Komprise for Higher Education

Intelligent Data Management for Higher Education

Komprise is the metadata and orchestration layer for enterprise unstructured AI data in higher education, trusted by leading public research universities and Ivy League institutions to cut storage and backup costs by 70%, govern research data at petabyte scale, and deliver precisely curated datasets to AI inferencing pipelines and analytics workflows. As this data continues to grow, often without effective archiving or deletion, it drives up storage and backup costs while remaining difficult to access and use. At the same time, universities face increasing economic pressure to better leverage student, academic, financial, and research data to understand shifting demand, engage stakeholders, and develop new programs. Cost-effectively transforming unstructured data into an AI-ready asset has become a strategic imperative.

This solution brief provides an overview of Komprise for Higher Education.

Cut storage and backup costs by 70% with intelligent tiering
Model savings across your full storage estate before committing
Showback reporting that makes departments accountable for data costs
Transparent tiering to any cloud with no vendor lock-in
Governed data classification and tagging for research compliance
Curate and deliver research data to AI inferencing pipelines automatically

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Learn more about Komprise for Higher Education.

FAQs

Why is unstructured data management a strategic imperative for higher education IT — and why has the pressure to act intensified?

The pressure on higher education IT to manage unstructured data more effectively has never been more acute — and the consequences of inaction are now measurable in both budget and AI competitiveness terms. The structural pressures that make this urgent today:

Storage consumes a disproportionate share of an already constrained IT budget — less than 3% of a university’s total budget is typically allocated to technology investment and operations; it’s not a stretch to assume that nearly half of a university’s IT budget goes toward data storage and backups; at a moment when federal research funding is under pressure and enrollment demographics are shifting, paying 40 to 50% of an already small IT budget on storage that is 60 to 80% cold is a structural inefficiency that directly constrains every other technology initiative
The no-delete culture has compounded for decades — institutions have a long history of keeping all unstructured data — student records, research files, lecture recordings, surveillance footage, HR data — without effective archiving or deletion policies; every year of deferred data management adds to a backlog that becomes proportionally harder and more expensive to address
Flash price increases compound the cost of inaction — Gartner estimates DRAM and NAND flash annual prices will increase by 125% and 234% respectively in 2026, with any meaningful pricing relief not expected until late 2027; universities that have been absorbing data growth by expanding NAS capacity are now paying for that growth at significantly elevated hardware prices; the cold data sitting on all-flash research NAS is paying performance-tier prices for data that should be on object storage
AI is creating a new competitive dimension for institutions — universities that can leverage decades of research data, student outcomes data, and institutional knowledge for AI inferencing and analytics will have a structural advantage in research competitiveness, student success programs, and operational efficiency over those that cannot; the data estate that costs too much today is simultaneously the AI asset that defines competitive position tomorrow
The Flash Stretch Assessment quantifies the opportunity for qualified institutions — for universities managing 500TB or more of unstructured data, the Komprise Flash Stretch Assessment identifies exactly how much cold data is consuming expensive primary storage, models what transparent intelligent tiering to lower-cost destinations would save annually, and projects how much hardware refresh cost avoidance is achievable; this is the financial analysis that turns an IT optimization project into a CFO-level conversation

What types of unstructured data does a university manage and why does treating it all the same cost so much?

Universities and colleges generate, share, and store data at incredible rates across an extraordinary diversity of file types and departmental use cases. Universities and colleges store a broad collection of different unstructured data types including student records, health and financial data, HR data, research data, online lecture content in video and audio, and surveillance footage; institutions have a long history of keeping all this unstructured data; yet today there’s a dire need to better manage data for cost efficiencies and to reuse it for analytics, research, and marketing. The cost of treating all of these data types identically:

