Big Data



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What is Big Data Analytics?

Suppose, you have a puncture in your water pipe in the yard. What would you do? probably pick up your bucket to collect water and few sealing material to stop the leaking. Then after a while, the puncture gets bigger and the sealing material is not enough to fix, you would now call a plumber to fix it, who get bigger tools with himself. Now just imagine instead of water pipe there is a hole in your yard out of which millions of liters of water is coming out every second. In this case, neither your sealing material nor your plumber can help you out. In order to rescue the town from getting flooded, bigger body, civil engineers who can handle both volume and velocity will come into play. Now if this water was actually data then you would need data scientists to help you with this. Here, the water is Big Data and operations of data scientist is Big Data analytics.
Replace water with data. This is what is called Big Data

Structure, Speed & Size

Structure is one component of Big data. Earlier things were different and we mainly relied on structured data like the one where we can classify data into tables and charts and neatly organise it like sales transactions by customers or  region, etc while the less structured data, such as video content, text files, photographs, media etc. was largely ignored. With big data, we today have the ability to analyse and utilise a large diversity of data including biometric data, written text, photographs, spoken words, even the tone in our voice and video content.


Speed is another component. Every minute we click almost 2 million likes on Facebook, send over 300 million emails, up-load 200K photos to Facebook, add 100 hours of video to YouTube and send almost 300K tweets and not only this, if we talk about daily searches on Google or any other search platform, it would add up to almost 5 billion.


According to IBM, about 90% of the data in the world today has been created in the last two years alone and that's because every day we are creating around 2.5 quintillion bytes of data. This data congregates from everywhere: social media posts,purchase transaction records, cell phone GPS signal, digital pictures and videos and sensors used to gather climate information to name a few. This is what we mean by the big data. It has created a flood of data that when categorised,analysed and organised would reveal habits and trends about us and of society at large. Size is just one component and its not just about the size of data but also refers to the size of available data to access methods, analyse and manipulate technologies to make sense of that data.


The increasing data is presenting both opportunities and problems. The pros being, having a customers complete data would let companies better customize their marketing efforts and products in order to build highest level of satisfaction with the customers. Companies collect huge amounts of data to get a richer and deeper analysis of everything. The cons of big data would be the overload and noise that is brings. With big data, your personal information gets on sale. Structured data is easily sorted and stored compared to the unstructured data because it consists of videos, emails and text documents that require more intricate techniques to be applied to make it more useful. And it also includes other forms of data, such as information from search engines and fitness gadgets that are unregulated and have addresses and identities attached.

Big Data into practice

Data is the new coal. Big Data has endless applications linked to it and just to cite, there are few examples. Ventures use big data to target and better analyse their users by linking together data from their social media accounts and also their own transactions as well. Industries track and analyse their supply chain delivery routes to optimise their processes, and combine this data with live traffic updates. Big data is driving a business worth millions when it comes to healthcare industry. It is used to predict diseases before the emergence of symptoms, optimise treatment and find new cures for deadly diseases.  To prevent cyber attacks, foil terrorism, predict criminal activity and detect credit card fraud, police forces and security agencies are using big data.

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