{"id":2885,"date":"2020-04-17T07:30:24","date_gmt":"2020-04-17T07:30:24","guid":{"rendered":"https:\/\/www.goodcore.co.uk\/blog\/?p=2885"},"modified":"2025-01-14T11:02:23","modified_gmt":"2025-01-14T11:02:23","slug":"big-data-analytics","status":"publish","type":"post","link":"https:\/\/www.goodcore.co.uk\/blog\/big-data-analytics\/","title":{"rendered":"Understanding Big Data Analytics In Less Than 10 Minutes!"},"content":{"rendered":"

If you own a small, medium, or large-sized business, looking for custom software services<\/a>, this article is a must-read for you. Before we discuss big data analytics, though, it is important to understand the definition of big data.<\/span><\/p>\n

Have you ever wondered what exactly the difference between data and big data is? Both are collections of information, right? While that is true, a unique identifier for big data is its large volume. Big data is often used by businesses for obtaining customer insight. Such datasets are so extensive that traditional software like MS Excel is not agile enough to handle them anymore.<\/span><\/p>\n

Not long ago, businesses were using traditional database methods to store quantitative information on spreadsheets like Excel; a program using grids, tables, and columns to organize the storage of data.<\/span> Little did we know that the gradual inflow of data would accelerate at such a high speed that we would find ourselves in an explosion of information. It was around that time that we realized that the traditional data management systems were no longer capable of processing such massive volumes of information.\u00a0<\/span><\/p>\n

To further understand big data, let’s take a look at its three defining characteristics.<\/span><\/p>\n

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The 3Vs That Define Big Data<\/h2>\n

Volume, Velocity, and Variety are the 3Vs that best define what big data stands for.<\/p>\n

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The 3Vs of Big Data<\/figcaption><\/figure>\n

Volume<\/h3>\n

Whether or not a dataset is considered to be big data depends entirely on the volume of the information. Hence, this is the most important V defining big data. It is a simple rule: any information that is massive in volume is big data. For example, according to <\/span>Fortunly<\/span><\/a>, there are around 1.5 billion WhatsApp users in 180 countries! Just imagine the huge volume of data being generated on a daily basis. As traditional data management can no longer take the burden of this colossal amount of information, we rely on new methods (like Hadoop) to store and analyse the information.<\/span><\/p>\n

Velocity<\/h3>\n

In his <\/span>recent letter to Amazon shareholders<\/span>, Amazon CEO Jeff Bezos emphasized how \u201cspeed matters in business.\u201d Velocity, which refers to the high speed at which data is received, helps businesses make the most timely decisions. Tesco, Sainsbury, and Marks & Spencer are some examples of retailers using <\/span>big data analytics in retail <\/span>and leisure industries.<\/span><\/p>\n

If you were the owner of a retail business, your biggest concern would be to know which items go out of stock within minutes. For retail businesses, predictive analytics is practised on a large scale, which means they predict future consumer demand with present data available. Retailers can observe customer behaviour to predict what product should be stocked in future to meet the demand.<\/span><\/p>\n

Restocking items based on <\/span>big data predictive analytics<\/span> will help retailers continue with product sales. However,\u00a0 you will have to get your skates on to match the speed at which data is pouring in. <\/span>Big data analytics tools<\/span> like Apache Spark and Kafka have solutions to match the velocity of data for both <\/span>real-time big data analytics<\/span> and predictive data analytics.<\/span><\/p>\n

Variety<\/h3>\n

Big data<\/span> always features different varieties of data. Social media alone has a diversified range of data, such as text messages, videos, audio files, pictures, and much more. When working with <\/span>big data, it is important that you<\/span> organise and structure the variety of information for later analysis.<\/span><\/b><\/p>\n

Variety of data refers to two basic categories:<\/span><\/p>\n