Research data accumulates fastest and ages fastest — genomics sequences, simulation outputs, imaging studies, and experimental datasets from completed projects are typically accessed intensively during active research and then rarely or never accessed after publication; keeping multi-petabyte research datasets on all-flash NAS at performance-tier prices for completed projects is one of the most common and most expensive higher education storage inefficiencies Komprise encounters; a major West Coast research university found that over 60% of its NAS data had not been accessed in over a year
Lecture recordings and media content are large and permanently cold — video and audio recordings of lectures, events, and campus activities accumulate continuously, are rarely accessed after initial viewing, and consume disproportionate primary storage capacity relative to their ongoing value; these files are ideal candidates for transparent tiering to cloud object storage where they remain accessible for compliance, accreditation, and occasional reference without paying performance storage prices
Student and administrative records require long-term retention but not performance storage — HR files, financial records, student transcripts, and administrative documents have retention requirements measured in decades; most are never accessed after the relevant process completes; keeping them on primary NAS is paying a performance premium for a compliance archive
Departmental data has no unified governance — each department, research lab, and administrative unit generates data under different policies, different ownership models, and different retention expectations; without a unified metadata layer spanning all silos, IT cannot identify what each department owns, what it costs, or which data is cold enough to tier; an IT manager at US university in the North East said: “We didn’t know what the data was, who owned it, or when it was last used — now we set policies to move data off to cheaper media; we save approximately $330,000 a year”
Komprise showback reporting changes departmental behavior — Komprise pre-built showback reports give each department a clear view of their storage footprint, cold data percentage, and tiering savings opportunity in terms they understand; when a research department can see that their completed project data costs $85,000 per year on primary NAS and could be transparently tiered to $8,000 per year equivalent on object storage, they approve tiering policies voluntarily rather than resisting IT mandates

How has Duquesne University used Komprise Smart Data Workflows and Amazon Rekognition to transform a 333-hour manual task into a 2-hour automated AI workflow?

The Duquesne University case study is one of the most concrete illustrations of how higher education institutions can move from treating unstructured data as a cost burden to treating it as an AI-accessible institutional asset. The library archive team wanted to search for and find specific images from the millions of files in their digital archives; assuming each file would require at least two minutes to manually inspect, they estimated it would take at least 20,000 minutes or 333 hours to fully review and record the results; the solution with Komprise and Rekognition reduced 14 days of manual labor to only 2 hours. How the workflow operates:

Deep Analytics identifies the right dataset before any AI compute is spent — Komprise Deep Analytics searches the Global Metadatabase to find the precise subset of image files relevant to a given search across the full Duquesne digital archive; since AI services are compute-intensive, time-consuming, and expensive, identifying exactly the right dataset before sending it to Amazon Rekognition is what makes the workflow economically viable; feeding everything to Rekognition would generate unnecessary cost and degrade accuracy
Smart Data Workflows automate the full pipeline end to end — Komprise Smart Data Workflows gives Duquesne University the ability to automate the use of cloud AI services like Amazon Rekognition for search and tagging images across their entire campus, saving months of recurring manual effort; the workflow runs continuously as new images arrive, automatically sending new content to Rekognition and writing enriched tags back to the Global Metadatabase without any manual curation on each cycle
Tags persist so the AI work is never repeated — results from Amazon Rekognition are written back as searchable tags in the Komprise Global Metadatabase; the next time a librarian or researcher searches for the same image criteria, the search runs against the existing tags rather than re-running the full Rekognition workflow; this tag persistence is what prevents the pay-per-use AI cost from compounding on repeat queries across the same dataset
The outcome extends far beyond the initial use case — “Our digital collections are growing in leaps and bounds but budgets stay flat; AI is still new but has tremendous potential; with Komprise we’re able to improve efficiency with a systematic workflow to index data, run AI and tag data”, said Rob Behary, Head of Systems and Scholarly Communications, Gumberg Library at Duquesne University; the same workflow architecture applies to any image, document, or media search use case across campus — student records, research archives, marketing assets, and surveillance footage all benefit from the same Smart Data Workflow pattern
This is what AI inferencing from institutional data looks like in practice — the Duquesne outcome illustrates the broader principle: most university unstructured data is inaccessible to AI inferencing workflows because it lacks classification, metadata enrichment, and governed delivery pipelines; Komprise is the metadata and orchestration layer for enterprise unstructured AI data, and Smart Data Workflows are the mechanism that unlocks institutional archives for continuous, governed AI inferencing without requiring data scientists to manually prepare each dataset

How does Komprise deliver transparent intelligent tiering for universities without disrupting researchers, faculty, or departmental users?

