The term dark data refers to the information that organizations collect but do not use. This can include web logs, call center transcripts, chat interactions, sensor readings, and even the long tail of social conversations that are never fully processed. Gartner estimates that more than 80 percent of enterprise data today is unstructured, and the majority of it remains untouched in storage systems. For marketers, that means critical parts of the customer journey are being missed.

A 2024 survey by Splunk found that organizations believe only 10 to 20 percent of their data is actively used for decision making. The remainder is left untapped either because it is unstructured, difficult to integrate, or locked in legacy systems. For companies spending billions on customer experience, this blind spot is growing more costly. “If you are not analyzing the full spectrum of your customer data, you are only seeing part of the picture,” said Carrie Palin, senior vice president and CMO at Splunk, when the company released its study.

The commercial stakes are rising. McKinsey research suggests that companies using the full breadth of customer data can achieve revenue increases of up to 20 percent and cost reductions of 30 percent in service operations. By contrast, firms ignoring dark data risk misallocating budgets and losing loyalty to competitors who act faster.

Examples are emerging across sectors. In retail, Walmart has been investing heavily in analyzing video and sensor data from its stores. By layering these dark data streams with transactional records, the company can identify bottlenecks, predict peak hours, and refine in-store marketing. In financial services, JPMorgan Chase has built models that mine previously unused call center transcripts to detect churn signals and fraud risks. In telecom, Vodafone has experimented with analyzing network event logs that were once discarded. These logs now feed into predictive systems that reduce service failures and improve customer satisfaction.

The technology to surface this information is advancing quickly. Natural language processing can now convert massive troves of call center notes into structured insights. Computer vision systems analyze video feeds to understand in-store behavior. AI agents connect across systems to stitch together patterns that were once invisible. Cloud platforms have added tools to bring unstructured data into the analytics pipeline at scale. Amazon Web Services, Google Cloud, and Microsoft Azure all now position dark data solutions as part of their enterprise offerings.

Despite these advances, challenges remain. Data privacy is at the forefront. Collecting and analyzing customer interactions at this depth raises questions about consent and transparency. Regulations such as Europe’s GDPR and India’s upcoming Digital Personal Data Protection Act set clear rules for what data can be used and how it must be protected. “The more you expand into dark data, the greater your responsibility to explain how that data is being processed,” said Julie Brill, chief privacy officer at Microsoft.

Another barrier is organizational culture. A PwC report on AI adoption found that fewer than 25 percent of executives feel their companies are “data mature.” Even where the technology exists, many organizations lack the skills or governance structures to turn raw information into actionable insights. Dark data often sits in silos owned by different departments, and integrating it requires cooperation across marketing, operations, and IT.

Still, early adopters show what is possible. Coca-Cola has long used vending machine data, much of it previously unstructured, to inform product development and distribution. The company discovered patterns in consumer choices that helped guide the launch of smaller packaging sizes. Airlines such as Delta have begun using maintenance logs and flight operation data to better predict disruptions, feeding insights into customer communication systems so that passengers are informed before problems escalate.

The consulting sector sees opportunity. Accenture has advised clients that unlocking dark data can generate competitive advantages not just in marketing but in innovation pipelines. By mining patent filings, scientific literature, and customer support data, companies can anticipate trends before they appear in sales reports. Accenture’s research highlights that firms applying advanced analytics to dark data are twice as likely to report market share gains compared to peers.

Industry analysts point out that dark data is not simply an untapped resource but also a cost. IDC estimates that by 2025 the world will create more than 180 zettabytes of data annually, most of it unstructured. Storing this information without using it drains IT budgets. Turning dark data into actionable insight is therefore both a defensive and offensive play. Organizations that fail to use it may find themselves paying heavily to warehouse a resource that competitors are actively exploiting.

The conversation is also shifting from collection to interpretation. Simply storing call recordings or web logs does not create value. Companies need systems that can connect these fragments to the broader customer journey. This is where marketing technology stacks are evolving. Customer data platforms are beginning to integrate unstructured inputs alongside structured purchase histories. The aim is a holistic view that captures not only what customers buy, but what they say, how they behave, and where they experience friction.

There are real world outcomes already visible. In 2024, a European bank reported that analyzing previously unused chat logs helped reduce call center volumes by 15 percent. A healthcare provider in the United States used unstructured physician notes to personalize outreach campaigns for chronic disease management, leading to higher adherence rates. A Southeast Asian e-commerce platform mined clickstream logs that had been discarded and discovered that customers frequently abandoned carts due to unclear return policies. Adjusting the website and communication around returns improved completion rates by nearly 10 percent.

The next frontier is predictive modeling. As dark data sources are integrated, algorithms can move from explaining past behavior to anticipating future decisions. This has already begun in insurance, where companies use telematics data to model driving risk. Similar approaches are now being tested in retail and hospitality, where unstructured behavioral cues can predict churn or upsell opportunities before they happen.

The question is how quickly organizations can scale these efforts. Surveys suggest enthusiasm is high but maturity is low. Most companies still analyze only a fraction of the information they collect. Data leaders warn that without a governance framework, diving into dark data can create noise rather than clarity. The challenge is to separate signal from background and to ensure insights are trustworthy.

For marketers, however, the opportunity is clear. Dark data represents the missing half of the customer journey. It contains the context, the emotion, and the nuance that structured data often strips away. In an era where personalization and trust define brand relationships, failing to use it risks irrelevance.

The race to harness dark data has therefore become a race to redefine customer intelligence. Companies that succeed will not only understand what their customers do but why they do it. They will spot friction points early, anticipate needs, and deliver experiences that feel intuitive rather than forced. Those who ignore it will continue to spend heavily on the visible parts of the journey while leaving the most valuable insights locked away.

As one senior marketing executive recently observed, the competitive battlefield is shifting. The question is no longer how much data a company has, but how much of its dark data it can illuminate. The companies that master this shift will set the standard for customer experience in the decade ahead.