What is big data analytics? Since, there has been no standard definition to understand the concept of big data and analytics. We define it as, the process of analysing the big data to uncover the hidden patterns, unknown correlations and information required for better decision making. Data has been exploding at an exponential speed which is beyond our imagination right from byte to kilobyte, megabyte, Gigabyte, terabyte, Petabyte, Exabyte, Zettabyte and to Yottabyte, which is the future for big data analytics. The data is being generated by Mobile devices, Sensor technology and networks, Social media and networks and scientific instruments. In other words, it is only us, who is generating data. But, how big is the data?, does all needs to be stored and analysed? and how to find which data points is relevant for decision making. To address the concerns, high-performance analytics becomes mandatory to distinguish the appropriate and junk data.
Being spent more than 26 years in technology industry, it was my privilege to look at the multiple revolution in technology. The technology has shifted from mainframe to mini computers to personal computer to mobile. The information source internet has shifted from web 1.0 like dumb web to web 2.0 interactive web to web 3.0 intelligent web. World has become borderless through ubiquitous social media. The famous brick and mortar company has been left behind by the more innovative virtual company like google, facebook, linkedin etc.
With evolution of telecommunication, the purchasing power and experience of consumer has enhanced. It would be most frustrating thing for food lovers or junkiesto ask for the menu on call from a restaurant manager and order food on call, without having information about dish ingredients, price and promotional coupons. It is equally annoying for manager repeating same menu for every customer on call, taking orders, managing billing and delivery details. Each of these scenarios seems like a headache if any single activity is missed out. The overhead can be simply reduced by setting up a mobile application for online food order services.
Big Data has got lot of significance in each vertical of industry, Big Data scientist areon high demand in industry to extract the meaningful insights from structured, unstructured and semi-structured data, as every vertical of the industry has the opportunity to explore the hidden insights from big data. Big data has been the buzz word of the industry since long, possessing a potential to provide competitive advantage.Industry leading players are analysing the big data to run their enterprise and provide their customer a better experience.Not new to know. Right?These facts generate curiosity to explore more about big data and gain insights. For anyone who think big data has a career growth opportunity and want to move in it only, here are the top three things to be considered before opting for a big data analytics course to enhance skills and knowledge.
What is big data analytics? Since, there has been no standard definition to understand the concept of big data and analytics. We define it as, the process of analysing the big data to uncover the hidden patterns, market trends, unknown correlations and other useful business information required for better decision making. The data has been exploding at an exponential speed which is beyond our imagination right from byte to kilobyte, megabyte, gigabyte, terabyte, petabyte, exabyte, zettabyte and to yottabyte, which is the future for big data analytics.
Have you ever realised legacy of your system could hold your organization back? If you have searched the term legacy system on the search engines, the answer would be the old methodology, out dated or needs replacement. Well legacy systems are providing descent output in various enterprises, but the technology used by the system is quite obsolete. Stakeholders are in discussion on this major point should we stay on the legacy system since it is serving the purpose or should we upgrade it with the latest market trend, which is digital?
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