CURATION AND ORCHESTRATION

Curate the files that matter. Automate what happens next.

Curation selects the right files from the catalog with queries, filters, and tags. Orchestration acts on them: policies copy, move, rename, change permissions, or delete files on a schedule or after each scan, and destructive actions can wait for an approval tag.

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Data orchestration in Diskover: cleanup policies select files by catalog query, such as file type, age, project, or tag, then delete, move, or copy them, and a legal hold tag keeps files out of every query

BENEFITS

Why it matters.

Policies turn your decisions about files into work that runs on its own. You cut storage costs, keep governance rules applied after every scan, and give AI projects a clean, known set of files.

Less manual work.

Scheduled policies replace scripts and ticket-driven cleanup, so your team spends time on real projects.

Lower storage spend.

Cold, duplicate, and temporary data leave premium tiers on a schedule, freeing capacity you already own.

Traceable runs.

Every run is logged, with the files it processed, which supports audits, retention questions, and policy reviews.

Clean inputs for AI.

Curated files are tagged in the catalog, so you can always see which files went into each model or pipeline.

HOW IT WORKS

From scattered files to decisions that run themselves.

Every cleanup, archive, and dataset starts with the same question: which files? Diskover answers it from the catalog with a query, can record the answer as a tag, and lets a policy act on it. A decision made once keeps running, scan after scan.

Data orchestration workflow in Diskover: select files, tag them, approve, and act by policy

CURATION

Curation: choose the right files.

Query the catalog.

Combine any attributes, such as path, age, owner, project, or enriched metadata from plugins. Save the query as a Smart Search, so the whole team can reuse it.
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Tag the results.

People tag files or whole folders in the interface. AutoTag rules tag files after every scan by any metadata. The AI Connector recommends tags for a person to approve.
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Find what to leave out.

Duplicate Finder compares file hashes across systems, and Index Differential shows changes between scans, so duplicates and stale versions stay out of the set.
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AUTOMATION

Orchestration: let policies do the work.

Copy.

Copy files to another system on a schedule, to keep a dataset in sync or a backup copy.
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Move.

Move inactive files to lower-cost storage by policy, after each scan or on schedule.
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Delete.

Delete expired, duplicate, and temporary files by policy, with a dry run first.
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Rename and fix.

Rename files in bulk, and fix owners and permissions on files with open access.
Explore data integrity plugin

Feed AI and BI.

Deliver a curated file list and its metadata to your lakehouse, BI tool, or AI pipeline.
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Custom tasks.

Run your own command on each selected file, for any step your workflow needs.
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SAFEGUARDS

Built-in safeguards.

Approval tags.

Destructive actions can require an approval tag, such as approved_delete, so they run only on files marked as ready.

Dry run first.

A dry run lists every matching file and logs what would happen, without changing anything.

Edited files wait.

If a file changed since the last scan, the policy leaves it for the next run, to protect recent work from mistakes.

Every run logged.

Each run logs the files and sizes processed, with warnings and errors, and scheduled reports can email the results.

USE CASES

Curation and orchestration in action.

SEMICONDUCTOR

Nearly 2 PB of abandoned data, cleared.

A semiconductor company found nearly 2 PB of abandoned and temporary data, including debug and core dump output that workflows never cleared, then automated the cleanup.

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MEDIA AND ENTERTAINMENT

300 million files, retention on autopilot.

A global media company indexed more than 300 million files across 4 storage platforms, and AutoTag rules now drive the retention policies that keep its storage lean.

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LIFE SCIENCE

500 TB reclaimed from core storage.

A life science research organization indexed 5 PB, cleared temporary and test data out of core storage, reclaimed more than 500 TB, and avoided $250K in storage overspend.

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Putting it all to work.

A localization services company uses Diskover from first search to final cleanup. Coordinators find source files in one catalog, then send them to GLIM for review and Vantage for proxies in one click. After delivery, policies move source files to cloud preservation and delete the working copies after seven days, as the service agreement requires.

Workflow orchestration in Diskover for a localization services company: launch GLIM and Telestream Vantage as file actions, then move source files to preservation and delete them 7 days after delivery by policy

FAQ

Questions about curation and orchestration.

Data orchestration automates what happens to files after you decide about them: copy, move, archive, rename, or delete, on a schedule and by policy. Diskover runs it from the metadata catalog, so each action follows a catalog query, which can include tags.
Data curation selects and labels the files that belong in a set, such as a training set, an archive, or a cleanup list. In Diskover, curation uses catalog queries, filters, and tags.
Only if you set it up that way. A policy can require an approval tag, such as approved_delete, before it deletes anything, and a dry run shows the matching files first.
Yes. A policy can run after each scan of a location or on a schedule your team sets, and each run is logged.
The AI Connector finds files and recommends a tag in plain language. A person approves the tag, and the policies on this page act on it.

See it on your own data.

We’ll index a sample of your environment and show you
what’s recoverable against your current storage spend.

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