SER Blog  Customer Stories & Use Cases

Dark data: Lost gold or cost trap?

Christian Bley

In the age of digitalization, data have become the most valuable assets for businesses. Yet while the importance of data has grown exponentially in recent years, there is still an element that remains lost in the dark – dark data. This article takes a deep dive into dark data, its importance, and the impact it can have on a business.

What is dark data?

The concept of dark data refers to the information and data that businesses collect but do not actively use or analyze. These data are frequently unstructured or unused and reside in data silos deep within enterprise systems, such as, for example, in emails, documents, images, videos, text messages, and much more.

Many businesses primarily see a cost factor in structuring. But dark data should be considered more as a valuable resource. Here are a few figures to give you a better idea of the importance of dark data:

-          In 2023, 120 zettabytes of data will be generated

-          80% of existing data is unstructured

-          50% of the data are dark data

Challenges and risks of dark data

Bulk of information

What information is important? What is unimportant? Who needs access to this information, when and where? Thanks in no small part to the pandemic, we can choose from a range of platforms that allow us to share information easily and quickly. As a result of this trend, which has been going on for years, we face a flood of information in our daily work.

Increasingly cheaper storage technologies have also added to this trend. A large part of this redundant and also partially forgotten information is dark data.

GDPR compliance

Another challenge is compliance with the General Data Protection Regulation (GDPR) and other data protection regulations. If a business does not know what data it has, it cannot take the steps needed to protect these data and comply with regulations. Dark data can thus pose a considerable risk for data protection compliance.

Lack of expertise and software solutions

It is becoming more and more important to be able to analyze dark data. To do so, a business needs to have the right know-how and the right tools. How data are handled is critical for the amount of dark data produced and for how it can be leveraged. As a result, software solutions that are usually not yet in use and therefore have no track record can help. Prompt engineering is one such solution: An AI program is given instructions in natural language, which are continually refined to achieve optimal results. This is a discipline that simply did not exist previously.

Cybercrime

Dark data are often unprotected and unsecured. This makes them a prime target for cybercriminals. For this reason, businesses need to ensure that they have implemented robust security measures, in order to protect dark data from threats.

Loss of valuable insights

Dark data contains potentially valuable insights about customer behavior, market trends, and operational efficiency. If these data are not analyzed, these insights are lost, and companies miss a valuable opportunity to improve their business strategies.

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How to leverage dark data

To leverage dark data, companies need technology and solutions that help them to identify, analyze, and use these data.

Enterprise content management (ECM)

ECM systems are crucial for organizing unstructured data and making it available for use. In addition to the ability to classify, index, and archive documents, access to information can also be clearly defined. This is important, because not every employee is authorized to access every bit of information. It can also help to manage the above-mentioned flood of information and ensure that users get only relevant information, which improves efficiency.

In fact, enterprise content management goes one step further and ensures that documents are deleted or anonymized at the end of their life cycle. This, in turn, enables businesses to meet compliance guidelines and legal requirements related to data protection.

Practical example: Dark data in contract management

A software company uses its ECM to manage all existing contracts and related documents, including service records and license information. The contracts mainly contain master data information about the contractual partners but they also contain conditions and other terms. Information about the objects of a contract can be found in the related documents.

An analysis of maintenance records provides information about the software modules covered by service agreements for all customers. Trends in number and duration can be determined. It turns out that over time certain licenses or products no longer appear in the contracts. This could indicate that they are either no longer in demand or are outdated. So a product is being maintained that is no longer used by customers.

Consequence: Discontinuing these products could result in significant cost savings as development is no longer being pushed and resources no longer need to be allocated to provide service in this area. Dark data therefore becomes a valuable source of cost savings and process optimization.

Artificial intelligence (AI)

The latest developments in artificial intelligence allow businesses to process dark data effectively and quickly.

Until now, businesses have hardly been able to analyze dark data at the same speed as it was produced. For the first time, AI makes it possible to classify data on a large scale so that patterns and trends can be identified more quickly.

Practical example: Tap dark data from chats

A company manages its contracts digitally. When drafting, during negotiations, and after conclusion, certain passages and information from the contract have to be requested. The problem: Where is the information you are looking for? How should it be interpreted?

The user can now interact with the document using a chatbot based on a large language model (LLM) and, for example, ask about the contractual partners involved in the document. In addition to such simple queries, much more complex questions about contextual information would also be possible. By assessing the contractual penalties, users can get information about potential risks. Users thus gain access to the entire process by talking to the document.

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Benefits of leveraging dark data

The benefits of leveraging dark data can be found at operational and strategic levels.

