What is Dark Data and What its Types?


Dark data is digital data that isn’t being utilized. Businesses accumulate tremendous volumes of data, which, they think, will help to improve their services and products. For instance, an organization may gather data on how clients utilize its products, internal insights about software development procedures, and site visits.

Progressively, the term Dark data is being linked with big data & operational data. For example, server log files that could give pieces of data to site visitor behaviour, client call records that include unstructured customer opinion data and geolocation data that could uncover traffic patterns that would help with business strategy planning.

Possibly, this sort of Dark data can be utilized to drive new income sources, dispose of waste and lessen costs. Dark data is a subset of big data yet it comprises the greatest part of the complete volume of huge data gathered by businesses in a year. Dark data isn’t normally examined or processed in light of different reasons by organizations however that doesn’t decrease its significance with regards to business value. There are two different ways to see the significance of Dark data. One view is that unanalyzed data contains new, significant bits of information and is an opportunity lost. The other view is that unanalyzed data if not taken care of well, can bring about a lot of issues, for example, lawful and security issues.

Types of Dark Data

In spite of the fact that the categories of Dark data may change across organizations, the following types of unstructured data ordinarily are viewed as Dark data:

  • Log Files
  • Customer Data
  • Account Data
  • Previous Employee Data
  • Financial Statements
  • Raw Survey Data
  • Email Correspondences
  • Old Versions of Relevant Files
  • Notes or Presentations

It is astonishing in light of the fact that at the time of data gathering, the organizations expect that the data will offer some value. Organizations spend loads on data accumulation so both financially and otherwise, data must be viewed as important. Here are a couple of reasons why there is such an extensive amount of Dark data:

Example of Dark Data

  • Lopsided priorities

Take the case of a bank examining online applications for credit cards. The marketing team is focused exclusively around customer details and eligibility yet no thought is paid to the data on how the client landed on the application page. The unattended data could have given important bits of information on the ease of use of the bank website and the application page.

  • The disconnect between different departments

In big business, different departments have their very own data gathering and storage processes which may not be known to the other. In this way, data, regardless of whether important to other departments of the organization, remain unused.

  • Technology and tool constraints

In the event that data collection is executed using various tools and technologies in the same business, there might be cases that these tools don’t interact with one another. This makes uniting all of the data together and putting it in use.

You can also read: Why You Should Transform Dark Data into Digital Format?

This entry was posted in and tagged on November 22, 2019 by

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