How To Win Over Your Rivals With Big Data Fusionex

Huge information has a great deal of prospective to benefit organizations in any industry, everywhere around the world. Huge information is much more than simply a lot of information and especially integrating various information sets will supply organizations with real insights that can be used in the decision-making and to enhance the financial position of a company. Prior to we can comprehend how huge data can help your organization, let’s see what huge information in fact is:

It is normally accepted that huge information can be discussed according to three V’s: Speed, Variety and Volume. However, I want to add a few more V’s to better describe the effect and implications of a well thought through big information strategy.


The Speed is the speed at which information is created, kept, analyzed and envisioned. In the past, when batch processing prevailed practice, it was normal to receive an upgrade to the database every night and even each week. Computer systems and servers required significant time to process the data and update the databases. In the huge data age, information is produced in real-time or near real-time. With the availability of Internet linked devices, cordless or wired, makers and devices can pass-on their information the minute it is produced.

The speed at which information is created currently is almost unimaginable: Every minute we upload 100 hours of video on YouTube. In addition, over 200 million emails are sent out every minute, around 20 million pictures are seen and 30.000 submitted on Flickr, practically 300.000 tweets are sent and nearly 2,5 million queries on Google are performed.

The obstacle organizations have is to deal with the massive speed the information is created and use it in real-time.

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In the past, all information that was produced was structured information, it neatly fitted in columns and rows but those days are over. Nowadays, 90% of the data that is generated by company is disorganized information. Information today comes in various formats: structured information, semi-structured information, disorganized data and even intricate structured data. The wide variety of data requires a different technique as well as various methods to save all raw information.

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


90% of all information ever produced, was produced in the past 2 years. From now on, the amount of data in the world will double every two years. By 2020, we will have 50 times the amount of data as that we had in 2011. The large volume of the information is huge and a huge contributor to the ever broadening digital universe is the Web of Things with sensing units all over the world in all gadgets producing information every second.

If we take a look at airplanes they generate around 2,5 billion Terabyte of information each year from the sensing units set up in the engines. Also the agricultural market creates huge quantities of data with sensing units set up in tractors. John Deere for instance utilizes sensing unit data to monitor device optimization, control the growing fleet of farming makers and assist farmers make better choices. Shell uses super-sensitive sensors 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 annually. That again is absolutely nothing if we compare it to the Square Kilometer Array Telescope that will create 1 Exabyte of data per day.

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In the past, the development of a lot information would have triggered severe problems. Nowadays, with reducing storage costs, better storage alternatives like Hadoop and the algorithms to produce significance from all that data this is not a problem at all.


Having a lot of data in different volumes coming in at high speed is worthless if that data is incorrect. Inaccurate data can cause a lot of issues for organizations as well as for consumers. Therefore, companies require to make sure that the data is correct as well as the analyses performed on the data are right. Particularly in automated decision-making, where no human is included any longer, you require to be sure that both the data and the analyses are proper.