ENRICHED METADATA CATALOG

Metadata is the heartbeat of intelligent data management

Metadata is the “who, what, where, when, and why” behind every file. It’s the connective tissue transforming raw, unstructured data into context-rich assets. By centralizing and enriching metadata, organizations gain the clarity to discover insights faster, automate workflows, and power AI-driven decisions with confidence.

CORE
METADATA

The core file and system attributes are the foundational building blocks of your catalog.

Enrich your metadata catalog with business context attributes, enabling highly accurate datasets.

SUPERCHARGED
METADATA CATALOG

Blend core and contextual attributes to build curated datasets and unlock true data intelligence and automation.

Global Visibility

Search your entire data estate using any metadata attribute—so teams find the right data instantly, wherever it lives.

Data Intelligence

Correlate metadata to uncover usage and growth patterns—so you can confidently plan retention, cleanup, tiering, and migration.

Data Mobility

Use metadata attributes to tier, move, and archive your data based on activity, cost, policy, or business value.

Automation

Use metadata-driven workflows to curate and organize datasets—then orchestrate workflows across your data lifecycle.

AI/BI Readiness

Combine core and contextual metadata to assemble high-value datasets—ready for analytics, reporting, and model training.

Data lineage—your data’s story matters more than its zip code.

Diskover uses metadata to map relationships across your data estate—so you can trace where data came from, how it changed, where it lives now, and what depends on it.

Trace origin and movement across NAS, object, cloud, and archive (moves, copies, tiering).
Understand relationships between projects, teams, systems, and downstream consumers.
Reduce risk by validating what depends on a dataset before you clean up, migrate, or tier it.
Support governance efforts with traceable lineage that helps teams answer audits, retention questions, and policy reviews faster.
Strengthen AI/BI pipelines with trusted, traceable datasets by validating provenance, changes, and dependencies.
Get instant impact analysis with the Diskover AI Data Assistant by asking a question and instantly seeing where data came from, where it moved, who owns it, and what depends on it.
Manual tagging.
Authorized users can manually tag files to classify and organize data, flag workflow stages, or identify items for review and governance.
Automated tagging.
Automatically apply tags based on rules and metadata attributes to maintain structured, searchable datasets ready for automation and AI workflows.
Diskover's manual and automated tagging overview
Inefficient data navigation and storage utilization.
Limited visibility into aging and temporary/transient data.
Delays, wasted time, and inconsistencies due to manual coordination.
Ability to keep in check 300+ million high-value files.
Indexing Pixit, Facilis, Windows SMB Share, GB Lab accelerated NAS, and SpectraLogic.
Analysis, organization, and automation using AutoTags.
Replaced the need for manual commands and system property lookups with instant tag-based searches.
Enabled automated file deletion using tags and retention policies.
Streamlined ongoing data hygiene with structured, tag-driven workflows.
Significant time and cost savings.
Smart data hygiene and storage optimization at scale.
Faster data access, reduced manual tasks.
Optimized human efficiency and collaboration.
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