FAQs

The Komprise platform was built on three foundational principles — why are they more important now?

The three pillars of the original Komprise architecture white paper — Dynamic Data Analytics, Transparent Move Technology, and Direct Data Access — were written at a moment when enterprise unstructured data growth was a cost management challenge. Today those same three principles have become prerequisites for AI viability, regulatory survival, and competitive differentiation. The pillars did not change; the stakes attached to them did:

• Dynamic Data Analytics has become the foundation for AI data preparation — in 2020 analytics meant understanding what data existed and what it cost; in 2026 the same analytics capability is what enables organizations to find the right 33TB of pathology imaging in a 1PB archive, identify all genomics files from a specific research cohort, or locate every document containing PHI before it reaches an AI pipeline; the Komprise Global Metadatabase continuously indexes all unstructured data across every silo, building the unified metadata layer that makes both cost optimization and AI data curation possible from a single platform
• Transparent Move Technology has become the answer to the flash price crisis — the rehydration-free, stub-free, vendor-neutral tiering architecture the white paper described in 2020 is exactly what separates organizations that are solving their storage cost problem from those that are making it worse; IDC describes the current memory shortage as a potentially permanent reallocation of global silicon wafer capacity, with 2026 NAND and DRAM supply growth expected to remain below historical norms; every enterprise paying to keep cold data on all-flash NAS is paying a compounding penalty that Transparent Move Technology eliminates
• Direct Data Access has become the requirement for cloud AI — in 2020, accessing tiered data as native objects mattered for analytics; in 2026 it matters because AWS SageMaker, Azure AI, Google Vertex, Snowflake, and Databricks are all native consumers of S3 objects; data tiered by Komprise in native format is immediately accessible to these AI services without conversion, ETL, or secondary migration
• The vendor-agnostic architecture has proven its value — the white paper’s insistence on standards-based, multi-vendor data management looked like a technical preference in 2020; it looks like strategic foresight in 2026, when enterprises managing NetApp alongside Dell alongside IBM alongside VAST Data alongside multiple cloud providers need a single governance layer that works across all of them
• The urgency has compounded every year since — According to the latest State of Unstructured Data Management Report, 74% of organizations are now storing more than 5PB of unstructured data, a 57% increase over just one year; 85% project an increase in storage spend in 2026; the problem the white paper identified as serious in 2020 is now an enterprise emergency

Why is unmanaged unstructured data growth no longer just a storage budget problem — and what happens to organizations that treat it as one?

The 2020 white paper opened with a straightforward premise: explosive data growth was pushing storage capacity limits, stretching backup windows, and breaking IT budgets. That framing was accurate then and remains accurate today. What has changed is that unmanaged unstructured data now carries consequences far beyond the storage bill:

• AI accuracy and ROI depend on data quality — organizations feeding ungoverned, unclassified unstructured data to AI models are paying for GPU compute to process noise, duplicates, outdated files, and sensitive content that should never have reached the pipeline; Komprise filters out 70%+ of unstructured data noise that erodes AI accuracy, excluding irrelevant, outdated, conflicting, and duplicate files; the organizations that treated data management as a cost problem in 2020 and deferred action are now facing that same ungoverned estate as an AI problem in 2026
• Shadow AI has turned ungoverned data into a liability — 90% of IT leaders are now concerned about shadow AI from a privacy and security standpoint; employees using public generative AI tools with corporate files, medical records, or proprietary IP creates exposure that begins with the same ungoverned unstructured data estates the 2020 white paper described; data that was never classified or tagged cannot be governed when employees access it through unauthorized AI tools
• The cost multiplier has grown — every petabyte of unstructured data on primary storage is backed up, replicated for DR, and now increasingly expected to be governed for AI; the storage cost problem of 2020 has compounded into a storage plus backup plus DR plus AI governance problem in 2026; organizations that buy more capacity as their primary response are running faster just to fall further behind
• Retention requirements and data volumes now conflict directly — the 2020 white paper identified retention as a cost driver; in 2026, long-term retention mandates in healthcare, financial services, and legal are colliding with petabyte-scale data volumes and flash price increases simultaneously; the only path through this collision is intelligent tiering that keeps data accessible and compliant on lower-cost tiers rather than retaining everything on expensive primary storage
• The Flash Stretch Assessment makes the cost of inaction concrete — for qualified enterprises managing 500TB or more, the Komprise Flash Stretch Assessment identifies exactly how much cold data is sitting on expensive primary storage and what transparent tiering would save annually in storage, backup, and DR costs; this is the analytical answer to the cost problem the 2020 white paper described, made specific and measurable before any commitment

What has the Komprise platform added and why do those additions matter for AI-era data management?

