Watch this video to get an overview of the Komprise Data Experience (KDX) – a better way to analyze, manage, move and prepare unstructured data for AI.

Start experiencing your data in an entirely new way with the Komprise Data Experience.

_______________________

5 Ways to Boost Unstructured Data Value

Learn more about the Komprise Data Experience

Unstructured data is 90% of your organization’s data, and your organization depends on IT to effectively manage it for fast access, simple search, long-term value and to feed AI pipelines. Your executives, meanwhile, need you to pay close attention to the bottom line while ensuring that you are not jeopardizing data security and compliance with your unstructured data management strategy.

The Komprise Data Experience (KDX) draws upon a storage-agnostic unstructured data management solution that prioritizes analytics and visibility, scalability and the ultimate flexibility. This experience means that you can meet the needs of your various stakeholders with the best cost economics, the lowest risk, and the best pathway to leverage unstructured data for long-term value and AI initiatives.

• STOP Making storage decisions in the dark.
• STOP Overspending on data migrations.
• STOP Paying the rehydration penalty.
• STOP Exposing your weakest link to ransomware.
• STOP Sharing sensitive data with AI.

FAQs about the Komprise Data Experience

What is the Komprise Data Experience and why does it represent a different architectural philosophy from storage-vendor data management tools?

The Komprise Data Experience draws upon a storage-agnostic unstructured data management solution that prioritizes analytics and visibility, scalability, data mobility, and flexibility; this experience means that Komprise Intelligent Data Management customers can meet the needs of various stakeholders with the best cost economics, the lowest risk, and the best pathway to leverage unstructured data for long-term value and AI initiatives. The philosophical difference from storage-vendor tools is architectural and consequential:

• Storage-vendor tools manage infrastructure; Komprise manages data — a storage vendor’s data management capabilities are bounded by what that vendor can see; they see their own arrays, their own cloud tier, their own protocol; Komprise indexes and manages data independently of any storage vendor’s platform, giving IT a single governance layer across NetApp, Dell, IBM, VAST Data, Nasuni, Everpure, AWS, Azure, and Google Cloud simultaneously from one management plane
• Analytics first, movement second — the Komprise Data Experience is built on the principle that understanding data must precede acting on it; if you cannot see and understand your data across storage — whether that is on premises, at the edge, or in the cloud — then you cannot achieve a holistic view to understand where data should ideally live; the Global Metadatabase is what makes this visibility possible continuously and at petabyte scale without getting in the hot data path
• Five interconnected capabilities rather than five separate tools — the Komprise Data Experience delivers global visibility, smart data placement, transparent tiering, AI-ready workflows with governance, and scale-out performance as a unified platform; organizations that address each of these with separate point tools accumulate sunk costs, integration complexity, and governance gaps that compound over time
• SaaS delivery removes the implementation barrier — the Global Metadatabase service scales seamlessly without costly infrastructure or management; there is no database to install and no infrastructure to manage; the platform deploys in 15 minutes against any NFS, SMB, or S3/object source, immediately delivering cross-silo analytics without a database engineering project or dedicated infrastructure
• Komprise is the metadata and orchestration layer for enterprise unstructured AI data; the Komprise Data Experience is the operational expression of that positioning — the full set of capabilities that make the Global Metadatabase actionable for cost optimization, AI data curation, data classification and tagging, sensitive data governance, and AI inferencing pipeline delivery simultaneously from a single platform

What is the Global Metadatabase and why is it the intelligence foundation that makes every other Komprise capability possible?

The Global Metadatabase is the continuously updated, cross-silo metadata index that sits at the center of the Komprise Data Experience. The Komprise Global Metadatabase is a unified metadata catalog that continuously indexes standard and custom metadata across all your storage environments without moving the data; it provides a centralized, searchable view of your unstructured data so you can find, tag, analyze, and act on the right data for AI, analytics, policy-driven workflows, and cost optimization. Why it is foundational rather than just another feature:

