For research institutes, data is everything. It fuels discovery, underpins analysis, and validates results. But while data drives progress, it also drives cost, often quietly. As volumes grow year after year, so do the hidden expenses of storing, processing, and protecting it.
What begins as a sign of success, expanding projects, richer datasets, and more complex simulations, can quickly turn into a drain on infrastructure and budgets.
The Hidden Burden of Data Growth
Data growth may seem like a positive sign of a thriving business, but it quietly drives up operational costs. As datasets expand, the infrastructure required to store, process, and protect them becomes more complex. Research and media companies, in particular, face unique challenges:
Storage Strain: The global data storage market is projected to grow from £200 billion in 2025 to over £386 billion by 2030 (Mordor Intelligence). For research institutes, that growth is reflected in ballooning costs for high-performance storage, especially where large-scale data collection, imaging, or genomic sequencing is involved.
Processing Costs: McKinsey & Company estimates data centres will require £5.3 trillion worldwide by 2030 to meet compute demand. For research bodies, that translates into greater investment in backup systems and long-term archiving — especially where data retention is required for regulatory or scientific integrity.
Examples of Unnecessary Costs
Data Classification: Identify what needs to be kept, what can be archived, and what can be safely deleted.
Underutilised Legacy Systems: Research organisations may continue to operate older storage or computing systems because migrating data seems too complex. These systems are often less energy-efficient and more expensive to maintain.
Tiered Storage & Governance: Apply policies that balance accessibility with cost — for example, moving older or infrequently used data to lower-cost storage tiers.
Why Proactive Data Management Matters
Left unchecked, data growth silently drains budgets and reduces operational efficiency. Companies that implement structured data management strategies can mitigate these hidden costs. Key practices include:
Data Lifecycle Management: Identifying which data is mission-critical, which can be archived, and which can be deleted.
Deduplication & Compression: Reducing unnecessary copies and optimising storage footprint.
Cloud Governance: Applying policies for data retention, access, and tiered storage to control costs while maintaining accessibility.
The Bottom Line
Data growth is inevitable, but its associated costs don’t have to be. In fact, businesses may lose up to 20% of revenue due to inefficient data management practices. Recognising the silent cost of uncontrolled data expansion is the first step toward smarter infrastructure planning, reduced waste, and more predictable budgets. Investing in proactive data management is no longer optional, it’s a strategic necessity.