Unstructured data has become a major part of the information enterprises create, store, and use. Documents, emails, images, videos, audio files, customer communications, presentations, logs, and other content that does not follow a predefined database structure make up a large portion of modern enterprise information. Industry estimates commonly place the share of enterprise data that is unstructured at around 80% to 90%, although the exact proportion varies depending on how organizations define and measure unstructured data. IBM estimates that unstructured data accounts for about 90% of enterprise-generated data.
The amount of unstructured data is also expanding as businesses adopt cloud applications, collaboration platforms, digital communication, connected systems, multimedia content, and artificial intelligence. The challenge is no longer limited to finding enough storage capacity. Organizations increasingly need to understand where unstructured data resides, identify sensitive information, control access, classify content, manage its lifecycle, and make useful data available for analytics and AI.
Recent enterprise research illustrates the scale of this change. Komprise’s 2026 State of Unstructured Data Management survey of 300 enterprise storage and IT leaders found that 74% of respondents manage more than 5 petabytes of unstructured data, while 40% manage at least 10 petabytes. The research also found that 85% expect their data-storage spending to increase in 2026.
Artificial intelligence is making unstructured data even more important. BARC’s 2026 research, based on 225 responses from data, analytics, and AI leaders in North America and Europe, found that only 29% of respondents fully know where their AI-relevant unstructured data resides, while 70% say less than half of it is discoverable and usable for analytics or AI.
This article brings together 127 unstructured data statistics for 2026, covering data growth, enterprise adoption, storage, management, artificial intelligence, analytics, security, governance, costs, and industry trends. The statistics are organized by topic to show how organizations are creating, storing, protecting, managing, and extracting value from unstructured data.
Key Unstructured Data Statistics
The following statistics highlight the scale of unstructured data and the growing challenges organizations face in storing, managing, securing, and putting this information to use.
1. Industry estimates suggest that 80% to 90% of enterprise data is unstructured, although the exact percentage varies by definition and measurement methodology.
2. IBM estimates that approximately 90% of enterprise-generated data is unstructured.
3. A 2026 Cloud Security Alliance study found that unstructured data represents approximately 33% of the data held by surveyed organizations, based on respondents’ own estimates.
4. 29% of organizations surveyed by the Cloud Security Alliance say unstructured data accounts for the majority of their annual data growth.
5. 74% of enterprises surveyed by Komprise manage more than 5 PB of unstructured data.
6. 40% of enterprises surveyed by Komprise manage at least 10 PB of unstructured data.
7. 85% of surveyed organizations expect their data-storage spending to increase in 2026.
8. 56% of organizations surveyed by the Cloud Security Alliance report only partial visibility into where their data is stored.
9. 68% of organizations report that less than 80% of their unstructured data is protected.
10. Only 29% of organizations surveyed by BARC say they fully know where their AI-relevant unstructured data resides.
11. 70% of BARC respondents say less than half of their unstructured data is discoverable and usable for analytics or AI.
12. 79% of BARC respondents are confident that they can extract value from unstructured data without violating governance controls.
13. 94% of enterprises surveyed by Nasuni report challenges managing unstructured data.
14. 90% of organizations surveyed by Nasuni report barriers to scaling AI initiatives.
15. 46% of organizations say their AI initiatives have exposed problems related to data quality or governance.
These figures show that the unstructured data challenge has several dimensions. The issue is not simply that organizations are generating more files and content. Enterprises must also establish visibility across distributed repositories, protect sensitive information, manage increasingly large storage environments, and determine which unstructured information can be used effectively for analytics and AI.
The statistics also reveal why unstructured data has become closely connected with AI strategy. Large volumes of documents, conversations, images, video, and other content contain information that organizations may want to use with modern AI systems. However, data that cannot be located, classified, governed, or accessed reliably is difficult to turn into useful AI inputs.
Unstructured Data Growth Statistics
The volume of unstructured data continues to grow as organizations generate more digital documents, communications, multimedia content, application data, and machine-generated information. Cloud adoption, connected systems, and artificial intelligence are adding further sources of data, increasing the amount of information enterprises need to store, process, and manage.
16. Global data creation and replication is projected to reach approximately 394 zettabytes by 2028.
17. Enterprise data creation is expected to continue growing substantially as organizations expand their use of cloud services, connected systems, analytics, and artificial intelligence.