The most common reason higher education IT teams fail to capture the full cold data savings opportunity is organizational rather than technical: departments resist data movement because previous archiving experiences resulted in lost access, broken application paths, or the requirement to file a support ticket to retrieve a file. Komprise Transparent Move Technology was designed specifically to eliminate every one of these friction points:

Researchers access tiered data exactly as before — when Komprise moves a file from primary NAS to cloud object storage, it leaves a Dynamic Link at the original file path built on industry-standard operating system symbolic link constructs; a researcher opening a file they worked on two years ago navigates to the same directory, clicks the same file, and opens it with the same application; there is no change to any workflow, no notification that the file has moved, and no support ticket required
Yale University validates the departmental partnership model — Steve DeGroat, Enterprise Storage Manager at Yale University, explains how his team uses Komprise to collaborate with departments and create data management strategies that serve both departmental and university-wide needs; the combination of showback reporting — which shows departments what their data costs — and transparent tiering — which removes the access risk that previously made departments refuse to participate — is what makes institution-wide tiering programs achievable (watch the video)
Duquesne enabled an all-flash NAS upgrade by first tiering cold data — a storage administrator at Duquesne University identified, tiered, and archived years of cold data to enable the move to an all-flash array; data management policies were put in place to meet the unique needs of each department as the IT team accelerated their path to cloud services and infrastructure; the all-flash upgrade was possible because cold data was removed first; the new array was populated only with active, high-value data, reducing both the hardware cost and the ongoing operational expense
Department-specific policies respect the diversity of higher education data needs — a research lab with active genomics projects has very different tiering thresholds than a completed research archive, which is different again from an administrative records department; Komprise tiering policies can be tailored by department, research group, file type, and project status, giving each part of the institution the data management approach that reflects their actual workflows
Storage savings of 70% are achievable with no user disruption — cutting backup and storage costs by 70% by transparently tiering cold data to object storage using Komprise patented Transparent Move Technology has been achieved by Komprise education customers while enabling researchers to precisely search and find their own data with automated tagging and the Komprise global metadata index; the 70% figure is not a theoretical projection — it reflects what Komprise education customers have achieved in production deployments across leading public research universities and Ivy League institutions

How does Komprise help higher education IT teams turn decades of accumulated research data into an AI inferencing asset — and what does that look like in practice?

Universities sit on some of the most valuable unstructured data estates in the world: decades of research publications, experimental datasets, clinical study records, genomics sequences, and institutional archives that represent extraordinary AI potential. The challenge is that almost none of this data has been classified, tagged, enriched with metadata, or positioned for AI inferencing access. Komprise is the metadata and orchestration layer for enterprise unstructured AI data, and higher education institutions are among the most compelling beneficiaries of that capability:

The Global Metadatabase makes decades of research data discoverable — Komprise continuously indexes all unstructured data across every NAS, cloud, and object storage silo simultaneously, building a unified metadata layer that makes the full institutional data estate searchable by research project, investigator, file type, date range, and custom classification tags; a researcher querying for all genomics files from a specific lab over a ten-year period can find that dataset in seconds across petabytes without knowing which storage system holds each file
Data classification and tagging are the prerequisite for research AI — the Duquesne image search use case illustrates a pattern that applies across research disciplines; before AI inferencing can run against institutional data, that data must be discoverable, classified, and enriched with the domain-specific metadata that makes it queryable by research criteria; KAPPA data services extend this classification to proprietary scientific file formats at petabyte scale, extracting custom attributes from genomics BAM files, FASTQ sequences, and domain-specific research formats using serverless processing
Smart Data Workflows deliver research data to AI pipelines continuously — a university research AI pipeline that relies on manual data preparation will never scale; Smart Data Workflows automate the full sequence from dataset identification through metadata enrichment, sensitive data exclusion, and delivery to any AI service; as new research data arrives and existing datasets age, the workflow runs continuously, ensuring AI inferencing pipelines are always operating on current, governed, precisely curated data
Metadata tags persist to avoid repeating expensive AI compute — Komprise tags results in the Global Metadatabase to cut hundreds of hours of manual effort for departmental teams; the tags become file characteristics that can be queried and acted on, so teams do not have to re-run the AI service on the same data repeatedly, saving time and money; in a university environment where budget constraints make pay-per-use AI costs a real concern, tag persistence is the mechanism that keeps AI data workflows financially sustainable at scale
The path from cold data cost problem to AI research asset is a single platform — universities that begin with Komprise Analysis to understand their data estate, add intelligent tiering to reclaim cold data savings, and upgrade to Komprise Intelligent Data Management to unlock the Global Metadatabase, Deep Analytics, Smart Data Workflows, and KAPPA data services are building the institutional AI data foundation from the same platform motion that reduces storage costs; the Flash Stretch Assessment for qualified institutions managing 500TB or more is the starting point that quantifies the cost savings opportunity before any commitment — and the same analysis that reveals the cold data cost problem simultaneously reveals the AI-ready data asset that systematic classification and tagging would unlock