<\/span><\/h2>\nBig Data refers to the mammoth amount of data that businesses nowadays receive from different sources on a daily basis.<\/strong> In fact, 2.5 quintillion (that\u2019s 18 zeros by the way!) data bytes are created on a daily basis.<\/p>\nThe sources of Big Data can be:<\/p>\n
\n- Mobile devices<\/li>\n
- Smart devices<\/li>\n
- Sensors<\/li>\n
- Social media<\/li>\n
- Transactions, etc<\/span><\/li>\n<\/ul>\n
Big Data can be unstructured, semi-structured, or structured. <\/strong>It provides huge benefits to businesses, but only if it\u2019s treated the right way.<\/p>\nThe treatment of your data begins with its cleaning followed by processing. We\u2019ll talk about the first phase here.<\/p>\n
<\/span>Source of Errors in Big Data<\/span><\/h2>\nErrors have a way of creeping into even a foolproof system. The errors in Big Data or, in fact, any data, may come from a variety of sources.<\/p>\n
The most basic cause of inaccuracies in data<\/span> is human error<\/strong>. For instance, while filling a survey form, a customer may enter his\/her name with incorrect spelling. This may lead to problems when the feedback is integrated into an existing customer profile database.<\/p>\nThere\u2019s always a possibility of having fake entries<\/strong>, or even multiple entries, <\/strong>which may also create problems in your data analysis.<\/p>\nFinally, you can also create errors in your data by condensing<\/strong> it. This occurs more commonly when dealing with a database of product reviews.<\/p>\n<\/span>Why Is Big Data Cleanup Necessary?<\/span><\/h2>\nUS businesses lose $600 million every year because of dirty data. Having clean data takes your revenue up by 66<\/em>%!<\/strong><\/p>\nNot to mention the fact that customers will be more willing to believe you if you have a reputation of maintaining clean data records.<\/p>\n
Know that having clean Big Data can save you time and money. It can build you a good reputation in the market and trust among customers.<\/strong><\/p>\nThe major benefit of having clean Big Data, however, is better decisions. If you\u2019re using some made up or unreliable data for your analysis, you\u2019ll get only invalid conclusions<\/strong>. As the saying goes, \u201cgarbage in, garbage out\u201d.<\/em><\/p>\n<\/span>What Forms of Errors Appear in Big Data?<\/span><\/h2>\nThe list of errors that you will have to face while fixing up your data is endless and ever growing. However, typical errors are:<\/p>\n
\n- Aliasing <\/strong>– When different entities are merged, perhaps because of the same tag<\/li>\n
- Incorrect entries <\/strong>\u2013 Either intentional or unintentional<\/li>\n
- Missing entries <\/strong>\u2013 When data is lost in the system due to glitches, etc.<\/li>\n
- Multiple entries <\/strong>\u2013 When the same information has different tags<\/li>\n<\/ul>\n
Data cleaning is a messy job.<\/strong> You can always hire someone to do it for you. After all, you need to have a clean source of information to take better, more informed business decisions.<\/p>\nBut bear in mind that no one can ever know the dirt in your data like you do. You\u2019re the only one who can truly identify the clean from the dirty. That\u2019s because you know what it should look like. So, be brave and do it!<\/p>\n
Can you draw good enough conclusions from raw data? Or is it necessary to have clean data? Share your opinions with us!<\/p>\n","protected":false},"excerpt":{"rendered":"
Big data is of no use to your business if it\u2019s unclean and unreliable. Learn why you need to fix it up!<\/p>\n","protected":false},"author":81,"featured_media":6547,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[744],"tags":[745,1382],"class_list":["post-6546","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","tag-big-data-2","tag-data-cleaning"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.esds.co.in\/blog\/wp-json\/wp\/v2\/posts\/6546","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.esds.co.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.esds.co.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.esds.co.in\/blog\/wp-json\/wp\/v2\/users\/81"}],"replies":[{"embeddable":true,"href":"https:\/\/www.esds.co.in\/blog\/wp-json\/wp\/v2\/comments?post=6546"}],"version-history":[{"count":3,"href":"https:\/\/www.esds.co.in\/blog\/wp-json\/wp\/v2\/posts\/6546\/revisions"}],"predecessor-version":[{"id":8999,"href":"https:\/\/www.esds.co.in\/blog\/wp-json\/wp\/v2\/posts\/6546\/revisions\/8999"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.esds.co.in\/blog\/wp-json\/wp\/v2\/media\/6547"}],"wp:attachment":[{"href":"https:\/\/www.esds.co.in\/blog\/wp-json\/wp\/v2\/media?parent=6546"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.esds.co.in\/blog\/wp-json\/wp\/v2\/categories?post=6546"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.esds.co.in\/blog\/wp-json\/wp\/v2\/tags?post=6546"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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