The twenty first century will be known as the ‘Age of data’. Massive amounts of data is being generated, collected and analyzed all the time.
Internet generates huge amounts of data every year. There are billions of pages on the web. Millions of emails are sent every day. A billion pieces of content (posts, photos etc.) are shared on Facebook every day. There are close to 2 million tweets every day. And this data flood is not restricted to just the Internet.
In astronomy, some of the large telescopes can produce 1 GB of information every second, that is 85 TB of data every day. That’s a lot of data.
Businesses are another source of data generation. Every business transaction creates new data. When a consumer buys something from a store, when a subscriber makes a call, when a customer applies for a credit card – data is generated in all of these cases. Large companies like Wal-mart or AT&T store hundreds of terabytes of information generated through their business. Banks, insurance companies, retailers, telecom companies, e-commerce sites, and pharmaceutical companies are all examples of businesses generating and using large amounts of data.
This is a far cry from, say, 30 years ago. In 1981, a gigabyte of storage space would cost you close to INR 3 crores. No business would even dream of having terabytes of information at this cost. Today, most college students have hard disks with storage in terabytes, available for a few thousand rupees. While people focus on processing speeds and the fall in cost of processors, cost of data storage has seen an astonishing fall in the last 3 decades. This has truly enabled the data revolution.
It is obvious that there is a huge amount of data being generated and stored. But what is the use of this? Obviously, most of this data is never going to be seen by the human eye.
This is where analytics comes in. Analytics, with its blend of complex statistics and advanced computing power, has the power to churn through all of this data and generate insights – pieces of useful information that were previously unknown or hidden deep in the jungle of data.
“How to build a successful career in analytics” – This article explains what you need to do to build a successful analytics career.
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“Career in analytics in a KPO” explains the typical career path in this industry.
“10 most popular analytic tools in business” provides a comparison of the most widely used tools in the analytics industry.