18. The proportion of enterprises managing more than 5 PB of unstructured data increased by 57% compared with 2024.
19. 29% of organizations say unstructured data represents the majority of their annual data growth.
20. 85% of organizations expect their data-storage spending to increase in 2026.
21. 38% of organizations expect their storage costs to increase by more than 20% in 2026.
22. 57% of organizations identify managing data growth as a significant challenge for their storage environments.
23. 61% of organizations identify data classification and tagging as an important future requirement for managing their unstructured data.
24. 60% of organizations identify analytics and reporting as an important future requirement for their unstructured data environments.
25. 57% of organizations identify sensitive-data detection as an important future requirement.
As unstructured data volumes increase, organizations face challenges beyond simply adding storage capacity. More information means more content to classify, protect, retain, move, analyze, and eventually delete. The growth of AI is also increasing the importance of previously underused documents, images, conversations, and other unstructured information.
This shift is turning unstructured data growth into a broader data-management challenge. Enterprises increasingly need to understand what information they have, where it resides, how quickly it is growing, and which data can provide business value.
Enterprise Unstructured Data Statistics
Unstructured data has become a core part of the enterprise data environment, but organizations are managing it across distributed file systems, cloud platforms, collaboration tools, and other repositories. As enterprises increase their use of AI, the quality, accessibility, and governance of this data are becoming increasingly important to business operations and technology investments.
26. 97% of enterprises surveyed by Nasuni have deployed or are piloting AI agents.
27. 57% of AI projects surveyed by Nasuni are not reported to be delivering their intended objectives.
28. Only 16% of enterprises currently rank unstructured data management as a core IT investment priority.
29. 60% of enterprises plan to invest in unstructured data management during the next 18 months.
30. 59% of IT leaders identify AI as their number-one investment priority in Nasuni’s 2026 research.
31. 79% of organizations report inconsistent file access and performance across locations.
32. Only 21% of organizations have a single, centrally managed environment providing predictable, high-performance global file access.
33. 35% of businesses say slow or unreliable file access negatively affects employee productivity.
34. 46% of organizations report that AI has revealed gaps in their data quality or governance.
35. 90% of organizations face challenges scaling AI, with data security, data trust, and integration among the major barriers.
The enterprise challenge is increasingly connected to how data is accessed and used rather than simply how much data is stored. Organizations may have substantial amounts of valuable information distributed across locations and systems, but inconsistent access and fragmented environments can make that information harder to use efficiently.
The shift toward AI is also changing the role of unstructured data management. As enterprises move AI projects from experimentation toward broader deployment, proprietary documents, operational files, customer information, and other enterprise content become increasingly important inputs. This is pushing organizations to reconsider unstructured data as an operational and strategic asset rather than treating it solely as a storage problem.
Unstructured Data Storage Statistics
The rapid growth of unstructured data is increasing pressure on enterprise storage environments. Organizations must accommodate larger volumes of documents, media, backups, archives, and application-generated content while controlling storage costs and maintaining access to information. Storage decisions are also becoming more complex as enterprises distribute data across on-premises infrastructure, private and public clouds, and specialized storage systems.
36. 55% of organizations spend more than 30% of their IT budget on data storage.
37. 74% of enterprises manage more than 5 PB of unstructured data.
38. 40% of enterprises manage at least 10 PB of unstructured data.
39. 38% of organizations expect storage costs to increase by more than 20% during 2026.
40. 32% of organizations use 11 or more tools to manage their unstructured data.
41. 12% of organizations use at least 21 tools for unstructured data management.
42. 68% of organizations report that less than 80% of their unstructured data is protected.
43. 56% of organizations have only partial visibility into where their data is stored.
44. 35% of organizations report having full visibility into their unstructured data environment.
45. 75% of organizations express moderate or high confidence in their ability to secure unstructured data.
The storage challenge is therefore not limited to capacity. As data estates become larger and more distributed, organizations also need to determine where information should reside, how long it should be retained, which data requires higher levels of protection, and when older information can be archived or removed.
The growing number of tools used to manage unstructured data can add another layer of complexity. Enterprises may use separate systems for storage, backup, security, classification, governance, migration, and analytics. As unstructured data continues to expand, reducing unnecessary duplication and improving visibility across these environments can become increasingly important for controlling infrastructure costs.
