Data tagging

Data tagging attaches labels to files so they can be grouped and acted on. Tags typed by people stop being accurate the moment the estate grows past what a person can review, usually at a few million files. Rules applied at index time keep working, because they are derived rather than entered.

Key takeaways

Tags group files so they can be found and acted on together.
Rules apply tags consistently at index time, which scales where manual tagging stalls.
A tag records a decision, and a metadata field records a fact.

Why tag files?

Because file names and folders rarely say what a file is for. A tag adds the missing context, such as the project, the client, the retention class, or whether the file can be deleted. Once files carry tags, teams can search, report, and set policies by meaning instead of by location.

What is an example of a useful tag?

A tag that answers a decision. For example, a studio tags every file in a show’s project folder with the project name and a status of delivered. Finance can then report storage cost by project, and the storage team can move everything marked delivered to lower-cost storage, without opening a single folder. Tags that answer no question, such as free-text notes, tend to go stale and are rarely used.

What is the difference between a tag and a metadata field?

A metadata field holds one value from a defined list, such as a retention class, and every file has a place for it. A tag is a label that a file either carries or does not, such as approved or archive candidate, and one file can carry several. Fields suit attributes every file has, and tags suit decisions made about some files. Both can be applied by rule at index time, so neither depends on manual entry.

How does Diskover help?

Diskover applies tags by rule as it indexes, and teams can add their own tags in the interface, so groups of files can be searched, reported on, and acted on.

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