forefront of innovation include healthcare, large tech companies and media giants. However, when it comes to harnessing the true analytical power of AI, there is probably no more sectors than life science.
From the early days of research, data has been at the heart of scientific discoveries. The process has not been changed: collection, analysis, interpretation, iteration. Today’s Life Science Labs are not that different from past labs in that respect, but face two unprecedented challenges. And explosive data growth.
These challenges don’t go hand in hand. Scientific advances thrive with data, and in today’s AI-enhanced environment, the more data you can use for analysis, the better.
However, due to limited budgets, life science researchers need to balance access and affordable prices in a delicate balance. This means making smarter choices when it comes to how you store, protect, and access your data.
The challenges facing the industry: Data growth, cost-effectiveness, and ease of use
Perhaps the most pressing problem facing researchers today is the vast amount of data generated. Scientific instruments are increasingly sophisticated, high-throughput, automated, and generate exponentially more data than they did a few years ago. Furthermore, organizations have been reanalyzing existing datasets well, further reinforcing the need to extract new insights from old data and keep and organize them all.
The Life Sciences Analytics market is projected to grow from $11.97 billion in 2025 to $248.5 billion by 2034, with a combined annual growth rate of 8.47%. It’s not just growth, it’s an avalanche. And that data has not been created to be idle. It must be accessible, analysable and stored in a way that supports both current and future use cases. However, storing all this data, especially in the layer of high-speed, high-performance storage, can quickly outweigh the financial resources of many labs.
Life Sciences organizations are often run with grants, donations, or public funds. All of this has become difficult to bring in in recent years. Even if an organization knows exactly what storage will best support their work, the gap between needs and budgets is often forced to compromise.
Next is the ease of use. Most life science teams do not have access to large, competent IT departments. Storage systems need to be intuitive, quickly deployed and flexible enough to grow with minimal manual intervention.
Hybrid infrastructure: smart storage, smarter growth
The answer to these complex challenges lies in hybrid data infrastructure. It is scalable, cost-effective and built with lifecycle management in mind. The reality is that research organizations cannot afford to lose or dispose of data. Every part-time job has potential value. Not today, but five or ten years from now. That’s why scalability is important.
The most successful storage solutions allow teams to start scale on small scales when needed, without taking on major upfront investments. Scale-out systems, leasing models, and wage wage subscriptions have become essential tools in this landscape, allowing researchers to grow their limited budgets without compromising functionality.
Object storage, especially S3 compatible storage – has become the de facto standard for large and affordable repositories. Removes constraints related to block size or file system, enabling automated lifecycle policies. These policies allow organizations to move more frequently accessed data to a lower cost storage tier on on-premises, private clouds, or multiple hyperscaler platforms, on-premises, or multiple hyperscaler platforms.
The new goal is not to know how to remove data, but to keep everything and manage it smartly. By examining metadata such as the last access date, organizations can automatically migrate data that has not been accessed in months or years. Some datasets may need to remain available indefinitely, such as ongoing research and datasets related to compliance obligations. However, the rest can be safely archived at a more cost-effective layer without sacrificing accessibility.
This hybrid approach is also effective when building private cloud infrastructures. Organizations can build their own clouds and create “cheap” storage tiers internally. Alternatively, you can leverage public cloud for deep archive storage. Many choose a combination of both and offload the rest to a centralized system while maintaining their own system for frequently used data. The days when bigger drives mean lower costs are gone. Costs are no longer reduced at the same rate as data is increasing. A 60TB drive costs more than twice as much as a 30TB drive. We have reached a turning point. Capacity is no longer the only important metric. Efficiency, agility and long-term sustainability are just as important now.
Prevent data loss
Of course, conservation and scaling are not the big picture. The cost of data loss is unsurprising in life sciences where data can drive groundbreaking treatments and diagnosis. Whether it’s a cyberattack, natural disaster, or accidental deletion due to a simple human error, losing important research data can bring the project back into the year or even go completely off track. Unlike natural disasters and human error, cyberattacks present an additional layer of complexity. It is often unknown when the attack began, how deep it became, and which files were affected.
Therefore, data protection strategies must exceed traditional backups. Snapshot-based backups are a popular solution because they are fast to run and are easy to restore. However, in the age of ransomware, snapshots must also be protected by themselves. This is where cyber-resilient backup strategies come into play. This hides the snapshot or makes it unavailable for recovery while preventing attackers from accessing it.
Ultimately, every life sciences organization needs a clear and practical backup and restoration strategy. It coincides with the storage lifecycle and takes into account not only recovery times but also attack detection and containment.
A multi-storey long-term approach
Life Sciences organizations must embrace new ways of thinking as they continue to generate and rely on more data. This views storage as an evolving multi-tier ecosystem, not as a fixed asset. From hot data to cold archives, the ability to manage information across a wide range of levels of accessibility and costs is the determinant of whether research is accelerated or delayed.
That means thinking beyond the initial price per terabyte. Total ownership costs include migration fees, backup costs, hardware refresh and license model. Fine printing is important and in life sciences there is no high interest. In addition to its urgency, regulatory initiatives such as the European Union’s NIS-2 Directive have raised accountability bans by holding individual board members personally liable for data loss caused by cyberattacks. This shifts data protection from operational challenges to executive-level responsibility, enforcing organizations to take decisive action now. Or risk serious consequences right away.
Researchers are not only solving for storage, but also for the future of medicine. They need infrastructure that is as resilient and looks as positive as science. By adopting hybrid systems, lifecycle policies and cyber-recilional backups, life science organizations can ensure that their most valuable assets, data, are always available, protected and working for the lives they are trying to save.
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