Report: Uses of Big Data in Business Organizations and Google

This report delves into the applications of big data within business organizations, with a specific focus on Google's data handling practices. It begins with an introduction to the concept of big data, defining its characteristics and potential to improve business operations and decision-making. The report then outlines its objective and scope, which is to explore the uses of big data in Google. A literature review provides context by discussing how companies are using big data to understand customers, personalize recommendations, optimize product development, assess risks, and improve operational efficiency. The report highlights Google's mastery of recommendation engines, real-time simulations for product design, and the use of telematics. The report also discusses how big data can be used to monitor machines remotely and government initiatives to encourage the use of open data. The report concludes by emphasizing the vast opportunities that big data offers across various industries, including logistics, and its impact on competitive advantage. It also underlines the advantages of using big data in business organizations and its potential for the future.

Uses of Big Data in
Business Organizations

Table of Contents
INTRODUCTION...........................................................................................................................1
Project objective...............................................................................................................................1
Project Scope .................................................................................................................................1
Literature Review ............................................................................................................................1
CONCLUSION................................................................................................................................6
REFERENCES ...............................................................................................................................7

INTRODUCTION
Big data refers to a term for data sets that are very large and convoluted. This can not be
deal properly by the conventional data processing application software. It is a phrase that defines
it as large or massive volume of both structured and unstructured data (Simon, 2013). In
different companies, the volume of data is moving too fast or increasing at faster rate and which
is exceeding the current processing capacity. Big data has the potentiality to assist enterprises in
improving their operations by making it more faster and helping in making intelligent decisions.
Project objective
This project is based on the uses of Big data in Business organizations. The objective of
this report is to explain all the uses of big data in Google. It tells how Google is dealing with
such a large volume of data that is being gathered in their corporation.
Project Scope
This project is explaining the small as well as large use of the Big Data in the company
like Google. This project will help company in finding out benefits of Big Data for them. By this
way they can easily finds out how Big data is essential for them.
Literature Review
According to the (Simon 2013), the volume of data is not fixed in any enterprise and the
traditional software and techniques are not capable for dealing with such a vast volume of data.
So, a new concept is emerging that is known as Big Data. It is used by approximately all
companies for handling their large volume of data. Minelli, Chamber and Dhiraj (2012) say that
in previous years organization were using focus group and questionnaires for finding out the
number of customers and where are they located. But the says that Davenport (2014) with Big
Data this is not needed any more. Big data are allowing enterprises to entirely represent or map
the DNA of its users. The selling can be increased effectively by knowing about the customers
well. This can also creates privacy issues if not being executed carefully.
A good example of that is the case when there is target to find all the teenagers who are
pregnant before the father even knew. The Cavoukian and Jonas ( 2012) say that if firms will
make sure that secrecy of consumers are not vulnerable, then Big Data can effectively present
personalised insights about each and every user. By using the inter related information of social
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media, mobile data, network analytics and other Big Data analytics, it is achievable or possible to
precisely know who each user is, what they are demanding, when they need that and all in real
time. The benefit of really knowing the consumers means they can supply recommendations or
display advertisements that are created as per the need of users. Google has mastered this to
perfection, as the suggestion it provides its users are not a fortuity. The recommendation engine
that is used by Google is based on what a customer has purchased in past, which things they have
in their imagery shopping cart, things that they have liked and rated and what different other
users have seen and purchased. The algorithm that organization uses are allows them in giving
each and every user a different web page. This strategy give its best result as the corporation
reported that their income has increased by 30 % in relation to last years.
As per the view point of Schmarzo (2013) in previous years, companies consist of panels
of users from whom they discuss about their demands or wishes. They used to show them their
manufactured goods that were completed and by this they determines their thinking about that.
But the Agarwal and Dhar ( 2014) say that if they do not like it then corporation seems to be in
trouble. Big Data analytics can assist in getting good understanding of what their consumers are
thinking about the goods. Via sensing or listening on social media and blogs about what civilians
say about their products, this is providing more information about the likes or wishes of users in
relation to the conventional questionnaires. Specially if this is being measured in real time,
enterprises can act upon the several possible problems instantly. This not only tell about the
sentiments of products but also describes it according to different demographic groups that are
present in different geographical areas.
Chen, Mao and Liu (2014) says that Big data are also allowing organizations to run real
time simulations, hundreds at a time, for testing a improved or fresh product digitally. Each and
every designs can be twinged a little bit and the program of simulation are combining all those
minor tweaks that are showing development into cardinal product. According to the Chen and
Zhang (2014) offline goods such as cars, can also be developed by using the Big data, if it is
known that how they are operating and performing on duty. Ford has actually a laboratory in
silicon valley in order to improve their cars. For improving their cars in terms of quality, fuel
consumption, security and emission, Ford aggregates information from more than 5 million cars
that are having car sensors as well as remote application management software. All data is being
analysed in real time that allows engineers in notifying problems in real time, understanding how
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the cars are responding in different road and weather conditions and any other factors that impact
the cars.
