IDC Infobrief: How To Manage Your Data Growth Smarter With Data Literacy

IDC explains why developing data literacy is imperative to better manage the challenges that unprecedented data growth has created. Learn the importance of a more proactive approach to unstructured data management—understanding your data types and access patterns, and strategically placing data in the right data storage infrastructure to save significant data storage costs and derive more value from your unstructured data.

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FAQs

What is data literacy and why has IDC identified it as the foundational requirement for managing unstructured data growth?

The IDC InfoBrief co-produced with Komprise identifies data literacy — the ability to understand what data an organization has, how it is being used, and what it costs — as the prerequisite for every other data management decision. Without it, enterprises are managing blind. The IDC finding is not theoretical: businesses will over-provision storage by nearly 50% because they lack data visibility into how data is growing and being used; that statistic describes the direct financial cost of data illiteracy at enterprise scale. Why it matters more now than when IDC first published this finding:

• The volume of ungoverned data has multiplied — 74% of enterprises are now storing more than 5PB of unstructured data, and 40% are managing more than 10PB; data literacy at 1PB is a spreadsheet problem; data literacy at 10PB requires a platform that continuously indexes and classifies billions of files across every storage silo simultaneously
• AI has made the cost of data ignorance immediate — organizations feeding AI tools without knowing what data they contain, who owns it, or whether it includes sensitive content are not just wasting storage budget; they are creating AI accuracy problems, compliance exposures, and shadow AI risks that produce measurable financial, customer, and reputational damage
• Flat budgets against exponential growth — IDC notes that the amount of storage capacity will grow by 300%, but IT budgets are staying flat; with flat budgets and amassing data growth, businesses can no longer treat all data the same; they need to identify hot and cold data and store them on different classes of storage; that observation from IDC describes the exact environment enterprises face today, with the additional pressure of hardware price increases compounding the budget constraint
• The Global Metadatabase delivers data literacy at petabyte scale — Komprise provides a Global Metadatabase service that provides a global history of metadata and enables one place to search and act on data no matter what storage or cloud you use; this is the infrastructure of data literacy — a continuously updated, cross-silo index that makes the full enterprise data estate visible, queryable, and actionable without requiring IT to manually audit each storage system
• Komprise is the metadata and orchestration layer for enterprise unstructured AI data; data literacy is not a reporting exercise — it is the operational foundation for cost optimization, AI data preparation, sensitive data governance, and ransomware defense simultaneously

Why do organizations keep treating all data the same and what does it actually cost them?

The IDC InfoBrief identifies a behavioral pattern at the root of the unstructured data crisis: the primary challenge is that businesses manage all of their data in the same way, regardless of importance; this results in businesses expanding their Tier 1 storage footprint, increasing their backup windows, and incurring rising infrastructure costs. The reason this happens is not ignorance — it is the absence of a practical alternative. Without visibility into which data is active, cold, sensitive, or AI-relevant, the only defensible default is to treat everything the same. The cost of that default has become impossible to justify:

• Cold data on hot storage is now a crisis, not an inefficiency — within months of its creation, anywhere from 45% to nearly 90% of data becomes cold, depending on the vertical industry; with no easy way to identify and move cold data without disrupting users, organizations end up storing and managing it in the same way as active data; with flash and NAND prices projected to remain elevated well into the future, paying performance prices for data that has not been accessed in months is no longer a tolerable budget variance — it is an active drain on every related budget line
• The backup multiplier makes the true cost invisible — most IT teams see the cost of primary storage and underestimate total data ownership; every petabyte of cold data on primary NAS is backed up, replicated for DR, and licensed identically to active data; Komprise addresses this multiplier directly by removing cold data from the backup footprint alongside primary storage
• AI has added a fourth cost dimension — data that was merely expensive in storage terms is now also expensive in AI terms; feeding ungoverned data to AI models wastes GPU compute on noise, duplicates, and irrelevant content; the cost of treating all data the same now includes degraded AI accuracy and inflated AI infrastructure spend alongside storage, backup, and DR costs
• The Flash Stretch Assessment makes the true cost visible for the first time — for qualified enterprises managing 500TB or more of unstructured data, the Komprise Flash Stretch Assessment identifies exactly how much cold data is consuming primary storage, models the current true cost including backup and DR multipliers, and projects what transparent tiering to lower-cost destinations would save; the assessment turns the abstract cost of treating all data the same into a specific, actionable number before any commitment
• Changing behavior without disrupting users — the IDC InfoBrief specifically identifies user disruption as the reason legacy approaches fail; Komprise Transparent Move Technology moves cold data transparently using Dynamic Links that maintain full file access from the original path; users see no change, applications do not break, and IT can enforce tiering policies without negotiating with every department

