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big data analytics (3)


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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7 familiar myths regarding Big Data analytics

Big Data analytics is in the buzz since a while, but people still have various misconceptions about it and the way it functions to assist you in transforming your business goals. Irrespective of the industry you are into, your company processes a huge amount of data raw data that can be tapered to a more organized form.

Let’s have a look on the common myths about Big Data:-

1. Big Data means lots of data

When you hear Big Data, instinctively an image of loads of data floats in your mind. Big Data is not all about having a huge bank of information which is hardly of any use, it means having quality data which is useful for your business. Having a huge data bank means, it is prone to have redundant and duplicate entries. Big Data analytics helps you streamline the right data, irrespective of the quantity.

2. Big Data is extremely essential

Having raw and unprocessed data is practically of no value for an organization, unless it is sorted and effectively organized to meet the specific nee

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