Unstructured Data Management Statistics
Managing unstructured data is becoming more complex as enterprises accumulate information across file systems, cloud platforms, collaboration applications, and other repositories. The challenge is not only finding enough storage capacity but also classifying data, controlling its lifecycle, moving it without disrupting users, and preparing it for analytics and AI.
46. 64% of organizations identify cost optimization as a top data-storage priority for the next year.
47. 61% of organizations rank data preparation and classification for AI among their top storage priorities.
48. 54% of organizations identify cloud migration as a top data-storage priority.
49. 58% of organizations identify classifying data for AI as a top technical challenge in unstructured data management.
50. 53% of organizations identify moving data without disrupting users or applications as a major technical challenge.
51. 51% of organizations identify data lifecycle management as a leading priority for unstructured data environments.
52. 64% of organizations plan to upgrade their data-storage and data-management platforms to address security and AI requirements.
53. 62% of organizations identify AI data management as their most significant skills gap.
54. 60% of organizations identify cloud-storage strategy as a significant skills gap.
55. 49% of organizations identify data security and compliance as a significant skills gap.
56. 58% of organizations are creating an internal task force involving IT, security, legal, and other functions to develop an AI strategy.
57. 53% of organizations plan to add IT infrastructure leaders focused on developing their AI foundation.
58. 49% of organizations plan to hire engineers and developers with AI expertise.
Effective unstructured data management increasingly requires organizations to connect storage decisions with data governance and business use cases. Classification can help identify which information is sensitive, redundant, obsolete, or valuable for AI, while lifecycle management can help determine where data should reside as its business value changes.
AI is also changing the skills organizations need. Traditional storage expertise is increasingly being supplemented by capabilities in data classification, governance, security, cloud storage, and AI data preparation. This reflects a broader shift in which unstructured data management is becoming part of the organization’s overall data and AI strategy rather than remaining solely an infrastructure responsibility.
Unstructured Data and AI Statistics
Artificial intelligence is increasing the value of unstructured data because many enterprise AI applications depend on documents, conversations, images, files, and other content that is not stored in traditional structured databases. At the same time, AI introduces new requirements for data classification, governance, security, and preparation. The following statistics focus specifically on how enterprises are connecting unstructured data with AI.
59. 62% of organizations identify reducing data risk from AI as the top business challenge in unstructured data management.
60. 54% of IT leaders identify AI data governance as a core concern, up from 29% in 2024.
61. 56% of organizations identify classifying and tagging unstructured data as the leading challenge when preparing it for AI.
62. 46% of organizations identify data governance and security concerns as another major challenge when preparing unstructured data for AI.
63. 58% of organizations identify classifying data for AI as a leading technical challenge in unstructured data management.
64. 40% of organizations plan to increase their IT budgets to support AI in 2026.
65. 64% of organizations plan to upgrade their data-storage and data-management platforms to meet security and AI requirements.
66. 14% of organizations restrict the use of AI within their workforce, indicating that most surveyed organizations have not imposed broad restrictions on employee AI use.
67. 46% of organizations identify corporate data leakage as their greatest data concern related to generative AI.
68. 58% of organizations are creating an internal task force involving IT, security, legal, and other teams to develop an AI strategy.
69. 53% of organizations plan to add IT infrastructure leaders focused on developing their AI foundation.
70. 49% of organizations plan to hire engineers and developers with AI expertise.
71. 35% of organizations cannot trace how their unstructured data is being used across systems.
72. Roughly one-third of organizations report deficiencies in areas such as data bias and data lineage when preparing unstructured data for AI.
The statistics show that AI is changing the role of unstructured data from passive enterprise content into an increasingly important input for AI systems. However, organizations still need to establish the underlying data foundations before they can reliably use this information at scale. Classification, governance, security, lineage, and visibility are becoming closely connected to AI readiness.
The investment response is also becoming more visible. Enterprises are increasing spending on AI, upgrading storage and data-management platforms, and creating cross-functional teams to address AI strategy and governance. This suggests that preparing unstructured data for AI is increasingly being treated as an enterprise infrastructure and governance issue rather than simply an AI-model problem.
