Why Universities Need to Balance Data Storage with Data Value
Universities are among the leading generators of data today. A prominent institution with around 40,000 students can produce over 15 TB of data daily, primarily from research activities. This volume necessitates storage in the petabyte range, akin to large enterprises. The demand for robust infrastructure is only expected to grow with the increased use of data-intensive AI tools.
In many academic settings, the rapid growth of data is outpacing IT teams’ ability to manage it efficiently. This imbalance can severely affect technology performance, research timeliness, and budget allocations, which are often under tight constraints.
The Challenge of Uncontrolled Data Growth
Typically, universities address data growth by simply adding more storage when existing capacities are maxed out. Compounding the issue, a significant portion of university data consists of inactive or inaccessible information. This data remains in primary storage unclassified, as institutions are often hesitant to archive or delete it due to risk aversion.
While such an approach offers a layer of security, it also leads to treating high and low-value data equally, inflating costs and diminishing the long-term effectiveness of technological investments.
When data growth is viewed solely through the lens of storage capacity, it overlooks a critical factor: the lack of visibility into data existence, location, and usage creates a disconnect between expenditure and the actual value derived from data.
A Change in Approach
To address these challenges, universities must regain control over their data, managing and budgeting according to its value. This requires shifting from a reactive storage expansion mindset to a proactive data management model focused on understanding and control.
Visibility is the starting point. Without a comprehensive view of the data set, distinguishing between data supporting active research and data that merely occupies costly resources becomes nearly impossible.
This strategy necessitates the capability to analyze vast amounts of unstructured data at a university scale, which usually involves billions of files across various systems and locations. Modern data management software must analyze these files to provide the transparency essential for informed decision-making.
At this scale, relying on manual processes is unfeasible. Automated intelligence is crucial to bridging the gap between requirements and resources, forming the basis for data-driven decisions about handling different data sets. This ensures the storage infrastructure aligns with the data’s actual value and access needs, complying with associated processes.
Universities must also consistently define and manage access permissions across environments. Without this control, sensitive data can remain exposed even after being moved to a more suitable storage tier, potentially compromising governance and compliance.
Equipped with definitive insights, institutions can then decide which records should remain on high-performance infrastructure and which can be archived or deleted. This lays the groundwork for policy-driven lifecycle management, actively managing data throughout its lifespan, and shifting it to the appropriate environment or deleting it permanently as needed.
The short-term benefits include reducing pressure on primary storage systems and adopting a more controlled approach to capacity planning. More importantly, it aligns budgets with actual data needs, directing investments to support key institutional priorities rather than merely absorbing funds.
Ultimately, this isn’t just about reducing storage costs. It’s about enhancing institutional operations at scale and preparing for a future of ever-growing data volumes. Sustainable IT investments require breaking the cycle of periodic storage expansion and adopting a more predictable, sustainable model. Institutions that achieve this balance will enjoy improved cost controls and enhanced support for research and innovation.
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About the Author
Steve Leeper is Vice President of Product Marketing at Datadobi.
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