{"id":1316,"date":"2016-01-25T11:18:25","date_gmt":"2016-01-25T11:18:25","guid":{"rendered":"http:\/\/localhost\/blog\/?p=1316"},"modified":"2018-08-29T08:37:43","modified_gmt":"2018-08-29T08:37:43","slug":"mistakes-using-big-data","status":"publish","type":"post","link":"https:\/\/www.kolabtree.com\/blog\/mistakes-using-big-data\/","title":{"rendered":"Five big mistakes to avoid while using Big Data"},"content":{"rendered":"

Trying to figure out how you can employ Big Data to grow your business? Make sure you\u2019re not jumping onto the Big Data bandwagon unprepared. The best of technologies and systems can fail if not implemented properly. Big data experts<\/a> shed light on a few critical aspects to look out for.<\/p>\n

We\u2019ve tried to enlist some important pointers below that you need to keep in mind while dealing with Big Data. Avoiding these five big mistakes will help you avoid some Big Data blunders.<\/p>\n

1. Choosing the wrong sources:<\/strong> Focusing on the wrong source can lead to huge misunderstandings and wrong conclusions. Big Data can be garnered from a multitude of sources such as website analytics, social media data, sensor data, machine log data, media, business apps, and the Internet of course! So, it\u2019s easy to get drowned in this sea of data. One of the common mistakes is to choose a dataset that is easily available and doesn\u2019t require cleaning up. However, it is of critical importance that you select the right source depending on what you need to solve for, even if that dataset requires a lot of digging or cleaning up. This also brings us to the next important aspect.<\/p>\n

2. Not defining your goal:<\/strong> Even before you begin skimming through your data sources, you need to zero down onto what exactly you\u2019re looking for. Unless you focus on what you\u2019re trying to solve for, you wouldn\u2019t be able to choose the right resources. When the goal is not clearly defined, there is a tendency to use the most easily available data. This, in turn, leads you to swimming aimlessly in vast amounts of data without any tangible results.<\/p>\n

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

3. Ignoring data quality:<\/strong> The next most important aspect is ensuring that you have high-quality data. You might have vast amounts of data that is from the right source and aligned with your goal; however, this does not undermine the need for that data to be accurate and consistent. Large companies actually employ people just to clean up vast amounts of data to ensure consistency and uniformity.<\/p>\n

4. Not categorizing data:<\/strong> Unless data is categorized properly in the initial stages, trying to sort data at a later stage to get micro-level insights can be quite cumbersome. Categorize your data by products, departments, geographies, etc. to ensure that you can readily slice your data as per your requirements. This will give you the advantage of drilling down deep into Big Data to get better insights with a lot of ease.<\/p>\n

5. Not moving to the cloud:<\/strong> Lastly, Big Data obviously requires huge amounts of storage space, which in turn involves huge infrastructure costs. Depending on the nature of your business and the need for Big Data as a critical growth tool, Big Data implementation can have a huge impact on your business. One wrong move and you might find yourself struggling with basics instead of leveraging the benefits of Big Data. Hence, moving your data to the cloud is one of the safest options that helps you optimize infrastructure costs and scale up or down depending on how things seem to be going.<\/p>\n

Big Data is here to stay. So, ensure that your organization implements it with the right foresight to reap early dividends and avoid blunders. Hiring experienced data analysis experts<\/a> typically helps in avoiding such mistakes right at the outset.<\/p>\n","protected":false},"excerpt":{"rendered":"

Trying to figure out how you can employ Big Data to grow your business? Make sure you\u2019re not jumping onto the Big Data bandwagon unprepared. The best of technologies and systems can fail if not implemented properly. Big data experts shed light on a few critical aspects to look out for. We\u2019ve tried to enlist<\/p>\n

Read More<\/a><\/div>\n","protected":false},"author":4,"featured_media":959,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[398,433],"tags":[119,18,175,72,325,174],"acf":[],"yoast_head":"\nFive big mistakes to avoid while using Big Data<\/title>\n<meta name=\"description\" content=\"Avoid these five major mistakes that are commonly made while using Big Data.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.kolabtree.com\/blog\/mistakes-using-big-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Five big mistakes to avoid while using Big Data\" \/>\n<meta property=\"og:description\" content=\"Avoid these five major mistakes that are commonly made while using Big Data.\" \/>\n<meta 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