Big data in today’s world is nothing new. Whatever data is present today is big data. But what if data stored in the history of organizations can be queried today to reveal patterns for a long period of time exposing how customers like what they liked or why clients didn’t choose what they didn’t choose.

Dark data can do exactly that. You can push archived files through the mechanism to process dark data which can open new avenues to be explored. What is dark data then? How can it be used to analyze old data? And how it can help to process dark data?

Digital information that is not being used is called dark data and is taking the data science industry by storm. Gartner Inc. defines dark data as information based assets which organizations collect, process, and stores for day to day business activities but rarely use for other requirements.

The majority of data that can be segregated as dark data is vast. It can be any file- Excel Sheet, Word Documents, scanned images or Powerpoint Presentations. You ask- why would data analytics companies do that? There are, apparently, practical reasons involved. It takes too much time, patience, and energy to precipitate the complete data through a filtration process. By the time, you are ready to look at the precipitate- they are too old to be of any use. It becomes outdated, in a sense.

Moreover, there are issues of data quality, transcription errors, lack of knowledge about such errors- which is particularly true about data sourced from internet based files. Another loophole could be confidence in dark data files and whether or not the collected data genuinely links back to the source they say they are sourced from.

The examples of dark data critical for data science industry can be server log files with hidden clues to the website traffic, visitor behavior, call detail records of customers, and mobile geolocation data. All of these clues are links to unstructured data in terms of consumer sentiments. Dark data is increasingly being related to the terms operational data and big data and holds a lot of value for data analytics companies around the world.

Dark data has a lot of potential to reduce the costs involved, eliminate wastage, and to eventually drive new sources of revenue. Most of the organizations in the business world today store these kinds of information for regulatory compliance purposes. But times are changing. Hadoop is increasingly being used to leverage such kind of data to be implemented in business value.

There are many examples to prove the profits of delving deep into the dark data. Companies involved in digital marketing (Seriously, which company isn’t) and check online advertising efforts and paid-search campaigns to tweak marketing tactics to bring home revenues. What worked or didn’t is easily visible in cold facts. Dark data can become business gold for data analytics companies if NoSQL databases and Hadoop clusters are allowed to work.