INSUBCONTINENT EXCLUSIVE:
While the origins of the term are elusive, and even debated, big data is one of those concepts that many know about, yet it defies a simple
At the heart of big data, as the term directly suggests, is an extremely large volume of data
This is often drawn from diverse sources and even different types of data, which is then crunched through advanced analytic techniques which
hopefully pick out patterns that can lead to useful conclusions.Big data also infers the three Vs: Volume, Variety and Velocity
Volume refers to the size of the data, variety indicates that the datasets are non-homogenous, and velocity is the speed at which the
terabytes to zettabytes (1ZB is equivalent to 909,494,701TB, for the curious)
In addition to the size of these datasets, the data can be of different types: structured, semi-structured and unstructured, plus it can be
drawn from multiple sources.This does beg the question as to where all this data is being generated from
It comes from all types of places, including the web, social media, networks, log files, video files, sensors, and from mobile devices.The
latter are particularly important as most of us keep our phones with us and on 24/7, and they have an array of sensors, including GPS,
cameras, a microphone, and a motion sensor
Furthermore, the majority of smartphone use is not voice communication, but rather other activities, including emails, games, web browsing,
A large driver of big data is this mobile data, which gets generated at a breakneck pace.Data miningBut data without any analysis is hardly
worth much, and this is the other part of the big data process
This analysis is referred to as data mining, and it endeavors to search for patterns and anomalies within these large datasets
These patterns then generate information that is used for a variety of purposes, such as improving marketing campaigns, increasing sales or
The big data and data mining approach not only has the power to transform entire industries, but it has already done so.For example,
Trainline is a leading European independent train ticket retailer, selling domestic and cross-border tickets in 173 countries, with
approximately 127,000 journeys taken daily by customers
The company utilized big data to modernize its approach to travel, with a focus on improving the customer experience via innovation through
its app.The results are that now customers receive enhanced disruption notifications through the app
The firm has also innovated in terms of predictive pricing, which is able to predict when advance fares will rise from the initial
discounted rate, allowing passengers to purchase fares at lower prices.Big data has also been used in restaurants, and in particular the
goal of improving its understanding on the level of each individual location, with the overall goal of a better chain of restaurants.Through
preparing for a spike in demand when larger cars join the queue.Another big data innovation has been those digital menu displays that can
flexibly show menu items based on a real-time analysis of the data
The menus shift the highlighted items based on data including the time of day and the weather outside, specifically promoting cold drinks
when it is hot outside, and more comfort foods on cooler days
This approach has boosted sales at Canadian locations by a reported 3% to 3.5%.Health mattersThis big data approach has also been applied to
the office, to Electronic Health Records (EHR), which now have all patient information neatly entered into a computer database, ready to be
our understanding of disease causation and classification relevant for early translation and to contribute actionable analytics to improve
A significant challenge for hospitals is staffing, which has to be adequate at all times, with the potential to ramp up during peak
They used a dataset of 10 years of hospital admission records, down to a granular level of the number of admissions by the day, as well as
the hour of the day, and combined it with weather data, flu patterns, and public holidays.Using machine learning, they then honed their
algorithms for future trends to predict the number of upcoming admissions for different days and times
The result is that they now have an easy to use, browser-based interface for hospital administration, as well as clinical staff who are able
to forecast admission rates over the next 15 days, which is used to obtain extra staff at times when a larger number of admissions is
anticipated.With data, and in particular mobile data being generated at a ridiculously fast rate, the big data approach is needed to turn
this massive heap of information into actionable intelligence
increase quality and efficiency across a number of diverse industries via faster and better analysis of these disparate sprawling
datasets.ZhvP5Yr5BgKq4ZfhVc7aeW.jpg#
