Dark processing - Function, Application and practical Examples

The consistent evaluation of data supports companies in optimizing their entire value chain. The analysis of unused and unstructured data proves to be a particular challenge, which - when evaluated correctly - streamlines processes and enables better decision-making. In this context, so-called dark processing is therefore playing an increasingly important role.

Dark processing enables organizations to turn unused resources into valuable information and thus promote innovative solutions. We explain how dark processing works, which concrete examples you should know from practical applications and which software you can also use to introduce dark processing in your company.

The most Important in a Nutshell

  • With automated dark processing, companies automatically tap into unused data and generate valuable information from it.
  • Dark processing is particularly widespread in the input management of insurance companies and in quality control in production.
  • With Konfuzio's AI software, companies automate dark processing in documents of all types.
dark processing definition

Dark Processing - Definition

Dark processing refers to business processes that take place fully automatically and without human interaction in the background. They take place "in the dark", so to speak, without companies observing or influencing the process.

Dark processing focuses on the extraction, analysis and use of unstructured data.

This data often accumulates in large quantities. However, organizations often neglect them due to their non-obvious importance or unclear structure. Dark processing aims to tap into these data sources to gain valuable insights.

To do this, it transforms raw data from a variety of sources, such as text documents, emails, log files or social media, into usable information - without requiring staff to intervene or monitor the process. To do this, dark processing applies techniques from machine learning, text analysis and data visualization. 

Dark processing is used in a variety of industries. These include healthcare, financial services, retail and manufacturing. The technology enables companies to identify trends, detect problems early, optimize processes, make informed decisions - and thus increase competitiveness.

How does Dark Processing work?

Dark processing allows companies to tap into unused data sources and extract valuable information from them. As a rule, the process takes place in 5 steps:

  1. Data collection

    First, companies collect data from various sources such as emails, text documents, databases, sensors or social media. This data can be structured or unstructured and can be in different formats.

  2. Data cleansing

    After data collection, it is necessary to cleanse the data. This means that companies remove erroneous, redundant or irrelevant information to improve the quality of the data. This process usually includes automated removal of duplicates, correction of typos, and standardization of data.

  3. Data analysis

    Powerful software with artificial intelligence for dark processing shows its strengths from this step on: It analyzes the data according to predefined criteria. In the process, the software searches the data for patterns, correlations or trends. Machine learning, statistical analysis and text processing techniques are also used.

  4. Gain knowledge

    Automated data analysis leads to the extraction of insights. These are, for example, new information about customer behavior, market trends, efficiency problems or other relevant aspects.

  5. Make decisions

    Based on the insights gained, companies make decisions. These include, for example, optimizing processes, developing new products or improving the customer experience.

By the way

Dark processing is not a one-time event, but a continuous process. Companies are constantly generating new data, so they need to constantly analyze it in order to always have information and insights.

Have questions about how dark processing can help your business? Then talk to one of our experts now!

dark processing use cases

Fields of Application of Dark Processing

Dark processing is used in those industries where companies are confronted with large quantities of unstructured data and unused data. The technology is therefore particularly widespread in the following fields of application:

Public health

In healthcare, facilities collect mountains of data in the form of patient records, medical images and research results. Dark processing helps to gain clinical insights, improve patient care, and reduce costs.

Practical example

A hospital has an extensive database containing medical images, patient records, and laboratory tests. Dark processing automatically integrates and cleanses this data, standardizes it, and analyzes medical images to detect anomalies. In addition, it creates comprehensive patient profiles based on health data and lab test results. Using machine learning, the dark processing software also trains models to help physicians diagnose cancer and recommend personalized treatment plans.

In addition, dark processing enables the use of data for medical research purposes to gain new insights into cancer. This enables the healthcare industry to detect diseases earlier and develop life-saving therapies.

Financial services

Banks and financial institutions hold large amounts of transaction data, customer information, and trading data. By analyzing this data, they uncover fraud, better manage risk, and provide personalized financial services.

Practical example

A large financial services provider collects extensive transaction data from thousands of customers, including credit card and account usage information. Dark processing software captures this data in real time and checks it for irregularities and errors. Using advanced analytics techniques and machine learning, the software detects unusual patterns and suspicious activity in the transaction data in real time.

As soon as it detects any deviations, it triggers an alarm. This enables the financial institution to act immediately by stopping suspicious transactions and informing the customer of possible fraudulent activity. This proactive measure helps minimize financial losses and boost customer confidence.


