{"id":6318,"date":"2017-12-08T18:01:55","date_gmt":"2017-12-08T12:31:55","guid":{"rendered":"http:\/\/www.allerin.com\/blog\/?p=6318"},"modified":"2017-12-08T17:06:13","modified_gmt":"2017-12-08T11:36:13","slug":"5-biggest-myths-about-big-data","status":"publish","type":"post","link":"https:\/\/www.allerin.com\/blog\/5-biggest-myths-about-big-data\/","title":{"rendered":"5 biggest myths about big data"},"content":{"rendered":"

\"\"<\/a><\/p>\n

In this blog post, we\u2019re going to be talking about five of the biggest myths about big data.<\/p>\n

 <\/p>\n

\"\"<\/a><\/p>\n

Big data myth #01. It\u2019s big<\/h1>\n

Big data is considered to be just too big. Big data does not mean that all the data that is being stored in a system has to be big. The fact that big data is being collated from several sources at the same time, does not necessarily mean it\u2019s too big to handle; it\u2019s rather diverse.<\/p>\n

Big data myth #02. It\u2019s all good<\/h1>\n

Big data contains good data is a big time myth. The information contained in big data is not necessarily good data, as it is coming from several sources \u2013 trusted and not so trusted.<\/p>\n

Big data myth #03. The Analysis is costly<\/h1>\n

With big data, everyone thinks of big data analytics, and then comes in the notion that analytics is extremely costly. With improvisations in technology, big data analytics has improved over time and the cost for it has come down drastically.<\/p>\n

Big data myth #04. Only for IT<\/h1>\n

During its early days, big data was implemented in IT related fields only. People still have that image in their minds. Well, over time big data has evolved and it has touched more than one field<\/a> and is currently being used in different industries.<\/p>\n

Big data myth #05 Everyone\u2019s using it<\/h1>\n

People hear so much about big data all the time that they feel almost everyone\u2019s using it. Thereafter, the fear of losing out on competition arises, and businesses adopt big data without thinking through.<\/p>\n

<\/h6>\n

To not get disappointed and invest wisely in big data, differentiate between the features and myths that follow big data.<\/p>\n","protected":false},"excerpt":{"rendered":"

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