Massive Big Data Myths Busted!

by March 13, 2018 1 comment

Big Data is the technology that has revolutionized organizations and businesses around the world. If there is one technology that has given birth to transformation, Big Data it is. The lucrative approach of a Big Data Analytics solution to provide you insights about your market, your customers, their likes and dislikes, along with the future predictions, has promised businesses growth and efficiency.

Organizations want to transform their business processes and operations, and they are no more sure than ever about their strategies and plans concerning Big Data because they know the massive nature of the returns the technology drives.

With all the facts and information about Big Data, there are numerous myths regarding what works and what does not in a Big Data strategy. And today, we bust the top myths surrounding Big Data, separating the wheat from the chaff!


•  It is everywhere – You must hear this in and out, everyone is utilizing the potential trapped inside the Big Data technology, and that it is fruitful for everyone. Well, it is not. For the majority of companies, Big Data is just a toy and their far-off ambition. Most companies talking about Big Data today are doing just that- talking! There is a tiny percentage of companies that are actually putting the technology to use and deriving growth from it.


•  You need all of your data – The biggest myth about Big Data is that all of the data that exists with a company is important. This is far from being true. You don’t have to hold on to the last smidgen of data left behind, but the true value of Big Data and Analytics lies in using it real-time for the real-world applications, rather than storing it, so it becomes historically antique.


•  Big Data is for big businesses – This is a huge myth and needs some busting left and right. Big Data is not an expensive entity that only the vast businesses can afford. The quality of data wins over its quantity, and therefore, small and medium enterprises can equally leverage the immense benefits of Big Data in their organizations. Precisely what data needs to be used is more relevant than how much of data needs to be used. This factor makes it possible for smaller companies to shun the need for expensive data storage systems and concentrate on real-time data analytics.


•  Big Data is only for newer businesses – Be it a new business that was put up yesterday, or an age-old business line, Big Data can prove to be a gem for both. The value of Big Data only lies in the way it is put to use, and not on the age of the business. If you can find out a use case for Big Data in your business, you may as well use it.


•  Clean data is essential – Many organizations spend large amounts of time in making sure that their data is clean. This data cleaning process finishes, and their data is no more relevant. It is important to realize that time is of the essence. Rife data is essential over clean data. It is high time companies heed to this fact.


•  Analytics means Big Data – It is not crucial that every analytics effort relates to Big Data. Some use cases are better off when carried on smaller chunks, or on the complete data only through matching and analysis. For more complex patterns and insights, Big Data is the answer, and not for everything that surrounds data.


While these myths have been cleared out, there are many others out there, which confuse companies into believing they cannot effectively leverage Big Data for their operations. If you are of the same opinion, lay out a Big Data strategy, conduct some ground research, and you will realize Big Data is not that difficult.

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  1. CyberH
    #1 CyberH 14 March, 2018, 20:42

    James, nice insight into these big data myths. The HPCC Systems open source offering provides a single platform that is easy to install, manage and code too. Their built-in analytics libraries for Machine Learning and integration tools for great BI capabilities make it easy for users who do not hold a PhD degree or carry a title like “Data Scientist” to easily analyze Big Data. For more info visit:

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