As per the view point of Sagiroglu and Sinanc (2013), today the determination of risks
that is faced by companies is very necessary. For determining the risk of a expected consumer or
supplier, they are being placed in certain categories and each with its own levels of risk. It is
more frequently than desired a user or supplier is placed in an incorrect category and thereby the
attained risk profile will be wrong. A too high profile of risk is not so noxious, apart from lost
money, but a very low risk profile would seriously damages the corporation. On the contrary, the
De Mauro, Greco and Grimaldi (2015) say that with Big data it is very simple to identify a
category of risk for each and every individual user or supplier which is based on all of the
information that are taken from past and present in real time.
Specially, in the business of insurance, predictive analysis is know how much revenue
will cost in future. They are wanting to determine the correct customer for the correct product at
correct cost and the lowest risk so that they can be assured to reduce claims cost and fraud. Katal,
Wazid and Goudar (2013) say that by using Big data techniques such as pattern recognition,
social data accumulation, text analysis, regression analysis and sentiment investigation, a 360
degree views of expected users are created. Therefore, this holistic and updated image of a
consumer can minimise risks significantly. Such a 360 degree investigation can of course also be
used in determination of expected risk of a new or current supplier. According to the Simon
(2013) ventures have used split test and A/B tests for some times now in determining the best
layout for their consumers.
On the contrary, Minelli, Chamber and Dhiraj (2012) say that with Big data this process
would alter forever. Web metrics could be analysed continuously and in real time. This is
allowing the enterprises to have a fluid system whereby looking, feeling and layout are changed
for reflecting multiple impacting factors. It would be possible to provide each individual visitor a
webpage specially tailored as per his or her wishes and requirements at that exact moment. A
returning users may see a different webpage after a week or a month depending on the personal
requirements of his or her for that moment. Davenport (2014) says that Big Data can also laid its
effect on the costs offered. Yield management in electronic commerce can potentially take on a
completely new meaning with Big data. Orbitz experimented with this are already presenting
Google users more costly hotels than PC users. By using algorithm it will also become possible
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to respond to events in market or acts of challengers in near real time and will modify prices as
per that. Ventures that are utilising Big data to personalize online offering towards the
personalised requirements are utilising or enjoying the increase in sales and profits.
The Cavoukian and Jonas( 2012) say that with Big data, it is possible to monitor
machines from great distances and can easily determine how they are working. Using telematics,
each different parts of a machinery can be visualised in real time. Data can be transfer to the
manufacturer and stored effectively for real time analysis. Each vibration, noise or faults can be
analysed automatically and when an algorithm is detecting any deviation from the normal
operation, the service support can be given warning about that. The machine can also program
automatically for its maintenance at a time when that machine is not in use. When the engineer
arrives to fix the machine , he knows exactly what should be done to all available information. A
good example is the building or construction business that are already using telematics in order
to develop or improve the efficiency of operations.
On the contrary Schmarzo (2013) says that more and more government are stimulating
corporations for making use of massive amounts of open data which is integrated by the
government in some way or another. European union has organized open data challenge in the
year of 2011. This was the biggest competition of open data for stimulating the starts ups that are
coming up with new innovation based solutions using the large amounts of open dat that are
created by the governments. The Dutch government are focusing actively to stimulate the re use
of open cultural data sets and organizing hackathons in order to come with new solutions.
As per the view point of Chen, Mao and Liu (2014), corporations are discovering unmet
needs of consumers using Big data. By performing pattern or regression analysis on the data of
companies, they will determine the demands and wishes of their customers which are not known
by them. Big data also assist in finding corporations where they should market a product firstly
or where have to place their goods. On the contrary, the Chen and Zhang (2014) say that Big data
also helps in giving better understanding of the competitors and knowing where they are
standing. Using Big data analytics, algorithms can assist in finding out the alteration in the
costing done by challengers and as a result enterprise can change their pricing automatically by
keeping that competition. Firm can also adopt new goods or services and their promotions by
monitoring on other action of the competition. But it should not be neglect that which is done the
corporation are available as open data so that can be tracked. According to the Provost and
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Fawcett (2013), analysis of all data within the firm can made that more organized. Specially, the
logistics industry are becoming more economic by using the new source of Big data that are
available in the supply chain. Electrical On Board Recorders present in the trucks explains that
where they are, how accelerated they are driving, where are they driving, etc. Sensors and RF
tags present in the trailers and distribution assist in on loading and off loading trucks more
expeditiously and combining the conditions of roads, information of traffic and weather
situations along with the locations of clients can considerably helps firm in saving their time and
money. Of course the above use cases are only a small part of the massive possibilities of Big
data, but it is also showing that there are various opportunities where Big data can be used.
