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What is Dark Data Discovery

Updated: Jan 16, 2023

Dark data, also known as "digital shadow," is a type of data that is collected but not used or analyzed. It is estimated that over the long run organizations accumulate about 80% of their data as dark data, which can include data from social media, emails, employee data, log files, notes, raw survey data as well as data from sensors and other sources. Some of the common data types are transactional, master, reference, reporting and meta data.

Dark data discovery is the process of identifying and analyzing this data to extract valuable insights and information. It involves identifying and cataloguing the sources of dark data within an organization, as well as the tools and techniques needed to analyze and interpret it.

One of the main challenges of dark data discovery is the sheer volume of data involved. Organizations can accumulate vast amounts of data over time, making it difficult to identify and extract the most valuable information. Additionally, dark data can be unstructured, meaning it is not organized in a way that makes it easy to analyze.

Despite these challenges, there are several tools and techniques that can be used to facilitate dark data discovery. Machine learning algorithms, for example, can be used to analyze large volumes of data and identify patterns and trends that would be difficult for humans to detect. Natural language processing (NLP) tools can be used to extract and analyze text data, such as emails and social media posts.

In conclusion , dark data discovery is a valuable tool for organizations looking to extract valuable insights and information from their data. By identifying and analyzing dark data, organizations can gain a deeper understanding of their customers, markets, and operations, leading to improved decision-making and increased efficiency and substantial cost saving For example, analyzing customer emails can provide valuable insights into customer preferences and behaviors, which can be used to improve marketing strategies and customer service. Similarly, analyzing social media data can provide insights into public sentiment and trends, which can be used to inform business decisions.

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