Glossary Term

Data Sprawl

What is data sprawl?

The uncontrolled spread of data across storage systems, clouds, and devices, creating visibility gaps, risks and hidden costs.

Data sprawl happens when an organization’s files multiply on one or across many storage locations- on-premises storage, cloud environments, edge devices, and SaaS applications – faster than teams can track or govern them. The result is fragmented data silos where no one has a clear picture of what exists, and whether any of it is at risk. Data sprawl is unintentional and slow to develop. It is often caused by departments pursuing different projects at different times with different funding and objectives. It can build for years with no visible consequences, until management starts to look for infrastructure savings, or stumbles across duplicate data, or worse, data that is not being fully protected.

The problem hits hardest with unstructured data: files, documents, images, and research outputs that make up the majority of enterprise data and lack the built-in organization of databases. Without centralized visibility, organizations pile up massive volumes of redundant, obsolete, and trivial (ROT) data that inflates storage costs year over year while delivering no business value.

In research and HPC environments, data sprawl reaches extreme scale as scientific workflows generate outputs across heterogeneous file systems. Fixing it requires a metadata-driven approach: cataloging and classifying data across vendors and platforms so organizations can see what they have, act on what matters, and delete what doesn’t.