Cost savings

By identifying and analyzing dark data, unused resources can be detected and removed. This can lead to significant cost savings, whether by discontinuing services that are no longer needed, as in the example above, or by optimizing processes.

Improved transparency and efficiency

Using dark data also means having better access to relevant information. This massively improves the transparency of processes, which significantly improves communication internally among your own employees and externally with business partners. Incorporating dark data in decision-making processes ensures greater efficiency and higher quality results.

Better customer relationships

Having the right data at the right time can also improve customer relationships. If you understand your customers better and know which products or services they prefer, you can offer customized quotes.

Risk management & compliance

Another benefit that can be derived from dark data is improving compliance and risk management strategies. Dark data often contains information that can indicate possible compliance violations or risks, but this information often goes unnoticed. By identifying and analyzing dark data, companies can identify potential risks early and take proactive measures to avoid legal and financial consequences. This can help protect the company's reputation and strengthen trust among customers and investors.

The future of dark data

What will happen to dark data in the future? Is the usefulness of this information overstated? What options do companies have to deal with dark data? In broad terms, the future will be influenced by two main factors.

Data literacy

The ability to understand and use data effectively is becoming increasingly important. Companies need to invest in training and upskilling of their employees to ensure they have the necessary data skills. The best way to leverage dark data is to ensure that dark data is not produced in the first place. So it is important to act responsibly when sharing and disseminating information. Employees have to ask themselves, What data do I store? Where do I store it? And in what format? So that it reaches the right recipients and can be retrieved in the future. Data has to be interpreted and evaluated correctly in order to decide the proper courses of action. It basically comes down to encoding and decoding data.

Advances in artificial intelligence

In recent years, we have seen significant progress in artificial intelligence. These advances will help to use dark data even more effectively, as AI systems are able to recognize patterns and connections in the data better and faster than humans. In the future, this technology will improve further in terms of quality and handling, making it more accessible to the broader market. This development has already given rise to completely new professional sectors. For example, prompt engineers ensure that an AI program provides the optimal results in response to specific questions.

AI can also help to differentiate dark data. Not all archived information is relevant, of course. A large part of the data comprises redundancies that simply need to be filtered out. It is also not true that every bit of information can provide insights. A significant portion of dark data is simply unimportant.

Tips for dealing with dark data

Hey Doxi, please give me some tips on how to leverage dark data

Here are my top 5 tips for leveraging dark data:

  • Prioritize: Start by identifying the most data-intensive areas and prioritize them.
  • Get help: If you don't have the necessary expertise, consider bringing in outside experts to effectively use dark data.
  • Automate data classification and analytics: Implement automation solutions that can facilitate classification and analysis of dark data. State-of-the-art data analytics platforms and AI tools can automatically search, categorize, and extract relevant information from large amounts of data. This not only saves time but also enables continuous monitoring of your data sources to identify dark data in real time.
  • Establish a culture of data responsibility: Create a culture in your organization that promotes responsibility for data and dark data. Make your employees aware of the importance of data management and data protection. Make sure every employee understands how they can help identify and securely manage dark data. Active participation by all employees is crucial to managing dark data effectively.
  • Don't ignore: The biggest mistake you can make is to ignore dark data. Identify its potential and use it systematically.

A lost trove of information

Dark data may be hidden, but they hold untapped potential for companies. By effectively identifying, analyzing, and leveraging dark data, companies can save costs, optimize processes, and make better decisions. It's time to shine a light on dark data and discover this treasure trove hidden in your business data.

FAQs about dark data

What is an ECM system?
ECM is an acronym for enterprise content management. It is enterprise-wide software that manages, structures, and digitizes information. The software is designed to capture, find, process, share and retain electronic documents.
What is a large language model (LLM)?
An LLM is a language model capable of using machine learning to process natural language. LLMs are used primarily to analyze text-based content. The most widely-known model currently is GPT from OpenAI.
What are ROT data?
Only a small part of dark data contains business-relevant data. However, these data have a disproportionate relevance. The rest are ROT data – redundant, obsolete, and trivial data. One of the biggest challenges when working with dark data is filtering out the ROT data.
What is the difference between dark data and big data?
Big data refers to all of the data that a business generates from a range of sources, such as emails/documents, websites, and social media, for example. Dark data are part of this data that are unused. This can be because these data are difficult to assess, unstructured, or not relevant.

Christian Bley

Hi, I'm Chris Bley, Solution Engineer at SER. For 15 years, I have been advising my customers on all aspects of digitalisation, developing concepts and implementing projects in this area. A significant part of my work consists of imparting my knowledge. In addition to organising workshops, I therefore create a lot of content in the form of articles, videos, white papers and much more. If there is still creative energy left at the end of the day, it flows into my recording studio and new song lyrics.

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