Big Data Case Study

This case study explores the impact of big data on organizations. It discusses the types of big data collected, business intelligence technologies used, and the benefits obtained. It also identifies the organizations that are most likely to need big data management and analytical tools.

Big data case study

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Question 1: Describe the kinds of big data collected by the organizations
described in this case
According to the given scenario, there are major four kinds of big data collected by the
companies which will be discussed in this part. The first company is IBM that used big
data and it helps the British library for controlling and managing a large amount of
statistics sets. It has been found that the IBM big sheets provided a platform to British
library for handling the data of consumers and British library is accountable for
preservative web applications of British which no lengthier occur but require to be
conserved. By utilizing IBM big data British library improved the performance of their
system and they easily controlled large data sets in an effective manner (Agarwal and
Dhar 443-448).
Second, the real time crime centre involves big data process for controlling data of
millions of consumers on city crime. For which state and government activities are
adopted big data approach to learn concealed designs in illegal cases. Moreover, the IBM
and new york city work with each other in order to design a warehouse that involves
data of 120 million criminal and 33 billion public records (Singh and Reddy 8). Third,
Vestas organization designed and applied a answer which involves IBM info sphere big
visions software consecutively on a huge level of presentation. Forth, Hertz A car rental
Hetrz with the help of big data process for analysing individual sentiment from emails,
text messages, web surveys, and traffic patterns. The Hertz organization was able to
control and manage the time spending process and recover organization reply period to
client response and variations in soppiness.
Question 2: List and describe the business intelligence technologies described in
this case
There are the following businesses intelligence skills described in this scenario:
Real time analytics for inventory management: this is one of the common intelligence
technology which is used by sears and they implemented hadoop and into a traditional
environment both technologies. According to the big data case study, by using business
intelligence process teams are able to deliver more efficient and real time inventory to
their consumers (Cevher, Becker and Schmidt 32-43).

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There are few other technologies explained in the given case study, for example, green
mountain coffee, data streams and calibrio analytics to glean insight form various
communication channels. By using such kind of business intelligence techniques
companies can analyse data and receive feedbacks from consumers and they can easily
deliver products more effectively (Elgendy and Elragal). It is identified the AutoZone is
utilizing a computer software which is known as NuoDB in order to control and
maintain their inventories across all their channels and stores.
IBM big sheets are defined as an insight engine which supports annotate, extract and
visually evaluate vast parts of unwanted web statistics. Central communities are
evaluating big data in order to collect concealed designs in illicit activity, for example,
opportunity, correlations between time periods, companies and criminal agenises
which would be complex to analyse a small number of data sets (Watson 65). It is
observed that the real time crime system involve lots of data sets on criminals and city
corruption and vestas rely on site based facts for calculating the finest spots to
implement their turbines. Moreover, it evaluated a explanation which involves IBM
Information Shpere big visions technology which is successively on a large presentation
IBM device.
Question 3: Q3: Why did the companies described in this case need to maintain
and analyse? What business benefits did they obtain?
In the given case study there are several organizations highlighted that uses big data
technology for analysing a large number of data sets. These organizations are described
below:
The British library
It is identified that the British public library required to control and manage big data
since old-style statistics organization techniques showed insufficient to store numbers
of web sheets. In this modern era, it is very important to analyse a large amount of data
for which the British library designed and implemented big data technology (Gandomi
and Haider 137-144). Moreover, their previous techniques and legacy analytics devices
could not extract important information from quantities of data due to which the British
library changed their tools and adopted the concept of big data.

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New York police department
There are several key factors due to which the New York forces department required to
control and analyse big data which are the following:
 The big data technique allow the NYPD for quickly responding to the criminal
cases
 It helps them for gathering sources of the suspects for example past offences,
suspect's photo, address with a map and it can be imagined in instants on a
audio-visual wall.
Vestas
Vestas required big data technique for managing and analysing a large amount of data
sets and there are following reasons for using big data:
 Vestas is one of the largest organizations which required for maintaining a huge
amount of data sets
 Locations of data sets are very significant to Vestas so that they can effectively
place their turbines
 Areas and fields without sufficient breeze will not produce the required control
and energy
 The part with too abundant mind may injury the system
 However, Vesta trusts on site founded data systems for calculating the finest
spots in order to implement its turbines
 With the help of big data sets Vestas store around 2.8 petabytes data (Slavakis,
Giannakis and Mateos 18-31)
Hertz
Wagon rental gaint Hertz requires to control and manage big data sets since:
 Decreasing time spent data processing
 Increasing the level of response time in order to contain and solve consumer’s
feedbacks
 With the help of big data technology, Hertz was able to evaluate and find the
delays which occur in the system

