An evaluation and decision guide for IT leaders and professionals
In this IT Buyer’s Guide from The Register, learn why traditional storage management won’t cut it with today’s massive data growth. Learn what to look for in a solution with a more granular and analytical approach, and find out how to assess which one is best for you to save the most.

Analytics-driven Storage Management

An evaluation and decision guide for IT leaders and professionals
In this IT Buyer’s Guide from The Register, learn why traditional storage management won’t cut it with today’s massive unstructured data growth. Learn what to look for in a solution with a more granular and analytical approach, and find out how to assess which one is best for you to save the most.

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Analytics-Driven Storage Management Buyers Guide FAQs

Why do enterprises need analytics-driven unstructured data management?

Most enterprise IT teams know they have a storage problem. What they often lack is the visibility to understand exactly what that problem costs them, where it is concentrated, and what the most effective response would be. Traditional storage management answers the question of how much storage exists and how full it is. Analytics-driven data management answers the more important questions: what data is in that storage, who owns it, when was it last accessed, how fast is it growing by department or project, and what would happen to costs and capacity if specific data were moved, tiered, or removed.

Without this analytical foundation, organizations make storage procurement decisions based on aggregate utilization trends that cannot distinguish between data that must stay on expensive primary storage and data that has been inactive for three years and could be tiered to cloud object storage at a fraction of the cost. The Freeform Dynamics research that informed this buyers guide identified six core challenges that characterize organizations stuck in the traditional approach: the desire to keep everything, data tiering that creates vendor lock-in, storage platform proliferation across multiple vendors, relentless data growth, inefficient use of expensive primary storage, and resource and skills constraints that make manual management unviable at scale. Analytics-driven unstructured data management addresses all six by providing the visibility, automation, and control that traditional storage tools cannot deliver.

What should IT leaders look for when evaluating unstructured data management solutions?

The buyers guide evaluation framework identifies several critical questions that separate genuine analytics-driven data management from storage tools that simply report utilization metrics. When evaluating any unstructured data management solution, IT leaders should assess capability across six dimensions.

• Visibility: Does the solution provide full visibility across all storage environments simultaneously, including multi-vendor NAS, cloud object storage, and hybrid environments, without requiring software agents on production servers? Can it show data ownership, access patterns, file age, and growth trends at a departmental or project level?
• Transparency: When cold data is moved to lower-cost storage, do users and applications continue to access it from its original path without any disruption, application reconfiguration, or awareness that data has moved? Is the movement genuinely transparent at the file protocol level?
• Openness: Does the solution store tiered data in native format on open, standards-based object storage, or does it create proprietary stubs or formats that create new vendor dependencies? Can the organization change storage destinations or vendors without recalling and reformatting its data?
• Policy flexibility: Can tiering and lifecycle policies be tailored to different user groups, departments, projects, and data types? Can policies be based on actual access patterns and business context rather than just file age?
• Cost modeling: Can the solution model the cost and capacity impact of different data management scenarios before committing to any action? Can it quantify the savings opportunity in a specific environment before a purchase decision is made?
• AI readiness: Can the solution identify, classify, and curate unstructured datasets for AI pipelines, detect and exclude sensitive data before ingestion, and enrich file metadata with domain-specific business context at petabyte scale?

Why Komprise for analytics-driven unstructured data management?

Komprise was purpose-built to answer the evaluation questions this buyers guide raises, with capabilities that have deepened significantly since the original publication to address the AI data preparation challenge that now defines enterprise storage strategy.

On visibility, Komprise Analysis scans all file and object data across multi-vendor NAS and cloud environments without agents, building a continuously updated inventory in the Global Metadatabase that captures file age, owner, type, size, access history, and custom tags for every file. This is unified visibility across the full storage estate, not a per-vendor dashboard.

On transparency, Komprise Transparent Move Technology tiers cold data to any cloud or object storage destination in its native file format with no proprietary wrapping. Users access tiered files from their original paths via Dynamic Links with no awareness that data has moved and no rehydration required. This is true file-level transparency, not stub-based redirection that creates new dependencies.