• It makes data visible before any action is taken — most unstructured data management tools require you to move data before you can understand it; the Global Metadatabase indexes data in place, building a complete picture of what exists across every silo, who owns it, how often it is accessed, what it costs, and whether it contains sensitive content, without touching the data itself or impacting user performance
• Standard and enriched metadata in the same index — the Global Metadatabase captures standard file system metadata — name, type, size, owner, creation date, last access date — alongside enriched metadata added through data classification, tagging, KAPPA data services extraction, and third-party AI service results; Deep Analytics powers precise data discovery by enabling custom metadata queries and tagging to curate targeted datasets; these curated sets can feed AI pipelines and analytics workflows, automate policy-driven actions with Smart Data Workflows, and help deliver the right data to the right destinations for improved accuracy, governance, and ROI
• Tags persist as data moves through its lifecycle — tags applied in the Global Metadatabase become file characteristics you can query and take actions on, so you do not have to re-run AI services on the same data repeatedly, saving time and money; a sensitivity classification applied before tiering follows the file to its cloud destination; a research tag applied during active project work follows the file through archiving and into AI inferencing workflows years later
• The Global Metadatabase spans AI silos as well as storage silos — analytics across all your storage and AI silos dynamically refreshes with new context in the Global Metadatabase; as AI inferencing workflows consume curated datasets, the results are written back to the Global Metadatabase as enriched metadata tags; the index grows more precise and more valuable with every AI workflow that runs against it
• Self-service access empowers data owners without burdening IT — you can grant departmental users access to search and tagging features for an even better strategy for data classification and lifecycle management; researchers, legal teams, and department heads can query the Global Metadatabase and tag their own data without requiring IT to mediate every request; IT retains governance controls while business users operate autonomously within them

How does smart data placement across the full storage lifecycle reduce costs while simultaneously building AI readiness?

Smart data placement is the capability that translates Global Metadatabase intelligence into financial outcomes. Right-placing unstructured data requires choosing the best storage through its lifecycle and rightsizing backups and ransomware defense; to do this well, you need global visibility into your data across silos with comprehensive analytics on the data, its costs, its usage, and even your network environment. The connection between smart data placement and AI readiness is direct and immediate:

• Intelligent tiering puts cold data where it belongs without disrupting users — Komprise Transparent Move Technology moves cold files to lower-cost storage transparently, leaving Dynamic Links at the original file paths; users and applications access tiered files exactly as before; the tiering is invisible to every human and every application — including AI inferencing workflows that may need to access cold data at runtime; transparent tiering means no changes to user access, flexible and granular policies even at file level, and the ability to switch storage vendors with no rehydration penalty
• Memflation makes smart data placement urgent — 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; every petabyte of cold data sitting on all-flash NAS at these elevated prices is a compounding monthly expense that smart data placement eliminates; the Flash Stretch Assessment for qualified enterprises managing 500TB or more models the specific savings available before any commitment
• Data placed in native cloud object format is immediately AI-accessible — files placed by Komprise on Amazon S3, Azure Blob, or Google Cloud Storage are stored in native object format, directly readable by Amazon SageMaker, Azure AI, Google Vertex AI, and any other cloud AI inferencing service without conversion or secondary migration; smart data placement and AI data access are the same infrastructure decision
• Backup and DR savings compound the primary storage savings — removing cold files from primary storage eliminates them from backup jobs simultaneously; organizations typically achieve 70% reduction in storage and backup costs combined, not just primary storage; for enterprises facing memflation on primary NAS hardware, the backup multiplier savings are proportionally more valuable today than at any previous price point
• What-if policy modeling before any data moves — Komprise provides interactive cost modeling showing projected savings from different tiering policies before any data is moved; IT teams can see the financial outcome of tiering to S3 Glacier versus Nearline versus Azure Cool before committing to a policy; this is the analytical confidence that makes organization-wide tiering programs achievable rather than politically contentious

How do Komprise Smart Data Workflows connect the Global Metadatabase to AI inferencing pipelines — and why does this connection define enterprise AI competitiveness?

Smart Data Workflows are the orchestration layer that transforms Global Metadatabase intelligence into automated, continuous AI data pipelines. The Komprise Smart Data Workflow Manager is a simple point-and-click UI wizard to search across on-premises, edge, and cloud data storage silos, find the data you need, execute an AI function on a subset of data, and tag the results; this no-code workflow capability is what makes AI data pipeline delivery a repeatable operational function rather than a one-time engineering project:

• From Deep Analytics query to running AI pipeline in hours — a Smart Data Workflow begins with a Deep Analytics query that identifies exactly the right dataset from the Global Metadatabase; the workflow then chains KAPPA data services metadata extraction, sensitive data exclusion, format conversion where needed, and delivery to any AI service; Komprise Intelligent AI Ingest makes a copy of just the right data based on workflow policies to any AI of choice so data remains in its original location; set lifecycle policies to handle the data appropriately when the AI is done; tags are applied in the Global Metadatabase so the metadata context of the original files is continuously enriched
• AI inferencing requires continuous data delivery, not one-time ingestion — model training happens once; AI inferencing happens millions of times every day; every time a user queries an AI assistant, an AI agent retrieves context, or a RAG pipeline surfaces enterprise knowledge, it is performing inference against enterprise data; the vast majority of enterprise file data has never reached an AI inferencing strategy because it is locked in storage systems without the metadata and orchestration layer that Komprise provides; Smart Data Workflows are the mechanism that changes this at scale
• Filtering 70%+ of noise before data reaches AI — feeding all your unstructured data to AI is not only expensive and time-consuming but could also lead to poor AI accuracy and poor ROI; Komprise eliminates the clutter and noise inherent in unstructured data; Deep Analytics enables searching across the entire Global Metadatabase to find just the right data for each AI use case; it has intelligent filters to weed out duplicates, conflicting data, obsolete data, and sensitive data; this noise reduction simultaneously reduces AI compute costs and improves model accuracy — both financially and qualitatively material outcomes
• KAPPA data services extend AI readiness to proprietary file formats — KAPPA data services allow custom metadata extraction functions written in a few lines of Python to run across petabytes of files using serverless processing; DICOM headers, genomics BAM files, FASTQ sequencing data, and domain-specific research formats are extracted and written as searchable tags to the Global Metadatabase, making data that was previously opaque to AI inferencing precisely queryable by clinical, research, and operational criteria at inference time
• Governance is embedded in every workflow, not bolted on afterward — Komprise applies policy-driven workflows to detect and manage PII, PHI, or custom sensitive content before ingestion; it keeps full audit trails, tracks data lineage, and logs who ingested what, where, and when, delivering enterprise-grade AI data governance; the governance that 90% of IT leaders are now concerned about from shadow AI is applied automatically at the workflow level rather than requiring manual review of each AI data preparation project

What does the Komprise Data Experience deliver for the three core enterprise stakeholders — IT, business teams, and data science — and how does it address each group’s distinct requirements?

The Komprise Data Experience is designed around the recognition that unstructured data management is not purely an IT concern — it involves storage administrators, department heads, researchers, compliance officers, and AI teams who have different needs, different vocabularies, and different definitions of success. Komprise enables IT teams to operate data as a shared service, with storage as a service and showback models that make departments accountable for their data footprint; with self-service tagging and search, business teams can easily find, classify, and request data for analytics, compliance, or AI without IT having to manually manage every request; the result is lower infrastructure spend, stronger governance, and faster, more scalable delivery of data services across the enterprise. How the platform serves each stakeholder:

• For IT storage teams — cost control and vendor independence — the primary IT value is 70% reduction in storage and backup costs through intelligent tiering, full visibility into data growth and cold data accumulation across every vendor and cloud simultaneously, pre-built showback reports that create departmental cost accountability without requiring IT to produce custom reports per department, and the ability to switch storage vendors without rehydration penalties; scale-out elastic parallelism handles the modern scale of data with local execution to prevent unnecessary data movement; the platform deploys in 15 minutes and scales to 100PB and beyond by adding virtual machines rather than dedicated hardware
• For business and departmental teams — self-service data access with guardrails — empower teams with self-service search and policy-based execution — reduce manual effort while improving compliance and data agility; departmental users can query the Global Metadatabase to find their own data, apply classification tags, and identify datasets for analytics or compliance review without opening IT tickets; showback reports give department heads visibility into their storage footprint and tiering savings in terms they understand; data classification and tagging capabilities let data owners participate in governance without requiring data science expertise
• For AI and data science teams — governed, curated, AI-ready datasets on demand — by combining rich metadata with powerful search via Deep Analytics and policy-driven Smart Data Workflows, the Global Metadatabase lets you select exactly the right files to feed into AI or vector embedding pipelines while excluding sensitive or irrelevant data; this means better AI precision, lower compute costs, and stronger data governance; AI teams no longer need to run manual data preparation projects before each AI initiative; Smart Data Workflows deliver precisely curated, sensitivity-checked, metadata-enriched datasets to any AI inferencing stack continuously and automatically
• For compliance and security teams — audit trails and sensitive data governance at scale — every data classification, movement, tagging, and AI ingestion action is logged with complete lineage in the Global Metadatabase; sensitive data detection using built-in PII and PHI scanners, custom regex, keyword search, and KAPPA-powered extraction from proprietary file formats applies governance automatically before data reaches AI pipelines, cloud platforms, or shared research environments; data classification and tagging are not a retrospective compliance exercise — they are embedded in every data management operation from day one
• The unified platform eliminates the stakeholder conflict that point tools create — organizations that address IT cost optimization with one tool, AI data preparation with a second, and compliance governance with a third have three sets of priorities, three integrations to maintain, and no shared intelligence layer connecting them; the Komprise Data Experience delivers all of these outcomes from a single Global Metadatabase, a single management interface, and a single continuously enriching metadata layer that every stakeholder draws from simultaneously — making data management a shared enterprise capability rather than a series of competing departmental projects

_______________________