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 Increased the overall performance of Herts and enhanced the level of consumer
satisfaction (Kambatla, et al. 2561-2573).
Business benefits
There are following major advantages for business by using big data approaches:
 Increased performance
 Competitive advantages
 Enhance consumer satisfaction
 Excellence operational
 Decreased cost and time spent
 Enhanced decision making approach in order to improve the level of customer
satisfaction (Marr).
Question 4: Identify three decisions that were improved by using big data.
Best utilization of sources and operative time
With the help of big data technology, organizations can ideal utilization of their sources
in order to improve the overall presentation of their businesses. However, Vestas
Company can prediction the best turbine process in 15 minutes in its place of 3 weeks
and safe more time that required developing a turbine place (Kwon, Lee and Shin 387-
394).
Quick and effective decision making
The choice creation process can be improved and achieved with the help of big data
technology. Consumers of the British library and NYPD can easily find facts from the
British library web places and it can be done by adopting big data technology. after
analysing the given case study, it has been analysed that NYPD can develop a faster
choice in order to obtain the details of suspects with the help of the real time crime
centre and big data technology (Lee, Kao and Yang 3-8).
Reduce the operational cost and other product related cost
It is observed that by using the fundamental concept of big data techniques companies
can easily make the right decision and identify the wrong decisions that affect their
performance (Loebbecke and Picot 149-157). In the given case study, Herts was able for

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effectively adjust the level of their staffs in office during the peak time periods and they
also improve the efficiency of their business by taking better decisions as per the
customer feedbacks.
Question 5: What kinds of organizations are most likely to need big data
management and analytical tools? Why?
It has been found that organization that is responsible for storing and collecting a large
amount of data from consumers which are most likely to use big data technique. For
example national library, registration department, and income tax department all these
are required big data approach because they collect and gather a large amount of data
from consumers and public (Raghupathi and Raghupathi 3).
Some authorities companies for example customs, police department, and immigration
also requires the big data technologies in order to store the data or information of
millions of consumers which can be used for the care of the civilization.
Moreover, company to go lime require the big data technology for obtaining the climate
and site because the data of location and weather are very important for an
organization to perfectly making decisions (Rumsfeld, Joynt and Maddox 350).
Therefore, it is very important to collect and monitor the huge quantity of data sets and
every organization which deals with consumers and stakeholders on a larger basis
required big data analytic process for handling these data sets.

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References
Agarwal, Ritu, and Vasant Dhar. "Big data, data science, and analytics: The opportunity
and challenge for IS research." (2014): 443-448.
Cevher, Volkan, Stephen Becker, and Mark Schmidt. "Convex optimization for big data:
Scalable, randomized, and parallel algorithms for big data analytics." IEEE Signal
Processing Magazine 31.5 (2014): 32-43.
Elgendy, Nada, and Ahmed Elragal. "Big data analytics: a literature review
paper." Industrial Conference on Data Mining. Springer, Cham, 2014.
Gandomi, Amir, and Murtaza Haider. "Beyond the hype: Big data concepts, methods, and
analytics." International Journal of Information Management 35.2 (2015): 137-144.
Kambatla, Karthik, et al. "Trends in big data analytics." Journal of Parallel and
Distributed Computing 74.7 (2014): 2561-2573.
Kwon, Ohbyung, Namyeon Lee, and Bongsik Shin. "Data quality management, data usage
experience and acquisition intention of big data analytics." International journal of
information management 34.3 (2014): 387-394.
Lee, Jay, Hung-An Kao, and Shanhu Yang. "Service innovation and smart analytics for
industry 4.0 and big data environment." Procedia Cirp 16 (2014): 3-8.
Loebbecke, Claudia, and Arnold Picot. "Reflections on societal and business model
transformation arising from digitization and big data analytics: A research agenda." The
Journal of Strategic Information Systems 24.3 (2015): 149-157.
Marr, Bernard. Big Data: Using SMART big data, analytics and metrics to make better
decisions and improve performance. John Wiley & Sons, 2015.
Raghupathi, Wullianallur, and Viju Raghupathi. "Big data analytics in healthcare:
promise and potential." Health information science and systems 2.1 (2014): 3.
Rumsfeld, John S., Karen E. Joynt, and Thomas M. Maddox. "Big data analytics to improve
cardiovascular care: promise and challenges." Nature Reviews Cardiology 13.6 (2016):
350.

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Singh, Dilpreet, and Chandan K. Reddy. "A survey on platforms for big data
analytics." Journal of big data 2.1 (2015): 8.
Slavakis, Konstantinos, Georgios B. Giannakis, and Gonzalo Mateos. "Modeling and
optimization for big data analytics:(statistical) learning tools for our era of data
deluge." IEEE Signal Processing Magazine 31.5 (2014): 18-31.
Watson, Hugh J. "Tutorial: Big data analytics: Concepts, technologies, and
applications." CAIS 34 (2014): 65.