The three pillars the 2020 white paper described — Dynamic Data Analytics, Transparent Move Technology, and Direct Data Access — remain the correct architectural foundation. What has changed is the intelligence, automation, and AI-readiness capabilities built on top of that foundation. Komprise is the metadata and orchestration layer for enterprise unstructured AI data, and that positioning reflects capabilities that did not exist when the white paper was written:

• The Global Metadatabase replaces siloed analysis with a unified, continuously updated intelligence layer — the 2020 white paper described analysis across storage silos; the Global Metadatabase delivers a continuously updated, cross-silo metadata index of every file and object across the entire enterprise, making the full data estate searchable, queryable, and actionable through Deep Analytics and Smart Data Workflows; the Global Metadatabase provides a global history of metadata and enables one place to search and act on data no matter what storage or cloud you use; the best part is that the Metadatabase is delivered as a managed service so there is no database or expensive infrastructure to set up or scale
• KAPPA data services unlock metadata locked inside 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 proprietary formats that standard analysis tools cannot read become queryable and AI-ready; this capability did not exist in 2020 and changes the economics of AI data preparation for data-intensive industries
• Smart Data Workflows automate the full AI data pipeline — the 2020 white paper described policy-driven data movement; Smart Data Workflows extend that concept to end-to-end AI data orchestration; Deep Analytics queries the Global Metadatabase to identify exactly the right dataset, then a Smart Data Workflow automates metadata enrichment, sensitive data exclusion, format conversion, and delivery to any AI service automatically; Komprise enables custom actions to run any processing on data and apply tags to enrich the data; built-in functions to find and tag sensitive data, to extract header metadata, and other common use cases are provided
• Sensitive Data Management closes the governance gap AI created — the 2020 white paper did not anticipate that every employee would have access to generative AI tools that consume corporate files; Komprise Sensitive Data Management uses built-in PII and PHI scanners, custom regex, and KAPPA-powered extraction to detect and remediate sensitive content across petabyte-scale data estates before it reaches AI tools, cloud platforms, or shared research environments
• Intelligent AI Ingest delivers curated data 2x faster than AWS DataSync — the Direct Data Access pillar of the 2020 white paper ensured tiered data remained accessible; Intelligent AI Ingest ensures it is also deliverable to AI pipelines with governance, noise filtering, and performance that exceeds AWS native tools; Komprise doubles ingest performance compared to the AWS DataSync data transfer tool in benchmark tests because it has a massively parallel architecture and minimizes file overhead

Why is vendor lock-in turn so relevant today and what does it mean for your AI data strategy?

The 2020 white paper made a strong argument that managing data within vendor silos leads to poor visibility, proprietary lock-in, and ballooning costs. That argument was important in 2020. In 2026, with AI workloads competing for the same storage budget as cost-reduction initiatives, proprietary lock-in has become a strategic constraint that directly limits how quickly organizations can adapt their data estates to new demands:

