This is an extension
to my earlier article [4] on big data. In this short article I will try to summarize
the authors [1] discussion on the business implications of big data. On a broad
scale there are 3 types of big data companies that have cropped up and are differentiated
by the value they offer.
First set is the data oriented companies or the Data holders:
These are the
companies that have the data with/without doing much on it. Some companies’
main focus is to collect the data as much as possible. They are collecting
humongous amount of data. Internet companies are collecting data about
everything that happens at their site. From the content you viewed to the
places you moved your mouse.
-Facebook collects
data about people, their relationships, their likes, dislikes, photos, their
locations, and posts.
-Amazon tracks the
books that you browsed but did not buy.
-Companies like VISA
and MasterCard collect all the credit card transactions for the different
banks. This data is then licensed to other companies to make some sense out of
it.
Second set is the Companies/Individuals
providing the skills to Analyze data:
These are the
consultancies, technology vendors and analytical providers who have the special
expertise to perform the analytics. They generally don’t have the data of their
own but clients provide them the data to work on it as a project. They may also
have not thought about the usages of the data on their own. For e.g. Teradata performs
analytics on WalMart retail data and make predictions.
Third set is the companies/individuals having the big data
mindset to draw wisdom from data. They have the attitude to think about unique
ideas of using data in ways that will unleash the potential value. These are
mostly the startups like FareCast, PriceStats, SkyScanner. These
companies might not even be asked by someone to look into the data. They may
have done this all by their own using the open data (FlyOnTime) or by licensing
the data (FareCast)
It is this mindset that makes the data to be used in ways other than
what it was intended for.
-The mobile geo locations are being used to display location specific
ads to the user.
-The geo location is also being used in understanding the traffic
congestions. The number of mobile users in the locality who are moving may well
mean people driving vehicle. If the coordinates of mobile phones from a
particular location is not changing within certain time it could mean traffic
congestion. Google use this method to show the real time traffic updates on
their Map service
-The traffic data can also be used to predict the local economies,
retail sales. In case the traffic over a period of few days is lean in the
business district of the city that means people are either not going to the
office or they have suddenly become green.
-If the number of cars near the retail store is decreasing, that might
be an indicator for lower sales and might be a tip for stock investors. More
cars correlate to better sales. But this is a correlation that somebody has to
work for to understand.
-The income levels of the people in few African localities has been predicted
based on the amount people are spending on charging their prepaid mobile phones
In todays
world we have the data in abundance, the big data technologies are also
affordable and easily available, but the scarce part is the knowledge to
extract wisdom from the data. The uses of big data are limited just by our
imaginations [2]. In many areas we might see a demise of an expert whose
decisions are mainly based on year-long experience, whereas newly emerging data
analysts who often come from fields outside of the area analyzed will take over
[3].
References
- Big Data: A Revolution That Will Transform How We Live, Work and Think. Viktor Mayer-Schnberger and Kenneth Cukier, John Murray Publishers, UK,2013
- https://www.youtube.com/watch?v=bYS_4CWu3y8
- Thomas Dreier, Book Review: Victor Mayer-Schönberger/Kenneth Cukier, Big Data, 5 (2014) JIPITEC 60, para1
- http://rohitagarwal24.blogspot.in/2014/10/big-data-pragmatic-overview.html
