Dark data refers to the information that is stored in an organization but is never used, analyzed, or reported on. Despite being collected and saved, this data remains unused and is often referred to as “invisible”. Dark data is a growing concern for organizations as it is estimated that up to 90% of data collected by organizations is never used. This information can be valuable for decision-making and optimizing processes, but it can also put organizations at risk if it falls into the wrong hands. In this article, we will explore what dark data is, how it affects organizations, and how to find it.
What is Dark Data?
Dark data refers to the vast amount of information that organizations collect and store but do not use. This information can include data collected from a variety of sources, including email, social media, customer interactions, and other internal sources. The data is saved, but it is never analyzed or acted upon. As a result, this data is often referred to as “invisible” or “lost”.
Dark data is a growing concern for organizations because it can represent a significant risk if it falls into the wrong hands. Sensitive information, such as customer data, financial information, or intellectual property, can be exposed if the proper controls are not in place. Additionally, dark data can also have a significant impact on an organization’s bottom line by taking up valuable storage space, slowing down systems, and increasing costs.
How Dark Data Affects Organizations
Dark data can affect organizations in several ways, including:
- Increased storage costs: Dark data takes up valuable storage space and can increase storage costs for organizations.
- System slowdown: Dark data can slow down systems and make it difficult for organizations to access the information they need.
- Security risks: Sensitive information can be exposed if proper controls are not in place to protect dark data.
- Incomplete insights: Dark data can prevent organizations from gaining a complete understanding of their operations and customers.
- Regulatory compliance: Organizations may be at risk of non-compliance with regulations, such as GDPR, if they are not properly managing dark data.
How to Find Dark Data
To find dark data, organizations must first identify the data sources within their organization. This can include data stored in databases, file systems, email, and other systems. Once the sources have been identified, organizations can use data analytics tools to analyze the data and identify patterns and relationships. Additionally, organizations can also use machine learning algorithms to identify dark data and categorize it based on its potential value and risk.
Conclusion
Dark data is a growing concern for organizations as it represents a significant risk if it falls into the wrong hands. To find and effectively manage dark data, organizations must first identify their data sources and use data analytics tools and machine learning algorithms to analyze the information. By taking the time to understand and manage dark data, organizations can reduce their risk, increase their insights, and improve their bottom line.