Retailers generate countless sales, customer and inventory data. Dark processing helps to better understand customer buying behavior, optimize inventory levels, and run personalized marketing campaigns.

Practical example

A major retailer is using dark processing to more accurately analyze the shopping behavior of its customers. To do this, the company collects data from various sources, including point-of-sale transactions, online purchases and customer reviews. By analyzing this data, the company finds out which products are particularly popular and at what times most purchases are made. Based on this, the retailer manages its inventory more efficiently by ensuring that popular products are always available and overstocks are avoided.

In addition, the company uses the insights gained to run personalized marketing campaigns. Customers receive tailored offers and recommendations based on their previous shopping habits. This leads to an increase in customer satisfaction and increases the likelihood of repeat purchases.


In the manufacturing industry, there are huge amounts of machine, production and quality data. Dark processing helps here to increase production efficiency, detect quality problems at an early stage and predict maintenance needs.

Practical example

A company that manufactures metal parts for the automotive industry uses dark processing to improve the efficiency of its production equipment. The machines in the production line continuously generate data, including information on speed, temperature and pressure during the manufacturing process.

Using dark processing techniques, the company analyzes this machine data in real time. It uses machine learning to identify normal operating patterns. When deviations from these patterns occur, the dark processing software automatically triggers alerts. This enables the company to immediately identify quality problems or anomalies in production and take countermeasures before defective parts reach final assembly.

In addition, the company uses automated dark processing to predict the maintenance needs of its machines. Using historical data and machine learning algorithms, it predicts when the company will need to replace or maintain certain components to minimize unplanned downtime.

Logistics and transport

Companies in this industry manage complex data streams related to supply chains, route optimization and vehicle monitoring. Dark processing helps improve logistics processes and reduce delays.

Practical example

An international freight forwarding company collects data from a variety of sources through dark processing, including GPS tracking data from delivery vehicles, weather data, traffic reports and delivery receipt information. This data is diverse and unstructured, but it provides valuable insights into the state of the supply chain.

By applying dark processing techniques, the company analyzes this data and identifies patterns in everyday processes.

For example, it finds that deliveries on certain routes are repeatedly delayed due to traffic problems. Based on these findings, the company takes measures to minimize these bottlenecks. To do this, it adjusts routes, selects alternative means of transport and plans delivery times better.

In addition, dark processing helps the company predict vehicle maintenance needs by analyzing data from sensors on the vehicles. This makes it possible to prevent breakdowns and increase fleet efficiency.


Insurance companies process large volumes of insurance applications, claims and customer communication. Among other things, they use dark processing to automate input management.

Practical example

With dark processing in input management, insurance companies process incoming documents such as damage reports or insurance applications without human intervention. As a rule, dark processing first converts documents into machine-readable text using optical character recognition (OCR). It then identifies the type of document, classifies it accordingly (for example as a damage report) and extracts relevant information such as names, addresses and damage numbers. It checks this extracted data for accuracy and completeness before integrating it into the insurance company's internal IT system or database.

Dark processing in input management enables insurance companies to significantly speed up their workflows as it minimizes manual steps. It also reduces human error, which leads to higher data quality. The result: insurance companies make their input management more efficient, reduce the processing time of customer inquiries and thus increase customer satisfaction.

Energy and supply

Energy and utility companies have a lot of data related to energy production, consumption patterns, and environmental impacts. Dark processing helps to increase energy efficiency and comply with environmental regulations.

Practical example

A detailed real-world example is the application of dark processing in a large electric utility. This continuously collects data from sensors in power plants, meters at customers' premises and meteorological stations. The data is diverse and includes up-to-date information on power generation, consumption and weather conditions. To gain valuable insights from this, the company analyzes consumption patterns to identify peak times and adjust power generation accordingly. In this way, it avoids bottlenecks. It also uses the data to optimize the use of wind and solar energy based on weather forecasts. 

In addition, dark processing helps the electric utility monitor environmental data and ensure that the company is complying with applicable environmental regulations. To do this, it monitors and analyzes emissions data: In this way, it ensures that air and water pollution remain within legal limits.


Educational institutions collect data on student performance and curriculum, among other things. This enables them to tailor their teaching to the requirements and needs of the students.

