Unstructured data must be systematically managed
Roughly 80% of it is unstructured—emails, videos, CAD files, social media posts, scanned documents. It’s scattered across systems, often inaccessible, and difficult to analyze. That’s a problem. If data isn’t structured properly, it’s worthless. Worse, it slows down decision-making, increases storage costs, and limits the impact of AI and automation efforts.
The scale of the issue is massive. Every day, businesses contribute to the 402.7 million terabytes of data generated worldwide, with much of it going completely unused. Many executives don’t even know how much of their company’s data remains in the dark—untapped, unknown, and entirely disconnected from strategic planning. If AI is going to shape the future of business, relying on just 20% of structured data won’t cut it.
Leadership must treat unstructured data as an asset and actively build a framework to extract value from it. That means defining a clear method for sorting, classifying, and integrating this information into core business processes. It doesn’t have to be complex, but it does have to be deliberate. Without a structured approach, businesses remain trapped in inefficiencies, unable to make full use of their own intelligence.
Splunk’s survey of 1,300 business and IT leaders reflects this reality: more than half of enterprise data is dark, meaning companies don’t know where it is, what it contains, or how to use it. That’s a ridiculous waste of potential. Organizations that solve this problem first will be the ones shaping the future.
Start with a thorough analysis
The first step in taking control of unstructured data is understanding exactly what you’re dealing with. This means breaking down where the data comes from, how much of it exists, where it’s stored, and who actually uses it. Without this foundational knowledge, any attempt at structuring data will be inefficient. Organizations need visibility before they can implement meaningful solutions.
Most enterprises don’t have an accurate picture of their unstructured data environment. Large volumes of data are created daily—emails, video files, network logs, and documents—but in many cases, they are stored without strategy, consuming unnecessary resources. Leaders should demand a full assessment, measuring storage costs, analyzing usage patterns, and identifying data owners.
A comprehensive audit sets the stage for smarter decision-making. It helps determine which data is critical, which departments rely on it, and how it can be better integrated across operations. IT teams and executives must collaborate to make sure this assessment goes beyond technical concerns and aligns with overall business strategy. Data is an enterprise-wide asset, and treating it as such ensures it contributes to growth, rather than becoming an operational burden.
Businesses generating unstructured data at high velocities need ongoing monitoring—automated tracking systems that continuously analyze data volume and usage. By investing in this level of oversight, enterprises reduce inefficiencies, improve compliance, and increase the strategic value of their data assets. Understanding your data is the first real step toward making it work for you.
Identify data silos
Unstructured data is often isolated within specific departments, making it inaccessible to the rest of the organization. This fragmentation creates inefficiencies—critical insights are locked away in separate systems, preventing their use for broader business goals. When different teams base decisions on incomplete or conflicting data, alignment suffers, and opportunities are missed. This is why identifying and addressing data silos must be a priority.
Many organizations operate with independent data environments—whether in finance, marketing, operations, or engineering—each managing its own information without full visibility across teams. Some of this data could provide strategic value to multiple departments, yet without integration, it remains underutilized. Unstructured data in particular, such as customer feedback in emails or product testing videos, may contain insights that could drive company-wide improvements, but only if made accessible.
Breaking down silos requires direct action: identifying where critical unstructured data resides, determining who controls access to it, and implementing systems that allow for broader integration. This is an enterprise-wide challenge. Leadership must create accountability by ensuring data policies support collaboration while maintaining security and compliance.
Beyond increasing operational efficiency, eliminating data silos reduces business risk. When different departments rely on separate and sometimes conflicting information, inconsistencies emerge in decision-making, which can lead to costly miscalculations. Businesses that successfully unify their unstructured data eliminate blind spots, improve agility, and create a stronger foundation for AI-driven insights.
Review and optimize data retention policies
Not all data is worth keeping. Enterprises store massive amounts of unstructured data, much of which holds no real business value. This includes outdated documents, redundant files, and meaningless system-generated data. Without a structured approach to data retention, companies waste storage space, increase costs, and slow down data management processes. A clear retention strategy makes sure only valuable data is preserved while the rest is eliminated.
Unstructured data accumulates quickly. Network logs, old emails, obsolete reports, and legacy hardcopy documents often remain stored long past their usefulness. IT teams must work closely with business leaders to assess which data contributes to operations and decision-making, and which data is no longer relevant. This requires a review of internal and cloud storage systems, identifying data that should be permanently archived, deleted, or consolidated.
Beyond storage concerns, managing retention policies is also about improving data quality. When businesses allow irrelevant data to build up, it makes searches inefficient, increases compliance risks, and reduces the accuracy of analytics. Financially, unnecessary data retention adds up in operational costs—whether through cloud storage fees, internal infrastructure costs, or time wasted navigating outdated information.
Optimizing retention policies is an ongoing effort. Businesses must regularly assess storage needs, update policies, and enforce data reviews to prevent unnecessary accumulation.
“Taking control of unstructured data retention is all about making sure the right information is accessible, manageable, and aligned with business goals.”
Tag and enrich unstructured data
Unstructured data is only useful if it can be efficiently identified, retrieved, and utilized. Without proper classification, organizations struggle to integrate it with structured datasets, limiting its impact on decision-making. Applying systematic data tagging makes sure information is categorized in a way that allows teams to locate and leverage it effectively. This process is essential for improving searchability, data governance, and overall usability.
Many organizations still rely on manual tagging, where subject-matter experts apply labels to unstructured data objects. This is a time-intensive process, but necessary to establish consistency in how data is classified. Standardizing these tags across departments make sure data remains accessible and logically organized rather than fragmented by differing taxonomies. Investment in automated tagging solutions can reduce labor costs and improve scalability, particularly as AI-powered tools advance in their ability to classify and organize data based on predefined rules.
Beyond tagging, data enrichment enhances how unstructured data interacts with structured datasets. The process involves cleaning, formatting, and normalizing data so that it can be integrated into cohesive repositories. Tools like ETL (extract-transform-load) systems automate much of this standardization, but IT teams must define the transformation rules to align with business objectives. Additionally, external datasets, such as industry benchmarks, regulatory data, or third-party analytics, can further enhance the value of enriched information.
The ability to efficiently classify, structure, and enrich unstructured data leads to better insights, faster decision-making, and improved operational efficiency. Organizations that prioritize this process will be better positioned to take full advantage of AI, automation, and business intelligence initiatives. Structured data has long been the foundation of enterprise decision-making, but it’s those who master unstructured data that will define the next generation of competitive advantage.
Key executive takeaways
- Unstructured data is a business risk and an untapped asset: Most enterprise data is unstructured and often goes unused, increasing costs and limiting AI potential. Leaders must establish structured data management to unlock strategic value.
- A clear data audit is the first step to gaining control: Organizations need full visibility into where unstructured data comes from, how it’s stored, and who uses it. Regular audits help refine data strategy, reduce inefficiencies, and improve governance.
- Breaking down data silos enables better decision-making: When unstructured data is isolated in departmental silos, businesses lose valuable insights. Leaders should push for cross-functional data access to improve collaboration and data utilization.
- Inefficient data retention adds cost and creates risk: Unchecked data accumulation wastes resources and complicates compliance. Companies must enforce retention policies that eliminate obsolete information while preserving critical data.
- Tagging and enriching data drives AI and business intelligence: Without classification, unstructured data remains difficult to use. Investing in automated tagging and enrichment processes makes data more accessible, actionable, and AI-ready.