The De Mauro, Greco and Grimaldi (2015) say that Google is really a mountain of data
and a group of tools for working with it. It has acquired from a index of websites to a key hub for
real time data feeds. This is just close to anything that can be measured. Big Data analytics
means utilising tools that are created to sort through and making sense of this data comes into
exist whenever a research is carried out. The Google algorithms are running complicated
algorithms that are designed for matching the queries client have entered with all the available
data. This will attempt to identify whether the customers are demanding facts, news, person or
statistics and are pulling those data from the proper feed. Where as the Katal, Wazid and Goudar
(2013) say that for more rigid operations for instance, translation, Google are invoking other
inbuilt algorithms that are made on the basis of Big Data analytics, when they are advertising via
Adwords services of Google. The organization are showing the clients adverts for the goods and
services on the basis of their interest. Advertisers are getting access to Big Data analytics when
they are using Adwords or other services like Google Analytics in order to attract civilians who
suits in their customer profile to their webpages and stores.
As per the view point of Simon (2013), there are certain benefits of Big data for small
business. Big data are making information readily available and approachable to businesses in
real time. There are various tools that are used in capturing user data . This assist in
accumulating all information in terms of customer behaviour at a faster rate. With this pool of
relevant data at their disposal, businesses are devising effective strategies for improving their
prospects. Every business can give benefit in plenteous amounts from analytics. This is where
big data comes in convenient. It is allowing businesses in tracking the results of their
promotional strategies by giving better understanding of what is working or what is not and by
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this way they can make improvement in their decisions for good outcomes. The Davenport
(2014) says that by using Big data, businesses can improve their understanding about the present
needs of their prospective users. This also tells the corporation how well their goods or services
are meeting the requirements of their consumers. Hence, they can effectively make changes that
requires to be enforced in order to improve these services. Big data makes corporations able in
testing their goods designs and assist in locating any defects that might results in losses if that
product is launched in the market. It also helps in improving the after sales services such as
maintenance, support, etc. On the contrary, the Agarwal and Dhar ( 2014) say that right
execution of Big data is providing competitive advantages for small businesses. It is providing
market insights of the competitors and is, therefore, an important tool which assists in
understanding which areas are lagged behind by the corporation.
CONCLUSION
From the above based report, it has been concluded that Big Data is playing an important
role in every organization now a days. It explains enterprises about their wrong decisions and
strategies. It is also providing relevant information regarding their competitors and tells that how
their performance differs. Big data are opening a door to a host of business opportunities. The
good news is that this is coming without any cost. Data sources available are of cost free. For
example, interaction of social media, sales data, etc. are obtaining at zero expenses.
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REFERENCES
Books & journal
Agarwal, R. and Dhar, V., 2014. Big data, data science, and analytics: The opportunity and
challenge for IS research.
Cavoukian, A. and Jonas, J., 2012. Privacy by design in the age of big data. Information and
Privacy Commissioner of Ontario, Canada.
Chen, C. P. and Zhang, C. Y., 2014. Data-intensive applications, challenges, techniques and
technologies: A survey on Big Data. Information Sciences. 275. pp.314-347.
Chen, M., Mao, S. and Liu, Y., 2014. Big data: A survey. Mobile Networks and Applications.
19(2). pp.171-209.
Davenport, T., 2014. Big data at work: dispelling the myths, uncovering the opportunities.
harvard Business review Press.
De Mauro, A., Greco, M. and Grimaldi, M., 2015, February. What is big data? A consensual
definition and a review of key research topics. In AIP conference proceedings (Vol.
1644, No. 1, pp. 97-104). AIP.
Katal, A., Wazid, M. and Goudar, R. H., 2013, August. Big data: issues, challenges, tools and
good practices. In Contemporary Computing (IC3), 2013 Sixth International Conference
on (pp. 404-409). IEEE.
Minelli, M., Chambers, M. and Dhiraj, A., 2012. Big data, big analytics: emerging business
intelligence and analytic trends for today's businesses. John Wiley & Sons.
Provost, F. and Fawcett, T., 2013. Data science and its relationship to big data and data-driven
decision making. Big Data. 1(1). pp.51-59.
Sagiroglu, S. and Sinanc, D., 2013, May. Big data: A review. In Collaboration Technologies and
Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.
Schmarzo, B., 2013. Big Data: Understanding how data powers big business. John Wiley &
Sons.
Simon, P., 2013. Too big to ignore: The business case for big data (Vol. 72). John Wiley & Sons.
Online
10 Ways to Use Big Data to Get to Know Your Customers Better. 2017. [Online]. Available
through : <https://www.wired.com/insights/2013/07/10-ways-to-use-big-data-to-get-to-
know-your-customers-better/>. Accessed on 19th August 2017.
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