Data silos

A data silo is a store of data that only one team, application, or storage system can see. For files, silos form when each department buys its own storage and each vendor reports only on its own platform. The data is still there, but no one can answer a question across the whole data estate.

Key takeaways

Silos form when each team, system, or vendor keeps its own view of the data.
Silos cause duplicate copies, wasted storage, and data that AI and compliance teams cannot find.
One index of file metadata across every system removes the silo for search and reporting, while the files stay where they are.

Why are data silos a problem?

Because the data in them costs money and carries risk, but no one can see all of it:

Duplicate copies: teams copy the same files into their own storage, and each copy is paid for.
Slow answers: a question about the whole estate, such as how much data there is or who owns it, needs a report from every system and a spreadsheet to combine them.
Compliance risk: personal data in a silo that no one watches is still covered by privacy laws such as GDPR.
Missed value for AI: data that cannot be found cannot be used to train or ground AI models. See AI-ready data.
Wrong purchases: new storage is bought for one silo while another has free space.

What is an example of a data silo?

An engineering team keeps simulation results on its own parallel file system, the media team keeps video on its own NAS, and finished projects sit in a cloud bucket that only IT can see. Each system has its own console and its own reports. When the legal team asks where every file for a past project is, three teams each search their own system, and no one knows if the answer is complete.

What causes data silos?

Usually growth, not a decision. Each department buys storage for its own projects, mergers bring in new systems, teams open their own cloud accounts, and each vendor’s tools report only on that vendor’s platform. Over the years, the organization ends up with many stores of data and no single view of them. This is how storage sprawl starts.

Are data silos always bad?

Not always. Keeping data separate can be the right choice for security, for performance, or for rules that require data to stay in a specific country, such as data sovereignty laws. The problem is not where the data sits, but that no one can see it. Data can stay in separate systems, as long as one catalog describes all of it.

How do you break down data silos?

You do not have to move the data. Index the file metadata of every storage system, on premises and in the cloud, into one metadata catalog. Add owners and business context so each file is described the same way everywhere. Then search, report, and set policies across the whole estate from that one place. Moving or combining data can come later, and only where it makes sense.

How does Diskover help?

Diskover indexes the metadata of every file across your storage systems, from every vendor, on premises and in the cloud, into one catalog, so your teams can search and report across the whole data estate without moving the files.

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