Dark Data: 6 Ways To Include It In Your Analytic Strategies

A subset of Big data, Dark Data refers to the data that is generally ignored and stored without any kind of indexing. This type of data gradually becomes invisible to the researchers and finally results in being lost. Dark data is usually unstructured data as they are collected by organizations unintentionally. They are  neither used for decision making nor made available to the public.

How Is It Generated?

Dark data is generated when huge volumes of data are collected and not much analysis is done. As you know, every minute data is generated. When you click on a site or a link, data is generated which is used by organizations to analyze and improve their business. However, only a fraction of it which is structured and stored in databases is used by organization. The rest, which remains unstructured eventually gets lost  between the other indexed data.

Recent studies highlighted that every single day in the world around 7.5 sextillion gigabytes of data is generated and 6.75 septillion megabytes of data remains as dark data which remains stored without being processed or analyzed in the files of data repositories. One of the main reasons why dark data is generated is due to the lack of good analytical tools that are compatible with other data formats in order to analyse for decision making process.

Information access has always been an important determinant in making intelligent decisions. While some information  is readily available at the ti of our hand, others require more effort. There are also information that lies inside unstructured data and thus requires considerable effort to access.

6 Ways You Can Include Dark Data In your Analytical Strategies

Dark Data which is usually buried in unstructured data cannot be easily accessed as it often needs modern data analysis tools like machine learning. As unstructured data is growing per day owing to today’s smart connected machines, the ability to analyze and access this data would prove to be the biggest differentiation for future businesses, especially investors.

  • Identify What You Have

    In the past decade, data was always an afterthought for most companies. Today, companies in the process of their digital transformation are finding that they possess huge volumes of unstructured data in hard copy that they had never thought of digitalizing till now. This big data can have the capability of providing valuable insights.

    The first step is to find out what data that the company did not realize it had. The next step is to create a strategic plan to address how to make use of this data to deliver value to the company.

  • Use What You Have

    As soon as you find specific areas of data that are useful, get started on digitalizing it and harvesting it for value so that you can make it work for your business.

  • Identify Outside Data That Can Enhance Your Decision Making

    Sometimes outside data sources can augment the data you already have with you. For instance, let’s consider the example of Greenland’s ice pack monitoring. If you are monitoring climate change and are perturbed by global warming, the ideal course of action would be to do a close study of the historical photos of Greenland;s land mass from decades ago. This is because a general comparison of how it is today and how it was decades back can illustrate both the progression and impact of global warming.

  • Collect Data For Integrity, Data Quality & Privacy

    With forms that are paper- based getting digitalized, it is required that data goes through quality assurance checks for quality and data integrity. During  this process, the errors in data must be identified and rectified. In certain cases, privacy concerns might also rise that must be addressed. These points mentioned above should be addressed while you are doing your data cleanup exercises before your new digitalized data is entered into a new data repository.

  • Develop Proactive Data Management Methods For Technologies New Technologies

    Your data management does not stop with getting control over all the data you have under management. Recent studies indicate that the data generated by IOt will see a huge increase in the coming years. Thus, as more and more companies adopt IOT technology today, there is a need to analyze what should be done with the future data that is collected with this implementation.

  • Show Results

    We can all agree with the fact that data should provide some value. If you are not able to demonstrate the business value derived from using unstructured data that you want to digitalize, then you should reconsider investing in and retaining the data.

 

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About Vibhuthi Viswanathan

Vibhuthi is a an avid follower of the latest trends in the world of Technology. Her writing aims to engage and educate the readers on all things Tech. When she is not twirling with words and pauses at SpringPeople, she binge reads popular literature.

Posts by Vibhuthi Viswanathan