Life sciences organisations stand to benefit the most from harnessing the analytical power of artificial intelligence (AI) in today’s data-driven world. Roland Rosenau, SE Director, EMEA at Quantum, delves into the evolution of data management within this sector.
When discussing the integration of artificial intelligence (AI) in various industries, the life sciences sector emerges as a key player poised for significant advancements. Data has always been at the core of scientific exploration, with the fundamental process of collecting, analyzing, interpreting, and iterating remaining unchanged. However, modern life sciences laboratories are now encountering unprecedented challenges in the form of constrained budgets and exponential data growth.
The primary challenge facing researchers today is the sheer volume of data being generated in life sciences. Advanced scientific equipment, characterized by high-throughput and automation, is producing vast amounts of data compared to just a few years ago. Furthermore, organizations are becoming more proficient at reanalyzing existing datasets, emphasizing the need to effectively retain and organize this expanding pool of information.
The life sciences analytics market is projected to experience substantial growth, transitioning from $11.97 billion in 2025 to $24.85 billion by 2034. This exponential growth necessitates accessible, analyzable, and efficiently stored data to support present and future use cases effectively. However, the financial constraints faced by research labs often hinder their ability to store data in fast, high-performance storage tiers.
Life sciences entities typically rely on grants, donations, or public funding for operations, funding sources that have become increasingly scarce in recent years. This scarcity makes it challenging to justify capital expenditures, leading organizations to compromise on storage solutions that best align with their research requirements.
In addition to the data growth and cost efficiency challenges, ease of use also poses a significant hurdle for most life sciences teams lacking access to extensive IT support. Storage systems must be user-friendly, quick to deploy, and flexible enough to accommodate growth with minimal manual intervention.
The solution to these complex challenges lies in adopting a hybrid data infrastructure that is scalable, cost-efficient, and designed with lifecycle management in mind. Research organizations cannot afford to discard any data, as every byte holds potential value not just in the present but also in the future. Scalability is paramount in this context.
Successful storage solutions allow teams to commence operations on a small scale and expand as needed without requiring substantial upfront investments. Scale-out systems, leasing models, and pay-as-you-grow subscriptions have become indispensable tools in this landscape, enabling researchers to optimize limited budgets without sacrificing functionality.
Object storage, particularly S3-compatible storage, has emerged as the standard for creating large, cost-effective data repositories. This storage type eliminates constraints associated with block size or file systems, facilitating the implementation of automated lifecycle policies. These policies enable organizations to transfer older, less frequently accessed data to lower-cost storage tiers, whether on-premises, in a private cloud, or across various hyperscaler platforms.
The focus has shifted from data elimination to intelligent data management. By leveraging metadata such as the last access date, organizations can automate the migration of data that has not been accessed for extended periods. While some datasets may necessitate indefinite availability due to ongoing research or compliance obligations, the remainder can be securely archived in more economical tiers without compromising accessibility.
This hybrid approach is equally effective in establishing a private cloud infrastructure. Organizations can construct internal clouds to establish a cost-effective storage tier or utilize public clouds for deep archive storage. Many opt for a combination of both approaches, maintaining proprietary systems for frequently accessed data while transferring the rest to a centralized system. The era of larger drives equating to lower costs is fading, with storage expenses failing to decrease at the same rate as data proliferation. The focus has shifted from capacity alone to encompass efficiency, agility, and long-term sustainability.
Efficient data storage and scaling are essential components, but they only represent part of the picture in the life sciences realm where data drives crucial therapeutic and diagnostic breakthroughs. The consequences of data loss, whether due to cyberattacks, natural disasters, or human error, are catastrophic. Implementing robust data protection strategies beyond traditional backups is imperative.
Snapshot-based backups are a popular choice due to their rapid execution and easy restoration capabilities. However, in the current ransomware landscape, safeguarding snapshots is equally crucial. Cyber-resilient backup strategies, which conceal or air-gap snapshots to prevent unauthorized access, ensure data availability for recovery while thwarting potential attackers.
Every life sciences organization must establish a clear, actionable backup and restoration strategy aligned with their storage lifecycle. This strategy should not only focus on recovery time but also encompass attack detection and containment measures to safeguard critical research data.
As life sciences entities continue to generate and rely on substantial data volumes, a shift towards a multi-tiered storage ecosystem is imperative. From active data to cold archives, managing information across different accessibility levels and costs will dictate whether research progresses swiftly or encounters delays.
Total cost of ownership in the life sciences sector extends beyond the initial terabyte price, encompassing migration expenses, backup costs, hardware updates, and licensing models. The minute details are critical, especially as regulatory initiatives like the European Union’s NIS-2 Directive heighten accountability for data loss from cyberattacks. Data protection has transitioned from an operational concern to an executive-level responsibility, prompting organizations to take decisive action now to avert potential repercussions.
Researchers in the life sciences arena are not solely tackling storage challenges but are also shaping the future of healthcare. The infrastructure supporting their endeavors must be resilient and forward-thinking, mirroring the innovation driving their scientific pursuits. By embracing hybrid systems, lifecycle policies, and cyber-resilient backups, life sciences organizations can ensure their most valuable asset – data – remains accessible, protected, and instrumental in their mission to save lives.
