What is Dark Data? Complete Guide and Best Practices

Right now, 55% of your organization's data is dark—untapped, hidden, or unknown (Splunk Global Survey, 2025). You're spending money to store it. You're exposed if it contains sensitive information. And you have no idea what valuable insights it might hold.

Most companies invest heavily in data infrastructure and AI initiatives, but over half of their data sits unused. That's a problem. Dark data creates regulatory compliance risks, wastes money, and hides business intelligence that could drive better decision-making. As AI becomes critical to staying competitive, you need to understand what data you have and where it lives.

This guide walks you through what dark data is, why it accumulates, the risks it creates, and—most importantly—how to find it, secure it, and transform it into strategic assets. We'll cover practical approaches to data governance, the role of data catalogs in managing unused data, and how to turn hidden information into data products that fuel analytics and AI use cases.

Key Takeaways

• Dark data makes up 55% or more of enterprise data—it's both a risk and an opportunity

• Main causes: data silos, missing metadata, and weak discovery tools

• Risks include security breaches, compliance violations, wasted costs, and missed insights

• Data catalogs and AI-powered tools help you find and classify dark data

• Turn valuable dark data into data products that support AI and business goals

What is dark data?

Dark data is information your organization collects and stores during regular business operations but never uses.

Gartner coined the term, defining it as:

"The information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing). Similar to dark matter in physics, dark data often comprises most organizations' universe of information assets. Thus, organizations often retain dark data for compliance purposes only. Storing and securing data typically incurs more expense (and sometimes greater risk) than value."

Unlike the data flowing through your dashboards and reports, dark data is dormant. Common examples of dark data include old email correspondence, customer service logs, sensor readings, system log files, and unprocessed machine data.

The real problem isn't volume. It's visibility. Without proper metadata, cataloging, and data governance, you don't know what data you have, where it lives, what it contains, or if it holds value. This creates a blind spot in your data analytics capabilities. Data is simultaneously your most valuable asset and your biggest cybersecurity liability.

Dark data examples: What different types exist?

Dark data spans structured, semi-structured, and unstructured formats—each posing unique challenges.

Structured dark data has clear organization—database fields, spreadsheet columns—but remains unused. Examples of dark data in this category include:

• Transaction records in legacy ERP systems that never get queried

• CRM data from discontinued products

• Financial records kept for regulatory compliance but never analyzed for business insights

• Archived employee and HR data

• Server log files with timestamps that accumulate without review

This data is organized and theoretically easy to query. But it becomes dark when teams don't know it exists, can't access it due to permission issues, or have no defined use case for data analytics.

Unstructured dark data represents the fastest-growing segment of unused data. According to IDC StorageSphere Forecast, unstructured data accounts for 78% of all data stored and is forecast to grow from 5.5 zettabytes in 2024 to 10.5 zettabytes by 2028, representing a 16% compound annual growth rate.

Examples of unstructured dark data include:

• Email correspondence with customer feedback and vendor negotiations

• Customer service call recordings

• Security camera video footage

• Social media posts and user-generated content

• Scanned documents, contracts, and paper records

• PowerPoint decks, Word docs, and PDFs

Semi-structured dark data falls in between. It has some organization but no rigid schema:

• JSON and XML files from API interactions

• Web server logs with mixed structured and text fields

• IoT sensor data with varying message formats

• Email metadata paired with message content

• Website and application clickstream data

Each type requires different discovery methods and data governance approaches. Understanding these differences is essential for building an effective strategy to manage and extract value.

What are the main causes of dark data?

Dark data grows for many interconnected reasons, most tied to organizational complexity and gaps in data governance.

Lack of awareness: As data volume explodes across cloud, SaaS tools, and legacy systems, organizations simply lose track of what they collect. Without modern discovery tools or catalogs, data becomes invisible.

Data silos: When departments operate independently, they create isolated data pockets. Marketing databases, sales CRMs, operations data warehouses, and finance systems function as separate islands. Each holds data that could benefit other teams, but it remains trapped within departmental walls.

Missing metadata and documentation: Data without context is effectively invisible. When datasets lack descriptions, business definitions, ownership details, and lineage information, users can't determine if the data is relevant, trustworthy, or safe to use.

Cheap storage and rapid growth: The economics of data storage have changed dramatically. With cloud storage costs dropping and data volumes exploding, companies adopted a "collect everything" mentality, assuming they'd find value later. This approach creates massive data lakes that become data swamps, where information pours in faster than it can be organized.