On openness, Komprise stores data on the customer’s chosen cloud or object storage destination in open, standards-based format. There is no Komprise-specific format, no lock-in, and no cost to change destinations. This directly addresses the lock-in challenge identified as one of the core problems in the buyers guide framework.

On policy flexibility, Komprise supports tiering policies based on last accessed time as the standard approach, with Deep Analytics Actions enabling more precise policies where a saved query becomes the direct input to a data management policy. Policies can be configured per department, project, file type, owner, or any combination of metadata criteria.

On cost modeling, the Komprise Flash Stretch Assessment and what-if scenario modeling in Komprise Analysis quantify the specific capacity reclamation and cost savings opportunity in each organization’s environment before any data is moved or any hardware is purchased.

On AI readiness, Komprise Smart Data Workflows automate dataset curation, sensitive data detection, and governed delivery to AI platforms. KAPPA data services extract domain-specific custom metadata from file content at petabyte scale. The Global Metadatabase serves as the unified metadata catalog that connects storage management to AI data preparation in a single platform.

Why is right now the most important time to act on unstructured data management?

The buyers guide identified the core challenges that made analytics-driven storage management a strategic priority. In 2026, three additional forces have made inaction significantly more expensive than it was when the guide was written.

First, Memflation has arrived. Gartner is calling the current memory price surge Memflation. NAND flash prices are forecast to increase 234% in 2026 driven by AI data center demand consuming available supply, with no meaningful relief expected until late 2027. For organizations with 60-70% of their NAS data sitting cold on expensive primary flash storage, the cost of that cold data has more than doubled in 12 months. Every quarter of inaction compounds this cost.

Second, AI programs are now competing for the same storage capacity that cold data is occupying. Enterprise AI teams need high-performance primary storage for training datasets, inferencing workloads, and RAG pipelines. Organizations that have not addressed cold data accumulation are discovering that their AI programs are capacity-constrained by inactive data rather than limited by their AI infrastructure investment. This is an avoidable problem but only if addressed before the next storage refresh cycle.

Third, agentic AI is raising the governance stakes. As enterprises deploy AI agents that autonomously retrieve and act on enterprise data, the quality, classification, and governance of the unstructured data estate directly determines whether AI programs produce reliable outcomes or compliance risk. Organizations that have not implemented systematic metadata management, sensitive data detection, and lifecycle governance for their unstructured data are not ready for agentic AI, and the cost of retrofitting governance after an AI incident is significantly higher than implementing it proactively.
Source: Gartner Memflation forecast April 2026
Source: Komprise Flash Stretch Assessment

What does the path from evaluation to implementation look like with Komprise?

One of the barriers to acting on unstructured data management is uncertainty about how disruptive or complex the implementation will be. Komprise addresses this through a non-disruptive, analytics-first approach that produces value before any data is moved.

For an unstructured data migration, the Komprise ACE tool assesses the customer environment before any data is scanned or moved, analyzing network topology, identifying potential bottlenecks, and testing connectivity across the specific source and destination infrastructure. This surfaces infrastructure issues before they affect production operations.

Once Komprise Analysis begins scanning, the first deliverable is visibility: a comprehensive picture of what data exists across all NAS and cloud environments, how much is cold, who owns it, and what the projected cost savings would be under different tiering scenarios. Most customers see this within days of deployment, before committing to any data movement.

The what-if scenario modeling in Komprise Analysis quantifies the specific capacity reclamation and savings opportunity in the customer’s environment. IT teams can present a data-backed business case to finance and executive stakeholders before any action is taken. This separates the visibility and analysis phase from the execution phase, eliminating the risk of acting before understanding the impact.

Intelligent tiering policies are then configured and run on a schedule, moving cold data automatically and continuously without any manual intervention or disruption to users or applications. Most Komprise customers reclaim 70% or more of primary NAS capacity and achieve payback within months of deployment.

For organizations facing a storage refresh decision, the Komprise Flash Stretch Assessment provides a pre-purchase analysis that quantifies exactly how much of the proposed refresh can be deferred or eliminated through intelligent tiering, at current Memflation prices. This single analysis has saved organizations millions of dollars in deferred capital expenditure.