Unstructured Data Classification
Automatically Discover and Classify Data
- Automatically extract file system metadata into a Global Metadatabase Service.
- Built-in sensitive data detection ensures AI data security and compliance.
- Rapidly extract and customize metadata extraction and enrichment with KAPPA serverless architecture.
- Extract metadata such as header metadata for BAM, DICOM, ELN, PDFs, MAMs, and more. Enrich metadata with contextual tags to get data ready for AI.
Protect and Manage Sensitive Data or Regulated Content
Identify, tag, and control sensitive data to reduce risk, support compliance, and strengthen security posture.
- Discover PII, PHI, and regulated data without moving or copying files.
- Apply metadata tags to drive governance, retention, and access policies.
- Reduce exposure by locating dark, stale, or over-permissive data sets.
Search Across All Your Unstructured Data with a Managed Global Metadatabase Service
Schedule a demonstration with the Komprise experts to get started today.
Curate and Move to Optimize Storage Costs and Deliver AI-Ready Data
Turn classified insights into action – cut storage costs while preparing clean, relevant data for AI and analytics.
- Feed analytics and AI pipelines with curated, well-classified datasets.
- Cut AI costs by proactively identifying and tiering cold data from expensive AI resources.
- Automate ongoing AI workflows with schedules so as new data arrives, it gets processed.
- Maintain AI data security and compliance with built-in reports and controls.
Dig Deeper
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Frequently Asked Questions
What is Unstructured Data Classification?
Unstructured data classification is the process of automatically identifying, categorizing, and tagging files such as documents, images, videos, and logs based on metadata, content, activity, and business context. It helps organizations understand what data they have, where it resides, and how it should be governed, optimized, or used for analytics and AI, without moving or copying the data.
Classifying and tagging unstructured data is the top challenge in prepping unstructured data for AI.
Read the 2026 State of Unstructured Data Management Report
Why is Unstructured Data Classification Important for Enterprises?
Most enterprise data is unstructured and spread across multiple storage systems, making it difficult to manage, secure, or extract value. Unstructured data classification provides visibility and context so organizations can reduce risk, control data storage costs, meet compliance requirements, and make data usable for AI and analytics initiatives.
How Does Unstructured Data Classification Improve Security and Compliance?
By classifying files based on sensitivity, age, ownership, and access patterns, organizations can quickly locate PII, PHI, and regulated data. This enables smarter governance policies, reduces exposure from stale or over-permissioned data, and supports compliance with regulations such as GDPR, HIPAA, and PCI, without disrupting users.
How Does Unstructured Data Classification Support AI and Analytics?
AI and analytics require clean, relevant, and well-understood data. Unstructured data classification identifies high-value datasets, filters out inactive or low-quality data, and enriches files with metadata. This creates AI-ready data pipelines and ensures models are trained on the right data while minimizing cost and complexity.
What is a Global Metadatbase Service, and Why Does it Matter?
Unstructured data is messy, scattered everywhere and yet is the bulk of an enterprise’s estate. Bringing structure to unstructured data with a Global Metadatabase ensures that no matter where your data lives, you can search and find the right data at the right time both for AI and for cost optimization. Managing a metadatabase across petabytes of data and billions of files can be costly and time-consuming, which is why Komprise eliminates these headaches with a fully managed Global Metadatabase Service that scales with your needs.
What is Metadata Extraction and Why is it Needed for Unstructured Data?
Unstructured data lacks a unifying structure, making it very hard to organize and curate. Poor unstructured data quality erodes AI ROI, and it causes security and governance risks. Metadata extraction is a technique to impute structure for unstructured data through gleaning metadata both from existing metadata and from file contents, from using AI, from using contextual clues and other means.
Ready to Bring Structured to Your Unstructured Data?
Schedule a call with our unstructured data management experts and see your file and object data in a whole new way.
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