Solution hub: data lifecycle management and responsible disposition
Automate and execute data lifecycle management across your entire data estate
Exposure risk, storage cost, CCPA, GDPR, or an AI model that needs fresh data instead of stale: whatever is driving the mandate, BigID gives every one of those programs the same missing piece. It evaluates ROT (redundant, obsolete, and trivial data) and out-of-compliance data the same way across structured and unstructured sources, reviews what it finds in a secure sandbox with a record left behind, and moves, restores, archives, or purges it only once legal hold and referential integrity are checked. One workflow, start to finish.
Redundant and obsolete data found, investigated in a sandbox with a tombstone left behind, then archived or purged, without stitching together separate tools
Regulatory reach
300,000+ retention rules
Across 220+ countries, live in the product through the filerskeepers integration, not a spreadsheet somebody has to maintain
Smaller attack surface
Automatically, not manually
Archived or purged on schedules and rules, so the footprint stays down without a recurring cleanup project
Where data lifecycle management stands now
The data that outlived its purpose is still exposed
Exposure risk pushes toward getting rid of what nobody needs, storage cost pushes toward cleanup, CCPA and GDPR push toward minimization, and AI pushes toward keeping only what is current. All of it points at the same pile of redundant, obsolete, trivial, and non-compliant data, and BigID closes it the same way every time: evaluated consistently, reviewed safely, and disposed of with no legal or referential surprise.
One evaluation, every source
ROT and out-of-compliance data detected the same way across structured and unstructured estates, instead of a spreadsheet exercise repeated per file system.
A workflow before the delete key
Flagged data moves to a secure sandbox and leaves a tombstone behind, so a reviewer with the right permissions attests before anything happens.
Guardrails on the way out
Legal hold and referential integrity checked first, so a soft archive or a hard purge cannot fire against data that is frozen or still depended on.
See what BigID can do
Watch it work
BigID Data Retention for Google Drive: Find ROT, Enforce Policies, and Take Action
What it looks like to find ROT inside Google Drive, enforce a retention policy against it, and take action on what turns up, without leaving the platform.
Demo
In this hub
Start here
The data lifecycle management questions BigID answers first
One workflow, with a gate before anything is deleted
The ROT usually gets found correctly. What fails is the workflow around it, where a script or a spreadsheet skips the review, the legal hold check, or the dependency check, because there is nowhere for those checks to live. BigID puts all three in the same workflow as the detection itself.
Nothing moves to disposition without a reviewer, and nothing under legal hold or structural dependency gets deleted at all.Capabilities
Detection, regulation, investigation, and disposition: one data lifecycle management workflow
In the order the work runs: detect what qualifies for disposition, keep the regulation behind that detection current, route it through a delegated review with a record left behind, then execute the disposition itself with guardrails on.
Advanced ROT and out-of-compliance data detection
Redundant, obsolete, trivial, and non-compliant data does not sit in one file system, so detection cannot either. This group finds it the same way everywhere, across structured and unstructured sources, calculating duplication independently rather than relying on a source platform's own flags, and catching near-duplicates too, not just exact copies.
Duplicate detection for files and folders across diverse file systems and data lakes
Advanced cluster analysis for detection of similar and derivative data
Detection of stale data based on creation date, last modified, or last access and activity
Detection of archived data
Detection of data with expired consent
Detection of data subject to specific regulations
Support for custom rules
Regulation monitoring
A detection rule is only as good as the regulation it is built on, and regulations do not hold still. This group pairs pre-built regulation policies with a low-code way to build new ones, so keeping it current is not an engineering ticket.
Outcome: audit-ready compliance that keeps up with the law
A library of pre-built regulation policies, out of the box
Integration with filerskeepers for automated regulation updates, 300,000+ retention rules across 220+ countries
Custom regulation definitions
A simple query builder, designed for business users rather than engineers
Delegated investigation workflow with tombstoning
Flagged data needs a reviewer before it needs a delete key. This group moves it to a secure sandbox, leaves a tombstone marker behind, and routes it through native Jira and ServiceNow hooks, so review does not mean tracking it in a spreadsheet on the side.
Outcome: no wrongful deletions, and proof of why
Integrated move-to-sandbox functionality
A tombstone marker left behind to notify users their data is under investigation
RBAC-secured workflow, so only a reviewer with approved permissions can review and attest
Integration with ServiceNow and Jira
Responsible disposition and deletion
This is where the workflow actually acts on the data: moving, restoring, archiving, or purging it, with legal hold and referential integrity checked first. Restore in particular is the part most vendors leave out: delete or archive is common, putting data back the way it was once a hold clears is not.
Outcome: defensible deletion, with evidence for every action
Support for soft delete (archive to cold storage) and hard delete (purge)
Guardrails for referential integrity impact on structured data
Safeguards for legal hold
Coverage across files, tables, and the other structured and unstructured objects an estate holds
Where it runs
The same detection and disposition workflow, wherever the data sits
A duplicate found on a file share and a duplicate found in a warehouse table go through the same detection logic and the same workflow, so disposition does not depend on which team owns which platform.
Files and file shares
Network shares and unstructured content across the data center and cloud, the largest source of duplicate and stale ROT.
Databases and tables
Relational and NoSQL, where referential integrity gets checked before a hard delete is ever allowed to run.
Data lakes and cloud storage
Object storage and the warehouses feeding analytics and AI, where stale and duplicate data does the most damage to model quality.
Archives and cold storage
The destination for soft delete, and the coverage that closes the loop on data nobody remembers moving there in the first place.
Proof in practice
Files deleted, storage reclaimed, and risk eliminated, at scale
Global airline
302M files deleted
707TB reclaimed from Azure Data Lake Gen 2, with $27K in savings every month
International insurance organization
100M+ files deleted
Redundant, stale, and irrelevant data cleared from NAS, OneDrive, and SharePoint
Global insurance provider
2.3M+ files deleted
Including 571,000+ high-risk emails holding PII, passwords, and credit card numbers
University of Maryland
$5M+ in risk eliminated
By removing 27,000+ exposed PII records from cloud storage
What stops a hard delete from breaking something downstream?
The referential integrity check runs inside the disposition workflow itself, not as a separate audit afterward. It checks structured data before a hard purge fires, so a row another table still depends on does not disappear underneath it. Where that cannot be proven safe, disposition defaults to soft delete: archive to cold storage instead of an outright purge.
What happens to data that is under legal hold?
Legal hold is checked in the same pass, before disposition ever touches the data. It runs as part of the same guardrail step as referential integrity, so a hold on one custodian does not mean pausing disposition for everyone else in the batch.
Do the people whose data gets investigated find out?
Yes, the moment it moves. A tombstone marker is left behind as soon as flagged data is moved to the sandbox, and a reviewer with RBAC-approved permissions has to attest before anything is disposed of, with the case routable through ServiceNow or Jira like any other ticket.
Take it further
What to read when disposition is the thing holding everything else up
Point us at the data you have been afraid to delete.
A scoped assessment on your own estate shows exactly what qualifies for disposition, what regulation or hold applies to it, and what is safe to archive or purge first.