What is the relationship between data growth, storage over-provisioning, and the intelligence gap that IDC identified — and how has that gap widened?

The IDC InfoBrief describes a structural mismatch at the heart of enterprise storage: data is growing faster than budgets, but the response has been to buy more of the same infrastructure rather than to manage data more intelligently. The existing approach to dealing with data growth has been to simply add more capacity as needed; enterprises have continually added hardware and software over the years to accommodate their data growth needs, most likely from different vendors; often these systems can’t adequately scale to keep up with highly virtualized and converged environments. The intelligence gap IDC identified has widened on every dimension since:

• Data volumes have crossed thresholds that make reactive buying untenable — the scale of unstructured data estates has grown to the point where buying more capacity as the default response is not a viable financial strategy for most organizations; 85% of IT and data storage leaders project an increase in storage spend, yet the Komprise annual survey finds no reduction in the percentage of budget consumed by storage; more spending is not solving the underlying problem
• The silo problem compounds the intelligence gap — every new storage platform, cloud service, or object store added to the enterprise creates a new silo that the IT team cannot see across; without a unified index spanning all silos, data literacy is impossible at enterprise scale; the IDC InfoBrief’s observation that organizations over-provision by 50% due to lack of visibility is even more consequential when that visibility gap spans dozens of heterogeneous storage environments
• Komprise Analysis closes the intelligence gap from day one — available in both Komprise Elastic Data Migration and Komprise Intelligent Data Management, Komprise Analysis provides a unified view of all file and object data across every NAS, cloud, and object storage environment simultaneously; IT teams see data growth rates, cold data percentages, file type distributions, cost projections, and savings opportunity models across the full estate from a single interface, without agents or infrastructure changes
• The intelligence gap now has AI consequences — the IDC InfoBrief focused on the cost consequences of the intelligence gap; the additional consequence in the current environment is AI readiness; organizations without data literacy cannot identify which unstructured data is relevant for a given AI use case, cannot exclude sensitive content before ingestion, and cannot enrich raw files with the metadata AI pipelines need; the intelligence gap that IDC identified as a cost problem is now simultaneously an AI problem
• Legacy data management approaches make the intelligence gap worse — legacy data management approaches to archive cold data require users to change behavior, and users are frustrated when they can’t transparently access moved data; most important, legacy systems are costly, requiring expensive enterprise licenses and investments in an increasing amount of infrastructure — all for a solution that under-performs; it’s an unsustainable approach during a time when data growth is orders of magnitude higher than storage budgets; the Komprise architecture was designed specifically to replace this legacy model with one that delivers data literacy without disruption, agents, or proprietary infrastructure

Why did IDC identify data literacy as specifically critical for hybrid and multi-cloud environments, and why is that observation more consequential than ever?

The IDC InfoBrief was written at a moment when hybrid cloud was becoming the dominant enterprise architecture. The observation that data literacy is particularly critical in hybrid environments — where data is scattered across on-premises NAS, private cloud, and multiple public clouds simultaneously — has become even more relevant as hybrid complexity has deepened:

