Higher Education Unstructured Data Management
Reduce storage costs amid funding declines. Improve visibility and access for high-value research data. Classify data for AI.
Why Komprise for Higher Education?
$1M
SAVED/YEAR
80%
Better AI Accuracy
ZERO
PII, IP Surprises
The Komprise Difference for Higher Education
Universities and research institutions generate massive volumes of unstructured data: student and faculty documents, images, video and instrument outputs spread across departmental storage, high-performance clusters and clouds. Data silos limit visibility to right-place data for departmental and compliance needs. Researchers struggle to identify which data is usable for AI initiatives. Komprise builds a Global Metadatabase across hybrid storage which cuts costs, delivers control for compliance and prepares research data for AI and analytics without disrupting workflows.
Visibility
Sensitive Data
Tiering Cost Savings
Tiering Policies
Cybersecurity
Research Metadata
Customizable Policies
Cost of Storage Refresh
Compliance Reporting
Research AI Workflows
AI Ingestion
Data Lake House Integration
Focus
Storage-Vendor Data Management
Siloed, storage specific
Limited to no support
Limited, only on storage
Limited, Cluster-Based
Expensive copies
None
Limited
Lock-in. Costly Rehydration when Switching Vendors
None
None
Manual
None
Storage
Komprise Intelligent Data Management
Global Analytics Across all Storage and Clouds
Built-in sensitive data detection and handling
Save 70% on storage, backup, DR costs
Flexible per Use Case or Region
Cut 80% of Ransomware and Cyber Security Costs
Extract Project, Demographic Metadata for Contextual Search from PDFs, Multimedia, SPSS, Stata, MATLAB, ESRI etc.
Flexible; Showback by team or department
No Rehydration Penalty or Data Lock-In
Built-in Chain-of-Custody Reports, Auditing
Automate workflows to LLMs, Cloud AI, Lakehouses
Intelligent Caching Keeps Data Secure in Place. Boosts AI ROI by +80%
Deliver the Right Data to Analytics Platforms
Data Management
Trusted by Universities & Academic Research
Duquesne University Finds & Tags Images 99% Faster with Komprise
Duquesne deployed Komprise Smart Data Workflow Manager with Amazon Rekognition to automate the process for two use cases, driven by the library archive team. The solution with Komprise and Rekognition reduced 14 days of manual labor to only 2 hours.
Getting Departments to Care About Storage Savings
How Showback and Simplicity Get the Archiving Buy-in You Need.
Analytics + Tiering
Understand data usage across hybrid storage to identify data ready for tiering and free Flash capacity.
- Analyze petabyte-scale data estates to findcompleted, stale or abandoned project data by owner and age.
- Identify cold files for automatic tiering by age, file type, or project with zero user disruption.
- See data growth and usage and model plans before a storage refresh.
Frictionless Governance
Meet compliance requirements (FERPA, HIPAA) across unstructured data with thorough visibility and scanning.
- Use built-in PII scanner to find and tag files containing student and employee personal and financial data.
- Scan file contents in place across NAS, object, and cloud storage to reduce risk.
- Mitigate with confine or move to secure storage without disrupting workflows and access.
- Run continuous Smart Data Workflows to ensure new sensitive data is detected and handled automatically.
AI & Lakehouse Data Preparation
Classify research data with tagging to improve search and AI curation.
- Automatically extract custom metadata to classify and curate the right data for AI and lakehouses.
- Filter redundant, orphaned and trivial (ROT) data that erodes AI data quality.
- Transparent File Tables export enriched metadata as Apache Iceberg tables for zero-move queries from lakehouses.
Dig Deeper
Blog
Transparent File Tables
Expose all your NAS and cloud data to Snowflake, Databricks and other lakehouses as Apache Iceberg tables without moving any data.
Overview
The Komprise Global Metadatabase
Rapidly extract rich structure for all your file and object unstructured data.
Solution Brief
Komprise for Higher Education
Komprise gives healthcare organizations control over 35–40% data growth across DICOM imaging, digital pathology, genomics, and EHR systems.
Frequently Asked Questions
Why is unstructured data management important for universities and research institutions?
Universities and research institutions generate massive volumes of unstructured data: student and faculty records, research datasets, imaging, video, and instrument outputs spread across departmental storage, high-performance clusters, and cloud. Data silos limit visibility into what needs to be retained for compliance, what can be archived, and what is ready for AI and analytics. The Komprise Global Metadatabase builds a unified view across all storage and clouds without sitting in the data path, giving IT and research computing teams a single view of file and object data across every department, lab, and campus.
How can universities reduce storage costs as budgets tighten and funding declines?
Most research and departmental storage holds a mix of active project data and cold data: completed studies, graduated students’ files, and abandoned datasets that are no longer accessed but still sit on expensive primary storage. Deep Analytics identifies this cold data by age, owner, project, or file type, and Transparent Move Technology tiers it to lower-cost storage in its native format, with no rehydration penalty and no vendor lock-in. Universities can use a Storage Refresh Assessment to model these savings before their next capacity purchase.
How does Komprise help universities meet FERPA and other compliance requirements without disrupting research access?
Student records, health data, and sensitive research data are often scattered across NAS, object, and cloud storage with no consistent way to find or govern them. The built-in sensitive data detection capability in Komprise scans file contents in place to find and tag files containing student, employee, and research subject data. Sensitive files can then be confined or moved to secure storage without disrupting how faculty and researchers access their data, and Smart Data Workflows run continuously so newly created sensitive data is detected and handled automatically.
How does Komprise prepare research data for AI initiatives?
Research data is rarely AI-ready as-is: it is spread across labs and departments, poorly tagged, and mixed with sensitive or regulated information. Smart Data Workflows scan and classify this data in place, while KAPPA data services extract project, demographic, and other contextual metadata from PDFs, multimedia, and formats like SPSS, Stata, MATLAB, and ESRI. The result is a governed, searchable dataset in the Global Metadatabase that research computing and AI teams can curate with confidence, filtering out redundant, obsolete, and trivial data that would otherwise erode AI accuracy.
How does Komprise connect university research data to platforms like Databricks, Snowflake, and Teradata?
Moving petabytes of research data into a data lakehouse is slow, costly, and disruptive to active projects, and most of that data is never actually queried at the file level. Transparent File Tables expose file and object metadata from the Global Metadatabase as Apache Iceberg tables, making research data queryable in Databricks, Snowflake, and Teradata without copying or moving the underlying files. This lets research computing and analytics teams accelerate AI and lakehouse initiatives while data stays exactly where faculty and labs expect it.
Ready to Bring Structure 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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