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Price discrimination and downward demand spiral are widely used analytical concepts/practices in the Airlines and Hospitality industries respectively, long before the term Big Data Analytics was even coined. Incidentally, these concepts have been taught in global elite b-schools for decades. So, how come Analytics, which has been there in practice for decades experience a meteoric rise suddenly? To answer this question, we need to get the Big Picture. Given below are key factors that led to huge buzz around analytics today.

  • Proliferation of Data Sources – Every day we create 5 quintillion bytes of data. This comes from digital footprints left on social media platforms, IoT sensors, wearables, transactions to name a few. Interesting fact is that only 1% of data collected is ever analyzed. To put into perspective all that innovation and insights driven by analytics are from analyzing just 1% of the data collected globally.
  • Change in Customers’ expectations – Today connected
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The advent of sharing economy has brought a sea change in the way urban populace commute locally. The Ubers, Lyfts and many other local players have made taxi riding convenient, affordable and safe. These rides have emerged as a strong alternative to the public transport clocking millions of rides per month in some cities. The emergence of hyper-local delivery models to optimize the supply chain has also led to a large number of daily trips by these vehicles.

These developments have mandated the installations of either standalone or smartphone app-based GPS devices to keep track of and better regulate these rides and a fleet of taxis. These GPS systems spew a ton of data generating up to GBs of data per second. With the automobile & technology experts predicting that self-driving cars would replace human-driven cars in no more than a decade, the volume and velocity of GPS data is only set to increase. With that context in mind, it becomes imperative to understand the GPS data and

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R + Hadoop = Data Analytics Heaven

Hadoop (MapReduce where code is turned into map and reduce jobs, and Hadoop runs the jobs) is the most well known technology used for "Big Data" because it allows an organization to store huge quantities of data at very low costs. R is a programming language and software environment for statistical computing and graphics. Put the two together to provide easy to use R interfaces for the distributed computing Hadoop environment and you have one king-hell data crunching tool for serious data analytics. RHadoop is a small, open-source package developed by Revolution Analytics that binds R to Hadoop and allows for the representation of MapReduce algorithms using R - allowing data scientists access to Hadoop’s scalability from their favorite language, R. It allows users to write general MapReduce programs, offering the full power and ecosystem of an existing, established programming language.
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