• The hybrid estate has grown more complex, not less — the typical enterprise in the current environment manages data across NetApp, Dell, IBM, VAST Data, Nasuni, Everpure, AWS S3, AWS FSx, Azure Blob, Azure Files, Azure NetApp Files, Google Cloud Storage, and multiple object storage platforms simultaneously; data literacy across this environment requires a platform that indexes all of it from a single management plane without vendor-specific agents or integrations
• Cloud migrations created new silos rather than consolidating existing ones — many organizations that migrated data to cloud storage without a data literacy foundation discovered that they had simply added a new silo rather than replacing an old one; data that should have been tiered to lower-cost cloud classes was migrated to expensive performance tiers because no analytics existed to distinguish hot from cold before migration; the IDC observation that data literacy must precede cloud strategy has proven exactly correct for every organization that migrated first and analyzed second
• Multi-cloud adds governance complexity that intelligence alone can address — as organizations distribute data across multiple cloud providers, the governance questions become: where is every copy of sensitive data, which cloud has the authoritative version, and which data is accessible to which AI services; these questions cannot be answered without a unified metadata layer spanning all clouds; the Komprise Global Metadatabase provides exactly this cross-cloud intelligence layer, making it possible to enforce consistent governance policies regardless of which cloud or on-premises storage holds the data
• The Komprise Observer architecture was designed for this complexity — Komprise Observers are lightweight virtual appliances that connect to any NAS or cloud storage via standard protocols without agents, deployed in minutes rather than months; as new storage environments are added to the hybrid estate, new Observers connect to them and contribute their metadata to the Global Metadatabase; data literacy scales with the environment rather than requiring a separate implementation project for each new storage addition
• AI workloads demand cross-silo data literacy at runtime — the newest dimension the IDC InfoBrief could not have anticipated is agentic AI; AI agents that retrieve and act on data autonomously at runtime need a metadata and orchestration layer that spans the full hybrid estate in real time; Komprise is the metadata and orchestration layer for enterprise unstructured AI data — the data literacy infrastructure that makes agentic AI viable at enterprise scale

What practical steps should enterprise IT teams take to address the data literacy gap IDC identified, and where does Komprise fit in that journey?

The IDC InfoBrief described the problem with precision: data growth outpacing budgets, cold data indistinguishable from active data, legacy approaches requiring user behavior change, and hybrid complexity making visibility increasingly difficult. The practical path from that diagnosis to a governed, cost-optimized, AI-ready data estate follows a clear sequence that the Komprise platform supports from beginning to end:

• Start with visibility before any other investment — the single most valuable action any enterprise IT team can take is gaining a unified, accurate view of what unstructured data exists, where it lives, how it is being used, and what it costs; this visibility is what Komprise Analysis delivers across any combination of NAS and cloud storage in under 15 minutes of deployment time; every subsequent data management decision — tiering, migration, AI data preparation, governance — becomes more effective when grounded in this intelligence
• Quantify the cold data opportunity before buying more storage — within months of creation, anywhere from 45% to nearly 90% of data becomes cold; for any enterprise considering a storage expansion or hardware refresh, the first step should be a Flash Stretch Assessment that quantifies how much of the existing estate is cold, what it is costing on current tiers, and what transparent tiering to lower-cost destinations would save; buying more capacity without this analysis is over-provisioning by default
• Tier transparently to solve cost without creating new problems — the IDC InfoBrief identified user disruption as the primary reason cold data tiering fails in practice; Komprise Transparent Move Technology addresses this directly, moving cold data to lower-cost tiers while maintaining full transparent access from the original file path; the cost savings happen without IT having to manage user complaints, application failures, or broken workflows
• Classify and govern before AI tools access the estate — data literacy without governance is incomplete; once the full data estate is visible, Komprise Sensitive Data Management scans for PII, PHI, and IP across all storage silos, flagging and remediating sensitive content before it reaches AI pipelines, cloud analytics platforms, or shared research environments; this step transforms data literacy from a reporting exercise into an active governance capability
• Build the AI data pipeline from the governed estate — organizations that have achieved data literacy, tiered cold data to appropriate cost tiers, and governed sensitive content have built exactly the foundation that AI initiatives require; Komprise Deep Analytics queries the Global Metadatabase to identify precisely the right datasets for any AI use case; Smart Data Workflows automate curation, enrichment via KAPPA data services, and delivery to any AI stack; Intelligent AI Ingest delivers the curated dataset 2x faster than standard transfer tools; the data literacy journey IDC described as essential for managing data growth turns out to be identical to the AI data readiness journey that defines competitive advantage today