Internode Team

Dark or Dormant Data

The collection of underutilised information within organizations, that holds untapped potential to drive insights.

The potential in your organization

Every organization collects enormous quantities of data that remains unused, sitting in databases and systems. This untouched information, commonly referred to as "dark or dormant data" represents valuable digital assets with unexploited potential to drive insights and fuel innovation.

Email archives, chat histories, meeting transcripts, and recorded video calls contain valuable insights that often remain untapped. These everyday exchanges hold rich information about client relationships, project development, problem-solving approaches, and institutional knowledge. Research suggests most businesses analyze just 1% of their data, leaving the extraction of actionable insights largely dependent on manual effort.

What causes data to become dark or dormant?

Several factors contribute to data dormancy. Organizations often collect information without clear objectives or gather data for specific projects that eventually conclude, leaving the information without ongoing purpose. The ability to access nearly unlimited storage has also encouraged data hoarding. Technical obstacles present additional challenges, data might exist in formats that are difficult to access or within systems that poorly integrate with one another.

IBM estimates that 80% of all collected data qualifies as dark data. This information typically results from regular business processes and is retained for various reasons, including regulatory compliance. Dormant data can reside in numerous locations, including cloud or local storage systems. Operational data stored in specialized programs: such as design software, project management tools, and customer relationship platforms, may also be classified as dormant. According to the New York Times, data centers waste approximately 90% of their energy.

Data requires context to deliver value

Even with specialized tools and personnel available, extracting meaningful insights requires proper context. Simply providing data to analysts doesn't guarantee they'll utilize it effectively or explore it with appropriate context. While technical staff may possess considerable expertise, they don't necessarily understand the key information their colleagues or end users require.

Furthermore, individual departments typically collect data for specific purposes tailored to their particular needs. Departmental disconnection often results in data duplication or fragmentation.

Real-world example: A manufacturing company recorded temperature and vibration measurements from equipment over several years, initially using this data only for basic maintenance alerts. Later analysis of this historical information revealed patterns that could predict equipment failures up to two weeks in advance, reducing downtime by 30%.

Companies might store years of meeting recordings or thousands of customer support interactions without ever analyzing these conversations for patterns, recurring issues, or successful resolution strategies. This communication history represents a goldmine of organizational intelligence—revealing how decisions evolved, identifying subject matter experts, and preserving solutions to problems that may recur.

The hidden value in dormant data

Dormant data represents untapped potential across multiple dimensions:

  1. Business Intelligence:
    Historical data can reveal trends and patterns that shape future strategy.
  2. Operational Efficiency:
    Process data analysis can identify bottlenecks and improvement opportunities.
  3. Innovation:
    Dormant data might contain solutions to problems currently under investigation.
  4. Customer Experience:
    Deeper customer behavior understanding enables better-tailored products and services.

Without systematic approaches to extract value from these interactions, organizations risk repeatedly solving the same problems while valuable intelligence remains hidden in plain sight.

Automate and reuse dataflows

Automate workflows rather than disrupting them. Instead of constructing workflows from scratch, reuse existing ones. Dataflows operate continuously atop your data. Creating dataflow templates allows for consistent data flows and reports with minimal effort, enabling more time for exploring meaningful insights.

How AI actually analyzes Dark Data?

AI analysis of dark data resembles an extraordinarily efficient detective examining old case files:

  1. Pattern recognition: Just as detectives might notice similar details across seemingly unrelated cases, AI identifies patterns spanning thousands of data points that human analysts would overlook. AI might detect subtle indicators preceding customer cancellations, such as minor increases in complaints two weeks before termination—patterns too nuanced for manual detection.
  2. Connecting dots: AI functions like a detective capable of instantly recalling and cross-referencing details from thousands of cases. It discovers relationships between disparate data sources, potentially revealing how weather patterns subtly impact both delivery performance and customer metrics.
  3. Prediction based on history: Similar to how skilled detectives predict behavior based on past actions, AI leverages historical patterns to forecast future trends. By analyzing years of dormant sales data, AI might anticipate upcoming product popularity shifts.

The fundamental difference lies in scale and speed—AI performs these investigative functions across millions of data points within minutes, uncovering insights that would require humans years to discover.

The future:

As data analysis capabilities advance, dark data's value will surge exponentially. Soon, organizations will deploy AI automation specifically designed to transform dormant data into actionable knowledge. These systems will continuously mine historical communications and operational data, automatically surfacing relevant insights to employees exactly when needed—without requiring specialized expertise. This shift from data collection to knowledge activation will revolutionize how organizations operate.

Teams will receive intelligence from previously untapped sources within their natural workflows. Industries like education and healthcare, which currently underutilize their vast information stores, stand to gain tremendously when AI helps convert their dormant data into timely, actionable knowledge.

Glossary of Terms

API: Application Programming Interface. Aset of protocols allowing different software applications to communicate with each other.

Machine Learning: An artificial intelligence subset enabling systems to learn and improve from experience without explicit programming.

Telemetry: The process of collecting remote measurements or data and transmitting them to receiving equipment for monitoring purposes.

Unstructured Data: Information lacking predefined data models or organization, such as text documents, emails, or social media content.

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