Unstructured Data Analytics Statistics
Unstructured data contains information that can be highly valuable for analytics but is often difficult to use with conventional analytical systems. Documents, emails, customer conversations, images, audio, video, and other content can contain business insights that are not captured in structured databases. Organizations are therefore increasingly using classification, metadata, search, natural language processing, and AI to make this information discoverable and analyzable.
73. 70% of organizations surveyed by BARC say that less than half of their unstructured data is discoverable and usable for analytics or AI.
74. 35% of organizations say they cannot trace how their unstructured data is being used across systems.
75. Roughly one-third of organizations report deficiencies in data lineage and data bias when preparing unstructured data for AI and analytics.
76. 60% of organizations identify analytics and reporting as a future requirement for their unstructured data management environments.
77. 79% of organizations express confidence that they can extract value from unstructured data without breaking governance controls.
78. 69% of users in a separate BARC data-fabric study report high or very high benefits for data accessibility from their data-fabric tools.
79. 68% of data-fabric users report high or very high benefits for data control and data trust.
80. 36% of data-fabric users report little or no benefit from their current tools when it comes to preparing data for AI.
The gap between the amount of unstructured information organizations possess and the amount they can actually discover and analyze remains significant. Data may exist across file systems, cloud repositories, collaboration platforms, and business applications without consistent metadata or lineage, making it difficult for analytics teams to locate relevant information and determine whether it can be trusted.
This is also why unstructured data analytics is increasingly connected to data management and AI readiness. Making content searchable is only one step; organizations also need to understand its context, provenance, quality, sensitivity, and relationships with other data before using it for advanced analytics or AI applications.
Unstructured Data Security Statistics
Unstructured data can contain sensitive information such as personal data, financial records, intellectual property, legal documents, credentials, and confidential business information. Because this information is often distributed across cloud applications, file systems, collaboration platforms, and other repositories, maintaining consistent security controls can be difficult as data volumes increase.
81. 74% of organizations identify security as a top concern when managing unstructured data.
82. 57% of organizations identify governance as a top concern for their unstructured data.
83. 54% of organizations identify privacy as a major concern surrounding unstructured data.
84. 50% of organizations identify compliance as a major concern for unstructured data environments.
85. 47% of organizations identify advanced AI-driven threats as the leading security risk to unstructured data in 2026.
86. Only 9% of organizations report having real-time scanning capabilities for unstructured data.
87. 23% of organizations cannot scan their unstructured data for security risks.
88. 36% of organizations identify a lack of automation as a challenge when scaling unstructured data security.
89. 10% of organizations report having no sensitivity labeling for their unstructured data.
90. Documents and files account for 73% of the unstructured data volume reported by surveyed organizations.
91. Communication data accounts for 62% of reported unstructured data volume.
92. Logs account for 43% of reported unstructured data volume.
93. 40% of organizations plan to use AI for threat detection and security-workflow automation.
94. 37% of organizations plan to use AI for unstructured-data classification, labeling, discovery, and inventory.
The security challenge is closely connected to visibility and classification. Organizations may have strong confidence in their overall security posture while still lacking complete knowledge of where sensitive unstructured information resides or whether it is consistently protected. Limited scanning and inconsistent labeling can make it harder to identify risky information before it becomes exposed.
AI is also creating a dual role in unstructured data security. Organizations expect to use AI for detection, classification, and automation, while simultaneously identifying AI-driven threats as a major security concern. This makes foundational controls such as data inventory, classification, access management, continuous scanning, and governance increasingly important as enterprise AI adoption expands.
Unstructured Data Governance Statistics
As unstructured data spreads across file systems, cloud repositories, collaboration platforms, and AI applications, governance becomes increasingly important. Organizations need to know what information they hold, who can access it, how long it should be retained, and whether it is subject to privacy or regulatory requirements. AI is making these questions more urgent because previously overlooked documents and files can become inputs to AI systems.
95. 57% of organizations identify governance as a top concern when managing unstructured data.
96. 54% of organizations identify privacy as a top concern surrounding their unstructured data.
97. 50% of organizations identify compliance as a top concern for unstructured data.
98. 56% of organizations have only partial visibility into where their data is stored.
99. 35% of organizations report having full visibility into their unstructured data environment.
100. 10% of organizations are unsure how much of their unstructured data is actually protected.