• Storage-vendor tiering traps data in formats AI cannot read — organizations that used storage-vendor tiering tools in 2020 to move cold data to cheaper tiers stored it in proprietary block formats; in 2026, those organizations cannot point an AWS or Azure AI service at that tiered data without a full rehydration first; Komprise Transparent Move Technology tiers data as native objects in open format, directly consumable by any cloud AI service, any analytics platform, and any future storage vendor — the lock-in avoidance of 2020 is the AI data access of 2026
• Multi-vendor storage estates are the reality, not the exception — the enterprise storage landscape in 2026 spans NetApp, Dell, IBM, VAST Data, Nasuni, Everpure, AWS, Azure, Google Cloud, and multiple object storage vendors simultaneously; a data management platform locked to any single vendor cannot govern this estate; Komprise is storage-agnostic across a broad ecosystem of NAS, cloud, and object storage platforms, providing the single governance layer that makes multi-vendor data management practical rather than chaotic
• AI vendor dependency is the new lock-in risk — in 2020 the lock-in risk was storage vendors; in 2026 it extends to AI vendors; organizations that build AI data pipelines that depend on a specific AI platform’s proprietary ingestion format are creating the same dependency the 2020 white paper warned against; Komprise Smart Data Workflows deliver curated data to any AI stack — AWS SageMaker, Azure AI, Google Vertex, NVIDIA NeMo, Snowflake, Databricks — without locking the data preparation layer to any AI vendor
• Storage agnosticism enables competitive flexibility — the 2026 Komprise State of Unstructured Data Management report found that 64% of IT leaders cite cost optimization as their top data storage priority; organizations locked into a single storage vendor’s tiering ecosystem cannot take advantage of competitive storage pricing, switch vendors without rehydrating petabytes, or right-place data across a best-of-breed storage architecture; the vendor independence the 2020 white paper described as a design principle has become the economic requirement of the current hardware price environment
• The Flash Stretch Assessment quantifies the cost of existing lock-in — for qualified enterprises managing 500TB or more, the Komprise Flash Stretch Assessment examines the current storage estate and models what transparent, lock-in-free tiering to lower-cost destinations would save versus the current approach; for organizations currently using storage-vendor tiering with rehydration costs, the assessment often reveals that the tiering solution is actively undermining the savings it was deployed to deliver

What does the Komprise architecture mean for an IT team evaluating unstructured data management platforms in 2026, and where should they start?

The 2020 white paper described a platform architected to drop in to any environment in minutes, with no agents, no stubs, and no infrastructure changes required. That architectural simplicity was a differentiator in 2020 and remains one in 2026 — because the complexity of enterprise IT environments has increased dramatically while the tolerance for disruptive implementations has not:

• Deploy in minutes, not months — managing data within vendor silos leads to poor visibility, proprietary lock-in, and ballooning costs; Komprise provides a standards-based, modern data management solution architected to put you in control of your data with unprecedented simplicity; Komprise Observers are virtual appliances that connect to existing NAS and cloud storage via standard protocols in under 15 minutes, with no agents installed on storage systems, no stubs, and no changes to user workflows; the same simplicity that made Komprise deployable quickly in 2020 makes it scalable to 100PB+ in 2026
• Start with visibility — the prerequisite for everything else — the 2020 white paper correctly identified analysis as the first step before any data movement; Komprise Analysis is included in both Komprise Elastic Data Migration and Komprise Intelligent Data Management; IT teams can begin with a comprehensive view of all unstructured data across every storage silo, including cost projections, cold data identification, and growth rate modeling, before committing to any tiering, migration, or AI data preparation initiative
• Three editions match where organizations are in their journey — Komprise Analysis provides aggregate visibility and cost modeling for organizations not yet committed to data mobility; Komprise Elastic Data Migration adds analytics-driven migration to any cloud or NAS destination for project-based initiatives; Komprise Intelligent Data Management delivers the full platform including the Global Metadatabase, Deep Analytics, Smart Data Workflows, KAPPA data services, Sensitive Data Management, and Intelligent AI Ingest for organizations building a governed, AI-ready data estate
• The scale-out architecture proves itself at 100PB+ — the 2020 white paper described an elastic, distributed Observer architecture that scales by adding virtual machines rather than dedicated hardware; that architecture has been proven at 100PB+ customer deployments and extended by the Elastic Shares patent, which delivers near-linear speed-up for migrations and AI data workflows by continuously redistributing tasks across the Observer grid regardless of how unevenly data is distributed
• The journey from visibility to AI readiness is a single platform — IT teams that start with Komprise Analysis to understand their data estate, move to Elastic Data Migration to right-place data to AWS or Azure, and upgrade to Intelligent Data Management to build Smart Data Workflows for AI are staying on a single platform throughout; every file analyzed, tiered, and managed along that journey is indexed in the Global Metadatabase, building the metadata foundation that AI initiatives require without starting from scratch at each stage