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Solutions Hub: Unstructured Data and Data Minimization

Know what your files, email and chat hold, keep what earns its place, and retire the rest

BigID classifies, minimizes, and disposes of unstructured data with a record of every step, from shared drives and mailboxes to the content AI assistants read.

Files, email and chat make up much of what an organization stores, and they are where the least is known about what each item contains. They are also the first content an AI assistant reads. BigID classifies unstructured data at scale, finds the duplicates, stale files and out-of-compliance data inside it, applies retention and minimization rules, and moves what is flagged through review to archive or purge, with legal hold checked first.

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What it reaches
Files, email and chat at scale
Hundreds of file formats and hundreds of connectors, agentless, with scan depth set per source
What it finds
Excess in every form
Exact and near-duplicate files, derivative copies, stale data, expired consent and regulated data, detected the same way across sources
What it delivers
302M files deleted at one airline
707TB reclaimed from Azure Data Lake Gen 2, with $27K in savings every month
Why minimization starts with unstructured data

Files, email and chat hold the exposure, the storage cost, and the content AI assistants read

Exposure risk, storage cost, retention obligations under GDPR and CCPA (see privacy and compliance), and AI assistants that read whatever they can reach all point at the same place: the unstructured data an organization has accumulated. Minimizing it starts with knowing what each file holds and finishes with disposal that has been reviewed and checked. BigID runs that path on one platform, from the first scan to the record of what was removed. Gartner projects that six in ten data governance teams will make unstructured data a priority by 2027, driven by GenAI use cases and the need for better decisions.

Source: Gartner, Magic Quadrant for Data and Analytics Governance Platforms, 6 January 2026.

For security teams

A smaller attack surface. Redundant and stale files holding PII, passwords or card numbers are found and removed, so there is less to breach.

For privacy and compliance

Retention applied as written. Regulation-based policies, expired consent and a documented review turn minimization from a principle into a process.

For AI and infrastructure teams

Current, classified content. Assistants work from files that are still needed, and storage shrinks as unused copies are cleared.

See it operate

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.

DemoWatch on YouTube ↗
Watch next

Prefer to click through it yourself?

Follow a retention policy from the first redundant-file finding through owner review to approved deletion.

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From file share to a smaller, cleaner estate

One workflow carries unstructured data from the first scan to disposal. The same detection logic runs on a file share, a mailbox and a data lake, nothing moves without a reviewer, and nothing under legal hold is touched.

Map and classify Find the excess Review Dispose safely File shares Email and chat Cloud storage and SaaS Code repositories Vector stores and AI data Hundreds of formats, agentless, depth set per source Exact duplicates Near-duplicates Derivative copies Stale by age or access Expired consent Regulated data Custom rules Detected the same way everywhere Delegated review nothing moves without a reviewer Retention rules Move to sandbox Tombstone left behind Reviewer attests Routed through ServiceNow or Jira Soft delete Archived to cold storage and restorable Hard delete Purged once legal hold and dependencies are clear Safeguards run first Every action is recorded Detection, review and disposal run in one workflow, so the evidence for each decision sits alongside the data it covered Retention policy Reviewer Legal hold check Dependency check Evidence record
Start here

Where teams start when they minimize unstructured data

How do we learn what is in our files and email without reading every one first? Metadata-only pre-scanning maps the estate before any content is read, smart sampling gives a first assessment of each source, and full scans go where the sampling says they are worth running. What the data holds → Does it handle the file formats our business actually uses? Hundreds of file formats, including specialized ones such as CAD, across file shares, Microsoft 365, Google Workspace, Box, Dropbox and object storage, with no agents to deploy. What the data holds → How do we find duplicates, near-duplicates and stale files? Duplicate detection across file systems and data lakes, cluster analysis for similar and derivative copies, and staleness by creation, modification or last access date, all calculated by BigID instead of taken from each platform's own flags. Redundant and obsolete data → Can it find data that should already be gone? Yes. BigID detects archived data, data with expired consent and data subject to specific regulations, and supports custom rules for your own definitions. Redundant and obsolete data → How do retention rules stay current as regulations change? Pre-built regulation policies ship in the product, and the filerskeepers integration keeps 300,000+ retention rules across 220+ countries up to date. A query builder lets business users define their own. Retention and minimization rules → Who reviews a file before it goes? Flagged data moves to a secure sandbox with a tombstone left behind, and a reviewer with approved permissions attests before anything is archived or purged. Cases route through ServiceNow or Jira. Review and disposal → What keeps a file under legal hold from being removed? Legal hold and referential integrity are checked before disposition runs. Where a purge cannot be proven safe, the data is archived to cold storage instead, and it can be restored if a hold clears or a reviewer reverses a decision. Review and disposal → How does minimization help our AI assistants? BigID classifies content inside RAG pipelines, vector stores and prompts, labels vectors by sensitivity, and clears out stale and duplicate files so assistants work from current content. Content for AI →
Capabilities

From the first scan to the evidence record, on one platform

BigID reads what the data holds, finds the excess, applies the rules that govern it, routes disposal through a reviewer with safeguards, and prepares what remains for AI. Every step works from the same inventory.

See what files, email and chat hold

Minimization starts with knowing what each file contains. BigID sets scan depth per source, so the whole estate can be mapped quickly and the detailed reading goes where it matters.

Explore data discovery and classification →
  • Metadata-only pre-scanning, so the estate is mapped before any content is read
  • Smart and customizable sampling for a fast assessment of what a source holds
  • High-speed full scans of structured and unstructured data, with no volume or frequency caps
  • Hundreds of file formats, including specialized CAD formats
  • 2,000+ pretrained and tuned categories of sensitive data, plus LLM-based and prompt-based classification
  • Just-in-time scanning of new and modified data, so one change does not trigger a full rescan
  • Cloud and SaaS optimization that works around platform throttling, including Microsoft 365

Find redundant, obsolete and trivial data

Excess does not sit in one file system, so detection cannot either. BigID finds it the same way everywhere, and catches near-duplicates as well as exact copies.

