Huge information has a lot of potential to benefit companies in any industry, all over across the globe. Big information is far more than just a great deal of data and specifically combining different information sets will provide organizations with real insights that can be utilized in the decision-making and to enhance the monetary position of an organization. Prior to we can comprehend how big data can help your organization, let’s see what big data in fact is:

It is typically accepted that huge information can be discussed according to three V’s: Speed, Range and Volume. However, I would like to add a couple of more V’s to better discuss the effect and ramifications of a well thought through big data strategy.


The Velocity is the speed at which data is developed, stored, examined and pictured. In the past, when batch processing prevailed practice, it was regular to receive an upgrade to the database every night and even weekly. Computer systems and servers needed significant time to process the information and update the databases. In the big data era, information is created in real-time or near real-time. With the availability of Internet connected gadgets, cordless or wired, makers and gadgets can pass-on their data the minute it is developed.

The speed at which data is produced presently is nearly inconceivable: Every minute we publish 100 hours of video on YouTube. In addition, over 200 million e-mails are sent out every minute, around 20 million pictures are seen and 30.000 uploaded on Flickr, practically 300.000 tweets are sent and practically 2,5 million questions on Google are carried out.

The difficulty organizations have is to handle the enormous speed the data is produced and utilize it in real-time.

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In the past, all data that was produced was structured information, it neatly fitted in columns and rows however those days are over. Nowadays, 90% of the data that is generated by organization is unstructured data. Data today can be found in several formats: structured information, semi-structured data, unstructured information and even intricate structured information. The wide array of data requires a various method in addition to various techniques to store all raw information.

There are various types of information and each of those types of data need different kinds of analyses or various tools to utilize. Social network like Facebook posts or Tweets can give different insights, such as belief analysis on your brand name, while sensory information will give you info about how a product is utilized and what the mistakes are.


90% of all data ever created, was developed in the past 2 years. From now on, the quantity of information in the world will double every 2 years. By 2020, we will have 50 times the amount of data as that we had in 2011. The large volume of the data is massive and a huge factor to the ever broadening digital universe is the Internet of Things with sensing units all over the world in all devices creating information every second.

If we take a look at planes they generate approximately 2,5 billion Terabyte of data each year from the sensors set up in the engines. Likewise the agricultural industry generates enormous amounts of data with sensors installed in tractors. John Deere for instance utilizes sensing unit data to monitor maker optimization, control the growing fleet of farming machines and help farmers make better choices. Shell utilizes super-sensitive sensing units to discover extra oil in wells and if they install these sensors at all 10.000 wells they will gather approximately 10 Exabyte of data each year. That again is absolutely nothing if we compare it to the Square Kilometer Selection Telescope that will create 1 Exabyte of information each day.

In the past, the creation of a lot information would have triggered serious problems. Nowadays, with reducing storage costs, much better storage options like Hadoop and the algorithms to develop meaning from all that data this is not a problem at all.

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Having a lot of data in various volumes coming in at high speed is worthless if that data is incorrect. Incorrect data can cause a lot of problems for companies along with for consumers. For that reason, companies require to guarantee that the information is appropriate in addition to the analyses carried out on the information are right. Particularly in automated decision-making, where no human is involved any longer, you need to be sure that both the information and the analyses are correct.