Complex security controls: Ironically, cybersecurity measures designed to protect data can make it inaccessible. Overly restrictive permissions, poor documentation, and orphaned access rights after employees leave all create barriers. The data exists and might contain valuable insights, but practical accessibility issues render it useless for data analytics.

Legacy systems: Older databases often lack APIs, documentation, or compatible formats. Instead of modernizing or integrating them, organizations leave the data behind—where it becomes dark.

Regulatory retention requirements: Regulations often force long-term data retention. Over years, these archives swell with unused data that still poses risk.

What are the risks of dark data?

Unmanaged dark data creates a complex web of risks that extend far beyond wasted storage costs. These risks compound as dark data volumes increase, creating both immediate liabilities and long-term strategic challenges.

Security and cybersecurity breaches: You cannot protect what you cannot see. Sensitive information—PII, financial data, intellectual property—often hides in forgotten systems. According to IBM’s data breach analysis, organizations continue to face substantial costs and reputational damage from breaches involving unmanaged data (IBM's 2024 Data Breach Roundup). Worse, you might not realize sensitive data was compromised because you didn't know it existed in the first place.

Compliance and regulatory violations: Data privacy regulations (GDPR, CCPA, HIPAA) require organizations to know where personal data lives, how it’s processed, and when it’s deleted. Dark data makes accurate reporting, retention, and subject access requests nearly impossible.

Wasted infrastructure costs: Every byte of dark data consumes storage in data centers, backup capacity, processing power, and management overhead. When dark data represents 55% or more of your total data footprint, it’s possible you’re spending millions on infrastructure for unused data that delivers zero value.

Operational inefficiency: Without visibility into existing data assets, teams unknowingly recreate analyses, duplicate data collection efforts, and make decisions based on incomplete information. Data scientists and analysts spend up to 80% of their time searching for and preparing data rather than generating insights—a problem that dark data significantly exacerbates.

Missed strategic opportunities and the value of dark data: Here's where the conversation shifts from risk to opportunity. With 95% of businesses recognizing data quality as critical to digital transformation efforts (Techment, 2025), the same dark data creating liability also represents untapped potential. Customer behavior patterns hiding in service logs, market insights buried in email correspondences, operational efficiencies lurking in IoT sensor data—these valuable insights remain invisible until you discover and activate dark data through proper data governance and data analytics.

The critical insight is this: unmanaged dark data is more than just a liability; it's a missed opportunity for strategic decision-making. The same hidden information creating risk could, if properly surfaced, become a source of significant competitive value and business intelligence.

What are the benefits of managing dark data?

The inverse of every risk presents an opportunity. Organizations that successfully illuminate and manage their dark data realize significant benefits that compound over time and support strategic initiatives.

Enhanced security and regulatory compliance: When you know what data you have and where sensitive information resides, you can protect it effectively. Comprehensive visibility enables proper access controls, encryption, monitoring, and incident response that strengthen cybersecurity posture.

Reduced costs and operational efficiency: Removing ROT (redundant, obsolete, trivial) data reduces infrastructure costs by up to 25%. Clean environments are also easier to modernize and govern.

Accelerated data analytics and AI initiatives: Activating relevant dark data dramatically expands the information available for data analytics, providing richer context, more training data, and better inputs for AI applications.

Improved decision-making and business insights: When previously hidden data becomes accessible, organizations gain new perspectives on customers, operations, markets, and opportunities.

Faster innovation and competitive advantage: Organizations with comprehensive data visibility and access can move faster than competitors still stumbling in the dark. Such teams can power new products, personalization, and faster experimentation.

How can you find dark data?

Discovering dark data requires a systematic, technology-enabled approach that combines people, processes, and modern data intelligence tools to improve data governance.

Conduct data audits and discovery processes

Begin with comprehensive audits across your entire data estate. Inventory all repositories and document what systems exist, what types of data they contain, who owns them, and when they were last accessed or updated.

Effective audits identify not just where data lives, but its characteristics: age, volume, structure, sensitivity, and business relevance. Interview data owners across departments to understand what data exists outside formal systems. Many organizations discover that significant amounts of dark data reside in shared drives, individual workstations, and shadow IT systems that never appeared on any official inventory.

Leverage data catalogs and metadata tools

Modern data catalogs can illuminate dark data and serve as the cornerstone of enterprise data governance. These platforms automatically discover data assets across disparate systems, extract technical and business metadata, and create a searchable, governed inventory of your entire data landscape.