Practical example

A concrete real-world example in the education industry is the use of dark processing techniques to personalize learning. When a school or college uses learning platforms and e-learning tools, students generate a variety of data during their learning process, including answers to quizzes, time spent on specific learning modules, click behavior within online courses, and interactions in discussion forums. Among other things, it reveals that some students have difficulty with a particular learning concept or subject, taking longer than average to complete that module or giving incorrect answers multiple times.

Based on these findings, educational institutions design personalized support. A student who has difficulty with mathematics, for example, therefore automatically receives additional exercises or resources on this topic.

dark processing advantages

Advantages of Dark Processing

Companies and organizations gain a competitive advantage through dark processing. Which benefits lead to this in detail?

Knowledge gain

Dark processing enables organizations to reveal hidden patterns, trends and relationships in their data. This leads to deeper understanding and new insights that help in decision making and problem solving.

Better decision making

The insights gained from dark processing serve as the basis for sound, data-driven decisions. In this way, companies and institutions minimize risks in everyday business operations and identify untapped opportunities.

Efficiency increase

By analyzing data sources, companies optimize inefficient processes. This leads to cost savings and better use of resources.

Higher data quality

If you automate manual processes via dark processing, you obtain higher data quality. Because: Without manual intervention, fewer errors occur in data evaluation. This also means that you can base your decisions on a secure database.

Customer understanding

Automated dark processing helps to better understand customer behavior and preferences. Companies thus develop personalized offers and marketing strategies to increase customer satisfaction and promote customer loyalty.

Early detection of problems

Dark processing helps identify problems and deviations early. This is especially important in industries such as healthcare and manufacturing, where timely intervention can be vital or business-critical.

Risk Management

Organizations use dark processing to better identify and proactively respond to risks and threats. This is critical to avoid financial losses and reputational damage.


Analyzing unused data leads to new ideas and innovations. In this way, dark processing helps companies to develop new products and services and broaden their horizons for future business opportunities.

Transparency and compliance

In some industries, such as healthcare and finance, dark processing is important to meet regulatory requirements and ensure transparency around privacy and security.

Cushioning the shortage of skilled workers

With dark processing, companies cushion the shortage of skilled workers in some areas. After all, if you have fewer processes to be handled manually by employees, more resources are available for more complex, strategic tasks.

Dark Processing Challenges

Dark processing brings several challenges that companies can overcome with careful planning, thoughtful resource allocation, and a willingness to invest in technology and training. Key challenges include the following:

Data security and data protection

Dark processing requires access to sensitive data, which raises security and privacy concerns. Processing sensitive data therefore requires robust security measures to ensure that data is protected from unauthorized access and data leaks.

Data quality

Often, unused data is unstructured and of low quality. Cleaning and preparing this data is therefore a crucial but time-consuming step to ensure that the analysis delivers correct and meaningful results.

Lack of expertise

Dark processing requires skills in data analytics, machine learning, and statistical methods. Companies face the challenge of finding or training employees with the necessary skills.

Integration of data sources

Organizations often have data in different formats and from different sources. Bringing this data together and integrating it is an important step in getting a complete picture. Without powerful software, this is a challenge for organizations.


As companies grow and generate more data, they need to ensure that their dark processing systems are scalable. Only then will they be able to handle the increasing volume of data.

Ethics and compliance

The use of dark processing techniques raises ethical issues, particularly with regard to privacy and the use of data. Companies must ensure that they adhere to ethical standards and comply with data protection regulations.


Implementing dark processing requires investment in technology and skilled labor. Organizations must also consider the long-term costs, including system maintenance and upgrades.

Konfuzio - Intelligent AI Software for Dark Processing

Dark processing means automation and Konfuzio is a proven expert in dark processing. Automation of business processes. To do this, the provider has intelligent software that combines artificial intelligence, machine learning and deep learning. In practice, this means that companies use Konfuzio to automate the entire dark processing process. The software is able to extract, analyze and evaluate unused and unstructured data in documents of all kinds - without manual intervention or verification. As a result, companies automatically have access to high-quality, meaningful data that they can use to make better, more informed decisions.


Dark processing takes place in the "dark" without a user witnessing or influencing the execution.

This makes dark processing a form of automation that generates numerous benefits for companies. Not only does it save their employees' resources, it also provides them with a high-quality database on which to make informed business decisions. It is therefore no wonder that companies are already using automated dark processing in numerous industries such as insurance, banking, manufacturing as well as logistics. 

Let our experts advise you now on how you too can introduce dark processing in your company! 

Jan Schäfer Avatar

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