Five Requirements of a Unified Control Plane for Unstructured Data Management Services

Unstructured Data Management is Becoming Increasingly Important

GigaOm notes in their Unstructured Data Management Radar Report:

“As data ecosystems flourish, sophisticated unstructured data management (UDM) tools are emerging, poised to unlock the vast potential of dormant data and propel organizations into a data-driven future.” The report also advises: “Strategic deployment of UDM solutions grants organizations full visibility into their data, informing the development of cost-effective roadmaps that maximize ROI on data storage.”

Unstructured Data Management is Not Storage Management

Unstructured data management has emerged as a new category that encompasses elements of data classification, mobility, governance, and cost optimization. While data storage and backup vendors have some data management capabilities, these solutions are focused on optimizing their own devices and deployments, not providing comprehensive data visibility, mobility and value across heterogenous environments. Furthermore, they address the problem from a storage-centric perspective, leveraging their storage file system, which leads to inefficiencies and lock-in that can be paralyzing to customers.

Five Requirements for an Unstructured Data Control Plane

Here are five requirements for a modern approach to unstructured data visibility, mobility and management which achieves maximum data storage price/performance optimization and data value:

1. Ease of Set Up and Administration. Are you administering a unified product or multiple piece parts? How many admin guides are there? What does it take to deploy and administer the solution?
2. Agentless Architecture. Can the solution scale across environments without complexity or are you stuck managing many brittle connectors? What are the connectivity requirements and what is required for upgrades and ongoing management?
3. Visibility + Mobility. Can the solution provide actionable data and storage insights? Can it help you define and execute data movement at scale with policies suited to your data and not to the storage cluster? Storage Insights demo.
4. Native Data Access without Vendor Lock-In. As you move your data (e.g. tier data to the cloud) are you able to access that data in the new location natively or do you need to go through the source storage system? When it’s time to upgrade your storage system, do you need to rehydrate all the data you tiered to migrate to the new system and then tier the data again from the new system?
5. Unlock Data Value. Does the solution provide easy mechanisms to find specific data sets and feed AI/ML engines and other processors? Does it allow you to tag your files based on its content and create custom workflows to address data governance and compliance, which are core data management functions?

Why Storage Agnostic Matters in a Data Control Plane

Unstructured data management requires a cohesive product vision, product architecture and long-term commitment from a vendor to deliver enterprise scale. This is exactly what Komprise has done since its inception, and why we are successfully managing an exabyte of data across customers. At Komprise, we believe that data management functionality is a layer independent of storage. By considering data to be separate from the storage in which it resides, it is possible to manage data holistically across vendors, be they on-premises storage arrays or cloud providers–and across technologies, be they files or objects. This approach has allowed Komprise to create an unstructured data management solution that is vendor agnostic and integrates tightly with on-premises and cloud storage to create a hybrid data management platform.

Komprise has also achieved multiple industry honors for our Komprise Intelligent Data Management platform. Check out the Awards page here.

Here are a few commonly asked questions about the benefits of a unified control plane:

What is a unified control plane for unstructured data management and why do enterprises need one?

A unified control plane for unstructured data management is a single, storage-agnostic platform that provides consistent visibility, mobility, governance, and cost optimization across all unstructured file and object data regardless of where it lives — on-premises NAS, cloud object storage, or hybrid environments. Enterprises need one because point solutions and storage-vendor-native tools each manage only their own silo, leaving IT with fragmented visibility, inconsistent policies, and no way to manage data holistically at petabyte scale. Key reasons a unified control plane matters:

• Single pane of glass — one platform manages analysis, tiering, migration, sensitive data detection, and AI data workflows across any combination of NetApp, Dell, IBM, VAST Data, Nasuni, Everpure, AWS, Azure, and Google Cloud, without separate tools or admin guides per vendor
• Storage-agnostic policies — data movement and lifecycle policies are defined by data characteristics such as file age, type, owner, sensitivity status, and project code, not by the storage cluster the data happens to sit on
• No vendor lock-in — a true unified control plane writes data in open, native formats and never sits in the hot data path, so enterprises retain full flexibility to change storage vendors without rehydration costs or data migration projects
• AI readiness at scale — GigaOm notes that strategic deployment of unified data management solutions grants organizations full visibility into their data, informing the development of cost-effective roadmaps that maximize ROI on data storage and AI initiatives
• Proven at exabyte scale — Komprise manages more than an exabyte of unstructured data across enterprise customers using a single, agentless, distributed platform with no central database or single point of failure

How does a unified unstructured data management control plane accelerate AI initiatives and improve AI data quality?