101. 62% of organizations identify data encryption as one of the security tools used to manage unstructured data.
102. 60% of organizations use cloud-security tools as part of their unstructured data security approach.
103. 59% of organizations use application-security tools to protect unstructured data.
104. 56% of organizations use identity and access-management tools as part of their unstructured data security approach.
105. 58% of organizations are creating an internal task force involving IT, security, legal, and other teams to develop an AI strategy.
106. 61% of organizations identify data classification and tagging as a future requirement for unstructured data management.
107. 57% of organizations identify sensitive-data detection as a future requirement for managing unstructured data.
108. 47% of organizations are concerned about departments lacking visibility into storage spending and data use.
Strong governance depends on foundational visibility and control. The research shows that organizations recognize governance, privacy, and compliance as major concerns, while many still lack complete visibility into where their unstructured data resides. Security tools such as encryption, cloud security, application security, and identity management are widely used, but organizations are also looking toward classification and sensitive-data detection to strengthen governance as their data environments expand.
The connection between governance and AI is particularly important. Organizations are creating cross-functional teams that bring together IT, security, legal, and other stakeholders because AI can introduce new ways for enterprise information to be accessed and processed. Effective governance therefore increasingly needs to cover not only where data is stored but also how it is classified, who can use it, and how it flows into AI systems.
Unstructured Data Cost Statistics
The financial impact of unstructured data extends beyond the price of storage hardware. Enterprises also incur costs for backups, disaster recovery, data movement, infrastructure upgrades, and the people and tools required to manage growing data estates. As organizations retain more information for longer periods, controlling the cost of that data becomes an increasingly important part of storage strategy.
109. 64% of organizations identify cost optimization as their top data-storage priority for the next year.
110. 85% of organizations expect to increase their data-storage spending in 2026.
111. 40% of organizations plan to increase their IT budgets to support AI initiatives in 2026.
112. 47% of organizations are concerned about departments lacking visibility into storage spending and data use.
113. 54% of organizations identify cloud migration as one of their top data-storage priorities for the coming year.
114. 64% of organizations plan to upgrade their data-storage and data-management platforms to address security and AI requirements.
115. 38% of organizations expect their storage costs to increase by more than 20% in 2026.
The financial pressure is closely connected to data growth. When organizations retain increasingly large volumes of unstructured information, they must account not only for primary storage but also for backup, replication, disaster recovery, data movement, and management. These additional copies and services can substantially increase the effective cost of retaining enterprise data.
Cost optimization is therefore becoming less about simply negotiating lower storage prices and more about understanding the data itself. Identifying inactive, duplicate, obsolete, or low-value information can help organizations determine which data needs high-performance storage and which information can be moved to lower-cost tiers or eventually removed.
Unstructured Data Trends for 2026
Unstructured data management is moving beyond traditional storage as organizations prepare their data environments for artificial intelligence, stronger security controls, and increasingly distributed infrastructure. The major trends for 2026 center on making unstructured information more discoverable, usable, secure, and cost-efficient.
116. 97% of enterprises surveyed by Nasuni have deployed or are piloting AI agents, increasing the importance of reliable access to enterprise data.
117. 59% of IT leaders identify AI as their number-one investment priority for 2026.
118. 60% of enterprises plan to invest in unstructured data management over the next 18 months.
119. 64% of organizations plan to upgrade their data-storage and management platforms to support security and AI requirements.
120. 40% of organizations plan to increase their IT budgets to support AI initiatives in 2026.
121. 53% of organizations plan to add IT infrastructure leaders focused on developing their AI foundation.
122. 49% of organizations plan to hire engineers and developers with AI expertise.
123. 40% of organizations plan to use AI for threat detection and security-workflow automation.
124. 37% of organizations plan to use AI for unstructured-data classification, labeling, discovery, and inventory.
125. 61% of organizations identify data classification and tagging as a future requirement for unstructured data management.
126. 57% of organizations identify sensitive-data detection as a future requirement.
127. 60% of organizations identify analytics and reporting as a future requirement for their unstructured data environments.
The direction is clear: unstructured data management is becoming increasingly connected to AI infrastructure and enterprise data strategy. Organizations are investing not only in additional storage but also in classification, discovery, security automation, analytics, and the skills required to make large collections of unstructured information usable.