Explore data lifecycle management →
  • Duplicate detection for files and folders across diverse file systems and data lakes
  • Duplication calculated by BigID, independent of any source platform's own flags
  • Cluster analysis for similar and derivative data
  • Detection of stale data by creation date, last modified date, 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

Apply retention and minimization rules

A rule is only as good as the regulation behind it. BigID pairs a library of pre-built policies with a low-code way to build new ones, so keeping them current is not an engineering ticket.

Explore privacy and compliance →
  • A library of pre-built regulation policies, out of the box
  • Integration with filerskeepers for automated updates: 300,000+ retention rules across 220+ countries
  • Custom regulation definitions
  • A query builder designed for business users rather than engineers
  • Retention policies enforced against the data, not only written down

Review and dispose with evidence

Flagged data needs a reviewer before it needs a delete key. BigID routes it through a delegated review, runs the safeguards, then archives or purges, with a record of each step.

Explore data lifecycle management →
  • Integrated move to a secure sandbox
  • A tombstone marker left behind to tell users their data is under investigation
  • An RBAC-secured workflow, so only reviewers with approved permissions can attest
  • Native integration with ServiceNow and Jira
  • Soft delete (archive to cold storage) and hard delete (purge)
  • Safeguards for legal hold
  • Guardrails for referential integrity on structured data
  • Restore when a hold clears or a reviewer reverses a decision

Prepare the remaining content for AI

Assistants and agents read whatever their identity can reach. Removing the stale and duplicate content first, and classifying what is left, gives them current files and lets access controls follow sensitivity.

Explore AI assistant readiness →
  • Classify content inside RAG pipelines, vector stores and prompts against the same categories
  • Label vectors in vector stores by sensitivity or line of business, and apply access controls to RAG applications
  • Detect sensitive data in training sets
  • Clear stale and duplicate content so models work from current data
Coverage

Wherever unstructured data accumulates, the same detection and disposal

A duplicate on a file share and a duplicate in a data lake go through the same detection logic and the same workflow, so disposal does not depend on which team owns which platform.

File shares

SMB, NFS, CIFS and NetApp shares, including legacy shares brought back under review

Cloud file platforms

Microsoft 365, Google Workspace, Box and Dropbox, with optimization for platforms that throttle

Object storage and lakes

Amazon S3, Azure Blob, Google Cloud Storage and Azure Data Lake

Email and chat

Conversations and attachments, scanned for PII, passwords and card numbers

Code repositories

Credentials, configuration and IP in source, found and classified

Archives and cold storage

The destination for soft delete, with long-forgotten data brought back into view

Databases and warehouses

Structured data, where referential integrity is checked before a purge

AI data sources

Vector stores and RAG pipelines, classified with the same categories

Scan depth Metadata only Sampled Full scan
Rules and review filerskeepers ServiceNow Jira
Proof in practice

Hundreds of millions of files deleted, and the savings and risk reduction that followed

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
See more customer results →
FAQ

Answers on deletion, safeguards, and retention schedules

Do we have to delete anything to get started?

No, the first output is findings. BigID maps the estate, classifies what it finds and reports what is redundant, stale or out of compliance before anything moves. Disposition is a later step, and archive to cold storage is available wherever a purge has not been approved.

How do we know a file is safe to remove?

Review and safeguards run in the same workflow, before disposition. A reviewer with approved permissions attests, legal hold is checked, and referential integrity is checked for structured data. A tombstone marker tells users their data is under investigation, and every action leaves evidence behind.

How does this fit with the retention schedule our records team already owns?

BigID enforces it against the data instead of leaving it on paper. Custom regulation definitions and a query builder for business users let records and legal teams encode their own schedule alongside the pre-built regulation library.

What is data minimization, and how does it apply to files, email, and chat?

Data minimization means keeping only the data you need for a stated purpose and retiring the rest. For unstructured data, BigID finds what each file holds, flags redundant, obsolete, and trivial (ROT) content, data with expired consent, and data past its retention period, then moves it through review to archive or purge. That supports the data minimization and storage limitation obligations in GDPR and CCPA, alongside privacy and compliance automation and data lifecycle management.

Does it find dark data and ROT across our file shares and cloud storage?

Yes, the same way on every source. Redundant, obsolete, and trivial (ROT) data, often called dark data when nobody knows it exists, is found with duplicate detection across file systems and data lakes, cluster analysis for near-duplicates and derivative copies, and staleness by creation, modification, or last access date. What each file holds comes from data discovery and classification, so the excess is found with the sensitivity already attached.

Resources

Go deeper on data minimization and retention

Guide / start here
Data Minimization and Retention Policies Mapping Guide

A practical map from regulatory driver to retention rule to disposition action, for keeping only the data actually needed.

Get the guide →
Whitepaper Redefining Data Lifecycle Management for the Modern Enterprise Finding every kind of ROT, scan depth, retention schedules, investigation workflow, tombstoning, and the checks that run before deletion. Get the whitepaper → Solution brief BigID for Data Lifecycle Management Business outcomes, key capabilities, and the customer quotes behind the proof above. Read the brief → Related solution Data Lifecycle Management The detect-to-dispose workflow in full, including how each safeguard runs. Explore the solution →

Point us at the file share or mailbox you most want smaller

A scoped assessment on your own estate shows what the data holds, how much of it is redundant, stale or out of compliance, and what is safe to archive or purge first.

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