Metadata, which is data that describes data, used to be an overlooked component of data in the enterprise. You may think of metadata as system information such as when a file was created, who owns it, how large it is or its file type. You may not have paid much attention to it in the past. My, how times have changed.
In 2025, all signs are pointing to the growing value of metadata. The ability to understand and enrich metadata is becoming a critical knowledge lever that manifests numerous benefits: storage optimization, more resilient security and compliance, enhanced data classification for fast user search, and precise AI data curation.
Metadata is integral to managing unstructured data for AI data pipelines.
First, unstructured data is hard to unlock for AI because of its variety and size; AI tools are now available to synthesize pertinent information hidden in files into useful metadata that adds structure and context. Secondly, AI needs data and feeding it the wrong data can create security issues and false or incorrect results; using metadata to classify and segment data at scale is necessary to glean benefits from AI.
According to the IDC report, “What Every Organization Needs to Know About Unstructured Data,” only half of an organization’s unstructured data is analyzed to extract value while 41% say that less than half of all unstructured data is reused (i.e., accessed more than once after the initial use).
Gartner identified metadata management as a top trend, advising organizations to implement tools that automate finding and analyzing metadata. “The analyst said various metadata types, including technical and business metadata, can then be used for data catalogs, data lineage, and AI-driven use cases.”
For storage and IT managers, understanding and strategically managing metadata is no longer optional. Metadata is now a foundational element for cost optimization and more importantly, competitive advantage vis-à-vis AI. Through 2026, Gartner predicts organizations will abandon 60% of AI projects that aren’t supported by AI-ready data. This is an unnecessary risk that’s possible to mitigate with the right tools and processes.
The Metadata Value Proposition for Data Storage Teams
Automatic metadata from storage systems, while useful for basic operations, is just the start of a strategic metadata management program. The real business value comes from enriching this foundation with metadata that precisely defines data so it can be easily searched and moved as needed to AI tools or other locations as required. With a rich metadata profile on your unstructured data, you can make more accurate decisions about data placement for different needs.
Storage and IT infrastructure and operations managers face several pressures that make comprehensive metadata management essential for delivering value-added data services for the modern enterprise.
Here’s the breakdown:
- Exploding costs are where the rubber hits the road. Organizations struggle with spiraling expenses to manage, protect and make data available for data scientists and business stakeholders running projects. Without proper metadata classification, valuable storage resources get consumed by unknown, unclassified, or obsolete data, while critical business data may be relegated to slower, inappropriate storage tiers. But if you can automatically identify and classify data based on business value, access patterns and project requirements, you can store data in the right place at the right time without wasting precious resources.
- Compliance and PII data governance have evolved from periodic concerns to continuous operational requirements. Nearly half of the states in the U.S. have passed comprehensive privacy laws. Many multinationals must also comply with GDPR and the EU AI Act. This places more stringent requirements on data governance mandates and sensitive data management. Meanwhile, ransomware attacks have not slowed down across the globe. Manual approaches to identifying PII, financial data, healthcare information and other regulated content cannot scale to meet modern compliance requirements. Metadata-driven classification offers automated policy enforcement and audit trail generation.
- AI data governance presents entirely new challenges as organizations start to implement artificial intelligence initiatives. AI projects require the right kind of data, properly classified and available for training and inference. Data scientists need to quickly discover relevant datasets, understand data lineage and ensure compliance with governance requirements.
- Ransomware threats have elevated the need for metadata classification. Organizations need to rapidly identify and protect their most critical data assets from ransomware. Sensitive data detection through metadata tagging for “PII” and other keywords helps find protected data that may be stored in non-compliant locations and secure it properly against cyberattacks.
The Four Tenets of Metadata Management
Modern metadata management solutions are evolving as AI advances and security threats change. What your storage vendor offers is not enough – and manual methods for metadata enrichment cannot scale with the pace of data growth and business needs today. Here are four key tenets to consider:
- Metadatabase for unified visibility: Independent unstructured data management solutions can deliver new insights on all data across distributed hybrid storage environments. This means you can see all your metadata across storage silos, in one place. This allows IT to understand trends like data growth, top file types, costs and usage patterns, which then inform data placement and appropriate governance policies.
- Cost optimization through intelligent data placement: With better knowledge about data, IT can model new data management plans based on current costs. They can automatically tier data to cheaper storage as it ages, achieve better ROI from data migrations and jettison zombie or duplicate data altogether.
- Compliance automation and risk mitigation: The ability to scan file shares across vendors and automatically tag sensitive data types for appropriate action is a game changer. Too often, data gets copied and/or moved to locations where it is not adequately protected based on policies and regulations. IT may never know the risk that lies beneath user actions. With automation, IT can quickly find those misplaced files, tag them and move them to secure locations where they are safe from hackers and cannot be ingested into AI tools.
- AI-powered automated classification and discovery: AI has revolutionized metadata enrichment by eliminating the scalability bottleneck of manual classification. New tools can automatically analyze file contents and generate semantic tags at scale. AI allows organizations to process thousands of files simultaneously with consistent criteria and then feed the results to a data management system for action.
Metadata has evolved from a storage by-product to a high-value asset that supports organizational success across cost optimization, security and risk management and AI initiatives. For storage and IT infrastructure teams now focused on delivering data services, 2025 represents a critical inflection point where advanced metadata management strategies separate high-performing organizations from those struggling to manage growing data volumes and help departments be more productive and competitive.
By Kumar Goswami
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