Roland Rosenau, SE Director, EMEA at Quantum, discusses the evolution of data management for life sciences organisations.
When we discuss artificial intelligence (AI) in business, industries often seen at the forefront of innovation include healthcare, large tech companies, and media giants. But when it comes to harnessing AI’s true analytical power, perhaps no sector has more to gain than life sciences.
From the earliest days of research, data have been at the heart of scientific discovery. The process hasn’t changed: collect, analyse, interpret, and repeat. Today’s life sciences labs aren’t so different from those of the past in that regard, but they are facing two unprecedented challenges: tight budgets and explosive data growth.
These challenges don’t exactly go hand in hand. Scientific progress thrives on data, and in today’s AI-enhanced environment, the more data available for analysis, the better.
But with limited budgets, life sciences researchers must now strike a delicate balance between access and affordability. That means making smarter choices about how data is stored, protected, and accessed.
Challenges the industry is facing: Data growth, cost efficiency, and ease of use
Perhaps the most pressing issue facing researchers today is the sheer volume of data being generated. Scientific equipment has become increasingly advanced, high-throughput, and automated, generating exponentially more data than was even a few years ago. What’s more, organisations have gotten better at reanalysing existing datasets, extracting new insights from old data – further reinforcing the need to retain and organise it all.
The life sciences analytics market is projected to grow from $11.97 billion in 2025 to $24.85 billion by 2034, with a compound annual growth rate of 8.47%. That’s not just growth—it’s an avalanche. And that data isn’t being created to sit idle. It needs to be accessible, analyzable, and stored in a way that supports both present and future use cases. However, storing all this data, especially in tiers of fast, high-performance storage, can quickly outstrip the financial resources of many research labs.
Life sciences organisations often operate on grants, donations, or public funding—all of which have become harder to come by in recent years—which makes capital expenditures hard to justify and even harder to scale. Even when organisations know exactly what kind of storage would best support their work, the gap between their needs and their budgets often forces them to compromise.
Then there’s the ease-of-use issue. Most life sciences teams don’t have access to large and capable IT departments. Storage systems need to be intuitive, quick to deploy, and flexible enough to grow with minimal manual intervention.
Hybrid infrastructure: Smart storage, smarter growth
The answer to these complex challenges lies in a hybrid data infrastructure—one that is scalable, cost-efficient, and built with lifecycle management in mind. The reality is that research organisations cannot afford to lose or dispose of any data. Every byte has potential value—not just today, but five or ten years from now. That’s why scalability is key.
The most successful storage solutions enable teams to start small and scale as needed, without incurring major upfront investments. Scale-out systems, leasing models, and pay-as-you-grow subscriptions have become essential tools in this landscape, enabling researchers to stretch limited budgets without compromising on functionality.
Object storage – particularly S3-compatible storage – has become the de facto standard for large, affordable repositories. It removes constraints related to block size or file systems, enabling automated lifecycle policies. These policies allow organisations to move older, less frequently accessed data to lower-cost storage tiers, whether on-premises, in a private cloud, or across multiple hyperscaler platforms.
The new goal isn’t to figure out how to get rid of data—it’s to keep it all and manage it smartly. By examining metadata such as the last access date, organisations can automatically migrate data that hasn’t been accessed in months or years. Some datasets, such as those related to ongoing research or compliance obligations, may need to remain available indefinitely. However, the rest can be safely archived in more cost-effective tiers without sacrificing accessibility.
This hybrid approach is also effective when building a private cloud infrastructure. Organisations can construct their own cloud to create a “cheaper” storage tier internally, or they can leverage public clouds for deep archive storage. Many choose a mix of both, maintaining their own systems for frequently used data while offloading the rest to a centralised system. Gone are the days when bigger drives meant lower costs. Storage is no longer decreasing in cost at the same rate as data is growing. A 60TB drive now costs significantly more than twice as much as a 30TB one. We’ve reached the tipping point: capacity is no longer the only metric that matters. Efficiency, agility, and long-term sustainability are now just as important.
Getting ahead of data loss
Of course, storing and scaling isn’t the whole picture. In life sciences, where data can drive breakthrough therapies and diagnostics, the cost of data loss is unthinkable. Whether it’s a cyberattack, natural disaster, or accidental deletion due to simple human error, losing critical research data can set projects back years – or even derail them completely. Unlike natural disasters or human error, cyberattacks present an added layer of complexity: it’s often unclear when the attack began, how deep it went, or which files were affected.
That’s why data protection strategies must go beyond traditional backup. Snapshot-based backups are a popular solution because they’re fast to execute and easy to restore from. But in an age of ransomware, snapshots must also be protected themselves. This is where cyber-resilient backup strategies come in—solutions that hide or air-gap snapshots, keeping them inaccessible to attackers while still being available for recovery.
Ultimately, every life sciences organisation needs a clear, actionable backup and restore strategy –one that aligns with their storage lifecycle and considers not just recovery time but also attack detection and containment.
A multi-tiered and long-term approach
As life sciences organisations continue to generate and rely on larger volumes of data, they must embrace a new mindset—one that sees storage not as a fixed asset, but as an evolving, multi-tiered ecosystem. From hot data to cold archives, the ability to manage information across different levels of accessibility and cost will be the deciding factor in whether research is accelerated or delayed.
That means thinking beyond the initial price per terabyte. Total cost of ownership includes migration fees, backup costs, hardware refreshes, and licensing models. The fine print matters, and in the life sciences, the stakes couldn’t be higher. Adding to the urgency, regulatory initiatives like the European Union’s NIS-2 Directive are raising the bar on accountability by holding individual board members personally liable for data loss resulting from cyberattacks. This shifts data protection from an operational challenge to an executive-level responsibility, forcing organisations to act decisively now – or risk serious consequences very soon.
Researchers are not just solving for storage – they’re solving for the future of medicine. They need infrastructure that’s as resilient and forward-looking as their science. By embracing hybrid systems, lifecycle policies, and cyber-resilient backups, life sciences organisations can ensure that their most valuable asset, data, is always available, protected, and working for them and the lives they’re trying to save.