AI is also creating a feedback loop. Organizations need better access to unstructured data to support AI applications, while AI itself is increasingly being used to classify, discover, analyze, and protect that data. This makes the quality of the underlying data environment increasingly important as enterprises move from AI experimentation toward broader deployment.
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Explore Insights →Key Takeaways
The statistics in this article show that unstructured data is becoming a larger and more strategically important part of enterprise data environments. Organizations are managing multi-petabyte data estates while dealing with rising storage costs, fragmented visibility, security requirements, governance challenges, and growing demand to make unstructured information usable for AI and analytics.
The biggest shift is that enterprises are no longer treating unstructured data solely as a storage problem. Documents, emails, images, video, audio, communications, and other content can contain information that supports business intelligence and AI applications. As a result, organizations are investing in classification, discovery, governance, security, analytics, and AI-ready data infrastructure alongside storage capacity.
The research also highlights a significant gap between having data and being able to use it effectively. Large quantities of unstructured information may remain difficult to discover, classify, protect, or analyze. Improving visibility and establishing reliable governance will therefore remain important as organizations continue expanding their AI and analytics initiatives.
Sources
This article was compiled using data and research from organizations and industry research firms including IBM, IDC, Komprise, Cloud Security Alliance, BARC, and Nasuni, along with the reports and studies published by these organizations on unstructured data, enterprise storage, data management, analytics, security, and artificial intelligence.
The statistics reflect the methodologies, respondent groups, and reporting periods used by the respective sources. Where multiple organizations have measured similar aspects of unstructured data, their findings have been treated as separate research rather than combined into a single estimate.
Frequently Asked Questions
What is unstructured data?
Unstructured data is information that does not follow a predefined tabular or relational database structure. Common examples include documents, emails, images, videos, audio recordings, presentations, social media content, customer conversations, and many types of machine-generated files.
How much enterprise data is unstructured?
Industry estimates commonly place unstructured data at around 80% to 90% of enterprise data, although the exact proportion varies depending on how unstructured data is defined and measured.
What are examples of unstructured data?
Common examples include emails, PDFs, Word documents, presentations, images, videos, audio files, social media posts, customer conversations, medical records, contracts, and scanned documents.
Why is unstructured data growing so quickly?
The growth is being driven by cloud applications, digital communication, multimedia content, connected devices, enterprise collaboration tools, application-generated files, and artificial intelligence. Organizations are also retaining more information for analytics, compliance, and potential AI use.
Why is unstructured data important for AI?
Many AI applications rely on information contained in documents, conversations, images, audio, and other unstructured content. Making this information discoverable, accessible, governed, and usable can provide AI systems with access to more of an organization’s proprietary knowledge.
What are the biggest challenges of managing unstructured data?
Major challenges include data growth, storage costs, limited visibility, security, privacy, compliance, classification, data lifecycle management, fragmented tools, and preparing data for analytics and AI.
How does unstructured data affect storage costs?
Large volumes of files, multimedia content, backups, archives, and replicated data can increase storage requirements. Organizations may also incur additional costs for backup, disaster recovery, data migration, data management, and maintaining multiple copies of information.
How can organizations manage unstructured data?
Organizations can use data discovery, classification, metadata management, lifecycle policies, access controls, encryption, retention policies, deduplication, storage tiering, centralized governance, and analytics tools to manage unstructured information more effectively.
What is unstructured data management?
Unstructured data management involves identifying, organizing, storing, protecting, governing, analyzing, and eventually archiving or deleting information that does not fit neatly into traditional structured databases.
What is the difference between structured and unstructured data?
Structured data follows a predefined format, such as rows and columns in a relational database. Unstructured data does not follow a fixed database schema and includes content such as documents, images, video, audio, and emails.
Is unstructured data useful for analytics?
Yes. Unstructured data can contain valuable information about customers, operations, products, employees, transactions, and business processes. Organizations increasingly use search, natural language processing, machine learning, and AI to extract insights from this information.
What is the future of unstructured data?
The future of unstructured data management is increasingly connected to AI, automated classification, intelligent search, data governance, security automation, analytics, and lifecycle management. As enterprise data volumes grow, organizations will need better ways to identify valuable information and make it usable while maintaining appropriate security and governance controls.