A comprehensive data catalog serves as your central nervous system for data discovery and management. It connects to databases, cloud storage, business intelligence tools, and applications to automatically profile data, identify sensitive information for cybersecurity protection, map relationships, and document lineage. Advanced catalogs use AI to generate business-friendly descriptions, suggest glossary terms, and identify similar or duplicate datasets.

Use AI and automation to surface hidden data

Artificial intelligence and machine learning technologies excel at categorizing dark data. AI-powered tools can analyze content at scale, identifying sensitive information—a critical capability for both data governance and cybersecurity.

Natural language processing (NLP) can extract meaning from unstructured text in documents, email correspondences, and communications. Computer vision can categorize images and video footage. Pattern recognition algorithms can identify relationships between disparate datasets, surface anomalies that warrant investigation, and predict which dark data assets likely contain business value.

Automation extends discovery into continuous monitoring. Rather than conducting periodic audits that quickly become outdated, automated discovery tools constantly scan your environment, flagging new data sources, detecting sensitive information, and maintaining an up-to-date view of your complete data landscape.

How should you address dark data?

Once discovered, dark data requires deliberate management decisions. Not all dark data is created equal—some holds tremendous value for data analytics, some creates unacceptable cybersecurity risk, and some is simply worthless. A structured approach to evaluation and action is essential for effective data governance.

Classify dark data by value and risk

Create a framework that evaluates each dark data asset on two dimensions: business value and risk.

For value, assess potential use cases, relevance, and strategic impact. Does the data offer customer insights, market intelligence, or operational patterns that could drive decisions? High-value data merits investment and activation.

For risk, examine regulatory requirements, sensitivity, and security exposure. Does it include PII, financial details, or IP? Is it subject to retention rules or data subject rights? High-risk data requires immediate governance to ensure compliance and reduce security threats.

This approach yields four actions:

• High value + high risk: activate with strict governance.

• High value + low risk: activate quickly.

• Low value + high risk: secure or delete.

• Low value + low risk: archive or delete to cut costs.

Secure sensitive or regulated data

For dark data identified as high-risk, immediate action is essential. Implement access controls, encryption, and data loss prevention measures to address cybersecurity concerns. Apply sensitivity labels and classification tags that follow data across systems. Ensure compliance with regulatory retention and privacy requirements to avoid violations and penalties.

This is where data catalogs prove invaluable again—they provide the centralized policy enforcement platform needed to apply consistent data governance across distributed data. Modern catalogs can automatically classify sensitive data, enforce access policies, and track compliance posture across your entire data estate, reducing the manual effort required to maintain regulatory compliance and cybersecurity standards.

Archive or delete redundant data

Not all data deserves to live forever in active storage in data centers. For dark data assessed as low-value, implement aggressive retention policies. Delete redundant, obsolete, and trivial (ROT) data that serves no business or compliance purpose. Archive data that must be retained for regulatory compliance reasons but has no operational value, moving it to cold storage tiers that minimize cost.

This data decluttering delivers immediate benefits: reduced storage costs, improved backup and recovery times, decreased cybersecurity surface area, and simplified compliance management. It also makes your active data environment cleaner and more manageable, accelerating future discovery and data governance efforts while freeing up resources for more strategic initiatives.

Transform useful dark data into active assets

For dark data identified as high-value, build activation plans that turn hidden information into trustworthy, accessible assets that support analytics and business outcomes.

A common approach is creating data products—curated, documented, quality-assured datasets built for specific use cases. Data products give dark data the context, reliability, and usability needed for decision-making and AI.

Examples include:

• Call recordings → Customer Sentiment Insights

• Transaction logs → Purchase Behavior

• IoT archives → Predictive Maintenance Signals

Data products apply product thinking to data: clear ownership, defined consumers, and service-level expectations. This ensures activated dark data isn’t just available but purposeful, trusted, and aligned to business needs.

Modern data catalogs power this strategy by providing the marketplace to publish, discover, and consume data products, track quality, and support collaboration—turning dark data into strategic assets for analytics and AI.

Conclusion

Dark data is both a major risk and a major opportunity. It can create security, compliance, and cost challenges, yet also contains untapped insights and strategic value. Leaving it unmanaged leads to blind spots that hinder protection, compliance, and innovation.

The answer is shifting from passive storage to active data intelligence. This requires modern data catalogs for visibility, AI-driven discovery to classify data at scale, and governance frameworks that evaluate both risk and value. It also means treating data like a strategic product.

Organizations that do this turn hidden information into trusted data products that power analytics, AI, and business results. Most enterprises have significant dark data—the real question is whether you’ll stay in the dark or illuminate it to unlock its potential.

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