The Hidden Cost of Unstructured Data
On the face of it, data storage has plenty in common with business real estate. In each case, organizations pay for capacity and then decide what assets go where. The aim is to make the best use of the available capacity, and both need active management as requirements change.
But this is where the respective strategies often go in radically different directions. Commercial property is typically evaluated based on its use, and the most expensive locations are reserved for activities that genuinely require them.
The same can’t always be said for data storage, however, where files can remain on the same costly infrastructure long after their value or access requirements have changed.
To an extent, this is understandable. Imagine a company whose main office is approaching capacity. The management team can walk through the building and see how its space is being used. Empty desks and poorly used areas are easy to identify. The physical nature of the problem makes a review feel obvious before the company commits to investing in more space.
An IT team facing the equivalent storage issue may see that capacity is running low, yet have no useful view of what is filling it. The dashboard shows that more storage is required, but it may not reveal which files are actively used or which have remained untouched for years and could be deleted or archived.
Aligning storage with data value
Spending more money on storage can easily look like a straightforward capacity issue, even though it is often caused by a lack of insight into the data already held. Unstructured data estates are a particular problem because they typically contain actively used information alongside files that must be retained for specific purposes.
For example, some files may have no clear owner, or have remained untouched for a long period without being assessed. The resulting capacity spend reflects the accumulation of those earlier placement decisions.
Instead, organizations need visibility into how their data is being used before they can decide whether it still warrants high-performance storage. Storage decisions should also reflect the current value of data rather than where a file first landed.
There are various scenarios that may apply: for example, frequently accessed information may require a higher-cost platform to ensure performance and availability. Next, files retained for compliance or internal reasons may still be important but have far less need for immediate access.
Then there are files that have not been accessed or modified for several years and may also be candidates for an archive platform, depending on why they must be retained. Orphaned files associated with inactive users require review because their ownership and ongoing purpose can be understood.
The point is, without this level of insight, storage spending will always remain a cost wildcard, determined by files that have accumulated without a clear view of their current value.
From insight to action
Effective data management turns this insight into action. It allows organizations to apply consistent policies across the unstructured data estate, with policy-driven data mobility moving files to a suitable archive environment when their access requirements change. This process needs to work across hybrid and multivendor environments, where a fragmented approach can leave IT teams unable to make decisions from a complete picture.
High-performance capacity can then be reserved for data that genuinely needs it. Future upgrades can be planned against a clearer understanding of actual demand, turning storage cost from a wildcard into a fully managed outcome.