Analyzing Big Data: An Information Systems Perspective Report

This report provides a comprehensive overview of big data and its analysis within the context of information systems. It begins by defining big data, highlighting its key characteristics: volume, variety, velocity, veracity, and value. The report then delves into various techniques for analyzing bi...Read More

Information
Systems and Big
Data Analysis

Table of Contents
Big Data...........................................................................................................................................3
Big data analytics.............................................................................................................................4
Various techniques for analysing big data are as follows................................................................5
Challenges of big data analytics......................................................................................................6
Big Data in business.........................................................................................................................7
References........................................................................................................................................9

Big Data
Big data refers to a field which provide systematic extraction of information from a number
of data sets that are too large and complex to be dealt with traditional ways of data processing. It
is often seen that the data that have a number of cases a rose with great statistical power and high
complexity a leading towards false discovery rate (Kache and Seuring, 2017). Big data helps in
including the challenge of capturing data, storing data, analysing data, sharing data, transferring
data, updating data, visualizing data and soon. Big data can be generally associated with three
different concepts which are volume, variety and velocity. When a big size of data is handled it
is not simple to track all the information regarding the data. In current context the term big data
refers to using of predictive analytics and different behavioral analytics in order to extract value
from certain data and also particularize the size of data set.
The characteristics of big data can be characterized into four components as mentioned below:
Volume: Volume is the first and major characteristic of big data which makes the dataset
big in its size which is to be evaluated. The date upsets are usually stretch to Peter bites and
exabytes. There are huge volumes of powerful data present and powerful processing techniques
are required in order to assess this data. Example can be taken to Facebook which is most
popular platform for social media and there are more than two .2 billion active users who were
spending huge amount of their day posting, commenting and liking on Facebook. Due to this
volume becomes a major characteristic of big data.
Variety: The variety which is present in big data is very high. Examples can be taken of
different email, CRM system, mobile data as well as Google advertisements that are included in
different data sets. These are all the sources which produce different data which are to be
collected, stored as well as analyse successfully. Along with this the data scientist as well as
analyst or not only limiting their job to collecting data from one source but there are a number of
different sources which are providing data. This can be structured, semi structured as well as
unstructured sources. The structured sources of data comprises of the well organised data
whereas the semi structured sources data are the data that are not very well analysed or
structured.On the other hand the unstructured data consist of different images, media post, instant

messages which do not have any internal structure and a disorder. Due to this the variety in big
data is increasing.
Velocity: When considering the amount of data its volume and the variety there is
consistent flow of data. This gives the birth to 3rd characteristic that is velocity (Dey and
Satapathy, 2018). Velocity of data means that more data is available on certain days and due to
this the velocity of data analysis is also required to be high. There are data professionals who
gather data over time and the date at the end of week or a month or quarter or rather hide and that
at other time of days. Due to this velocity is a major characteristic of big data which is to be
analyse successfully.
Veracity: Voracity refers to the accuracy, quality as well as trustworthiness of the data that
is collected. The reliability of data needs to be distinctive in order to make sure that the data
which is collected is accurate and conclusions can be drawn from it. It is often required to
understand the valuable sources of information from which the big data can be analysed
successfully. When the veracity of data is low it is often estimated that bad decisions can be
evaluated and drawn due to the data.
Along with all these mentioned characteristic of fifth characteristic can also be taken that is
value. Value refers to the importance of data that is collected. It is the top in the pyramid of all
the organisations ability that will help the business to successfully use the data and bring out
conclusions from it. The values can be found in real time as well as in industries. There are a
number of government agencies who are using value of data in the current time in order to deal
with different challenges.
Big data analytics
Big data analytics refers to the specialized analytic software in high-powered computing
system which is enabling scientist and analytics to use the volume of structured as well as
unstructured data in order to draw conclusions from it for business benefits. These benefits can
be the revenue opportunities, improving operational efficiency as well as effectiveness in
marketing campaigns in a business.

Various techniques for analysing big data are as follows
Association rule learning: The association rule learning is method which is used to discover
interest of correlation between different variables in huge data sets. It was developed by
supermarket in order to understand the relationship between products and also determine point of
sales for the organisation. The association rule learning helps in order to place products in better
proximity to each other so that the sales can be increased. It also helps in extracting information
about the visitors to different websites from server logs. Association rule learning is effectively
used to analyse biological data in order to uncover new relationships and also monitor systems in
order to detect any malicious or intruders.Along with this association rule learning also helps in
identify people who are buying different products at the same time such as milk and butter along
with diapers.
Classification tree analyses: Classification tree analysis is successfully helping to identify
categories to which the new observations are belonging. It is requiring a training sit in which the
observations are correctly identified. Such as historical data. Classification tree and losses is
successfully used to automatically assign different documents to various categories. It also helps
in categorizing organisms into different groupings. In order to develop profile for students those
who are taking online courses classification realities can also be successfully used.
Genetic algorithms: Genetic algorithm is a technique which is inspired by evolution. It
helps in mechanism such as mutation, natural selection and inheritance (Hwang and Chen, 2017).
This is basically used for understanding the evolving situations and solution to problem that
require maximum optimization. Genetic algorithms are successfully used in order to schedule
different doctors for hospital emergency rooms. This can also be used to generate artificially
creative combinations of different optimal materials or engineering practices such as fuel-
efficient cars.
Machine learning: Machine learning is involving software which helps in learning from
different data. It is providing the computers with an ability to learn without programming
explicitly and also focusing on making different predictions for the already available training
data. Machine learning is successfully used to distinguish between spam and non- spam emails.
It can also be used to understand the preferences of learners and make remove recommendations