A unified control plane is the foundation that makes enterprise AI initiatives viable at scale. Without it, AI data preparation is manual, inconsistent, and prohibitively expensive at petabyte scale. With it, every step of the AI data pipeline from discovery to ingestion is automated and governed. The Komprise approach:

• Global Metadatabase as the AI foundation — Komprise continuously indexes all file and object data across every storage silo, building a unified, queryable metadata layer that Smart Data Workflows query to identify exactly the right files for any AI use case without moving the full dataset
• Intelligent AI Ingest — Komprise filters 70%+ of unstructured data noise including duplicates, outdated files, and irrelevant content before AI ingestion, improving model accuracy and reducing GPU compute costs; Komprise delivers curated data to any AI stack up to 2x faster than standard transfer tools
• KAPPA Data Services — serverless processing extracts custom, domain-specific metadata from proprietary file formats including DICOM medical images, genomics BAM files, and financial documents at petabyte scale using a few lines of Python, enriching the Global Metadatabase with the context AI models need to produce accurate results
• Sensitive data governance — Komprise Sensitive Data Management detects PII, PHI, and IP across the full data estate and excludes or remediates sensitive files by policy before they reach AI pipelines, ensuring compliance with HIPAA, GDPR, and internal governance requirements
• Elastic Shares acceleration — Komprise Elastic Shares technology applies dynamic partitioning to keep all compute resources fully utilized during large-scale AI data processing jobs, delivering near-linear speed-up across metadata extraction, AI ingestion, and data migration workflows at petabyte scale

Why does storage-agnostic architecture matter for unified unstructured data management and what are the risks of storage-vendor-native approaches?

Storage-vendor-native data management solutions are designed to optimize within a single vendor’s ecosystem. They cannot manage data across the multi-vendor, hybrid cloud environments that enterprises actually operate, and they create dependencies that become progressively more expensive to exit. The storage-agnostic alternative:

• Multi-vendor reality — enterprises store data across NetApp, Dell, IBM, VAST Data, Nasuni, Everpure, and multiple cloud providers simultaneously; no single storage vendor’s management tools span all of these with consistent policy enforcement, analytics, and governance
• Lock-in tax — storage-centric approaches leverage the vendor’s own file system to provide management capabilities, which means data movement policies, tiering configurations, and metadata are all tied to that file system; replacing the storage requires rehydrating all tiered data first and rebuilding all policies from scratch
• AI data accessibility — storage-vendor tiering stores data in proprietary block formats that require the originating storage OS to be running for access; Komprise writes data in native file and object formats so cloud AI services can access it directly at the destination without any intermediary
• Independent innovation — because Komprise treats data management as a layer independent of storage, it can adopt new AI capabilities, new cloud integrations, and new metadata enrichment approaches without being constrained by a storage vendor’s product roadmap
• Unified governance — a single Komprise platform enforces consistent sensitive data policies, retention rules, and AI workflow governance across all storage vendors and clouds, whereas storage-vendor tools enforce policies only within their own environment

How does a unified unstructured data management platform reduce storage costs and help enterprises offset the current surge in flash and NAND prices?

A unified control plane addresses storage costs at every layer simultaneously — hardware, backup, DR, and cloud — rather than optimizing a single storage vendor’s array in isolation. In a market where Gartner estimates DRAM and SSD prices will rise 130% by end of 2026, this comprehensive approach is the difference between sustained cost reduction and a one-time fix. The Komprise approach:

• Visibility before spend — Komprise identifies exactly how much data is cold and sitting on expensive primary storage across all silos, with projected 3-year savings models, before any data is moved or any hardware is purchased
• Transparent tiering across any destination — Komprise Flash Stretch uses intelligent tiering to move cold data off expensive flash NAS to lower-cost cloud or object storage transparently, reclaiming 70%+ of primary storage capacity without disrupting users, applications, or AI workflows
• Backup and DR cost reduction — because Komprise moves entire files off primary storage, backup footprints shrink immediately, cutting backup licensing, DR replication, and disaster recovery costs alongside storage hardware costs
• No rehydration penalty — unlike storage-vendor tiering that triggers costly recalls during antivirus scans, hardware refreshes, or vendor migrations, Komprise Transparent Move Technology uses Dynamic Links that eliminate rehydration entirely and preserve full data portability across any future storage platform
• Proven enterprise results — Pfizer reduced storage and cloud costs by 70 to 75% using Komprise intelligent tiering; a major hospital in the southeast is saving $2.5M per year by tiering cold files from on-premises storage to the cloud; these results span storage hardware, backup, DR, and cloud costs simultaneously because Komprise manages all layers from a single unified platform

Next steps?

Schedule a demonstration to find out how Komprise does against these five requirements for delivering a unified control plane for what Gartner now calls Data Storage Management Services (DSMS).