for the information. Machine learning is often determined to understand best content and engage
customers through the use of those content.
Regression analysis: Regression analysis is involving manipulation of independent variable
in order to understand how these are influencing dependent variables. It can also describe the
value of dependent variable changes as the variable independent is varied. This can work best
with the continuous quantitative data such as speed or age. Regression analysis is used to
understand the customer satisfaction level and effects on their loyalty. It can also use to
understand the neighborhood and size of different listing price houses.
Challenges of big data analytics
There are a number of challenges faced by business while using big data analytics. Some of
the major challenges are mentioned below:
Need for synchronization through different data sources: As the amount of data is
increasing day by day it is a big challenge for organisations to incorporate them in analytical
platforms (Tiwari, Wee and Daryanto, 2018). Due to this huge gap is created and wrong
messages or insides are derived from the data.
Shortage of professionals: Is often seen that the requirement of professionals for big data is
increasing day by day. However there are trained and experienced employees required to manage
this data. There is a shortage of data scientist as compare to the massive amount of data that is
being produced which is a great challenge for businesses to handle.
Generating meaningful insights: Bringing in meaningful results from the data available and
also providing it to relevant department is difficult for businesses now. This is due to the big
amount of data that is present and the business data analytics need to mend the gap between
departments and huge amount of data successfully.
Getting voluminous data into big data platform: The data is growing with every day and it
only indicates that it is necessary for business to handle this Raj amount of data on a day-to-day
basis. Due to this large amount in variety of data it is overwhelming for the data engineers to

consider the importance of data and access to all the data easily. Due to this the data available
cannot be successfully brought into the big data platform by every organisation.
Data storage and quality: The growth of business organisation is rapid due to which the
data used is also increasing. Along with this massive amount of data it is a real challenge for
everyone to successfully use and store the data (Ghani, Hamid and Ahmed, 2019). It is necessary
to distribute the data into unstructured and structured format which is a huge problem as the data
might be missing, inconsistent, logic conflicts and duplication possible which leads to challenges
regarding quality of data.
Security and privacy of data: There are huge variety of possibilities and opportunities
generated for a business as they use big data. However it is also involving potential risk for
businesses due to privacy and security of the data. There are huge amount of disparate sources
used to analyse data and it might lead to higher risk for exposure and vulnerability of data for an
organisation. The more volume of the data is associated with more risk of privacy and security
concerns for a business.
In order to overcome all these challenges of big data it is necessary that organisations are
training their employees successfully in order to make sure that the concept of big data is clear
and they can move towards success facing all the challenges of big data analytics.
Big Data in business
There are a number of technologies through which organisations are using big data in order
to bring benefits for them. Some of such use of big data is mentioned below:
To boost customer acquisition and retention: Data is successfully allowing businesses to
observe their customers and understand the patterns and trends. This helps in triggering loyalty
within the customers. Examples can be taken of Coca-Cola as it has managed to use big data
strategy so that they can analyse strong customer and retain them for a longer period of time.
To solve advertisers problem and offer marketing insights: The marketing and advertising
technology sector is now effectively using big data analysis in order to understand the online
activities and monitor the point of sale transactions so that they can effectively generate more

targeted campaigns for their consumers (Choi, Wallace, and Wang, 2018). Examples can be
taken where Netflix is using big data analytics in order to target an advertise the content. They
are providing a number of suggestions for next movie along with insights of customers according
to their interest.
Risk management: There are high-risk business environments which are requiring risk
management processes. A risk management plan is investment for business necessary regardless
of whichever sector it belongs to. Big data analytics contribute greatly towards development of
different risk management solutions. The tools are allowing business to quantify and also model
the risk they are facing every day. Organisations are required to implement structured
evolutionary so that they can successfully accommodate the scope of big data. Examples can be
taken where UOB banks from Singapore uses big data in order to manage risks. The big data risk
management system is enabling bank to reduce its calculation time which was earlier 18 hours
and now it takes only a few minutes.

References
Books and Journals
Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data analytics in operations
management. Production and Operations Management, 27(10), pp.1868-1883.
Ghani, N.A., Hamid, S. and Ahmed, E., 2019. Social media big data analytics: A
survey. Computers in Human Behavior, 101, pp.417-428.
Tiwari, S., Wee, H.M. and Daryanto, Y., 2018. Big data analytics in supply chain management
between 2010 and 2016: Insights to industries. Computers & Industrial
Engineering, 115, pp.319-330.
Hwang, K. and Chen, M., 2017. Big-data analytics for cloud, IoT and cognitive computing. John
Wiley & Sons.
Dey, N. and Satapathy, S.C. eds., 2018. Internet of things and big data analytics toward next-
generation intelligence (pp. 3-549). Berlin: Springer.
Kache, F. and Seuring, S., 2017. Challenges and opportunities of digital information at the
intersection of Big Data Analytics and supply chain management. International
Journal of Operations & Production Management.