Information Systems and Big Data Analysis: Challenges, Techniques, and Business Applications

This report discusses the challenges, techniques, and business applications of big data analysis in information systems. It covers the characteristics of big data, the available techniques for analysis, and how big data technology can support businesses. It also highlights the importance of big data for leading companies and the obstacles faced in data analytics.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data Analysis
Poster and Accompanying Paper
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Contents
1

Introduction p
What big data is and the characteristics of big data p
The challenges of big data analytics p
The techniques that are currently available to analyse big data
p
How Big Data technology could support business, an explanation with
examples p
Poster p
References p
2

Introduction
Big data can be defined as data that contains great variety of complex data receiving
from various sources which is so enormously big. However, it can not be processed in
traditional style or software won't be able to manage them. It is mainly used to study the
behavior of the user based on the data that is been provided by them.
Information system can be defined as sophisticated system which stores information in
the manner which simplifies the procedure of determination the data in easy manner. It can
also be refereed as a software which helps to organize and analyze data, the sole purpose of
information system is twist raw data into useful information which can be used by an
organization in decision-making.
What big data is and the characteristics of big data
The concept of big data is kind of new but can be traced back to 1960s and 70s.
Throughout the mid 2000 people began to realize how data is generated through Facebook,
you tube and various online services(Berdik, and et.al. 2021). The data is then processed by
Hadoop (a systematic framework which store and analyze the big sets of data) the
development of such framework was important for the growth of big data because this
software is easy to work with and is cheaper for storing the data. Years have passed but the
volume of the data that the users are generating is still high and not only the humans are doing
it but the various objects and devices which are connected through the internet gathers and
send data which enable the company to read customer usage and patterns and performance of
the product.
The characteristics of big data are as follows.
VOLUME - It is referred to the size of the exploding data of computing world. It can also be
termed as enormously huge amount of data that is generated and collected every second, by
various sources. Storing and processing of big amount of data was a problem earlier, but now
with various big data software companies can run through huge amount of data generated by
user or devices.
VARIETY – while the data that is generated and received by different sources results in
variety of data earlier it was in the form of spreadsheet only but now it can be in form of
picture, video, text or PDF etc. the variety is very crucial for the storage and analyze of the
data.
VELOCITY – This referred to the speed through which the data is being generated or
created. It is directly proportional to the speed of processing. This is certain because only after
analyzing or processing the data can be used to meet the demands of the users. If the data flow
is not continuous there is no point to invest efforts and time.
The challenges of big data analytic
New technologies have been created for storing large data but the volume of the data is being
doubled in every 2 years. Various companies are still facing the challenges to keep up their
pace with the amount of data that is showing up and struggling to store it. Although, it is not
enough to only store the data but to process all the data that is being generated (Sivarajah.
and et.al 2017). Data analyst uses most of the time to curate the date and make it more
relevant to the client and organization before it can be used. Big data technologies is
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improving and changing at very high pace. Earlier Apache Hadoop was a popular technology
which was followed by Apache Spark in 2014 is considered to be the best combination to
approach big data. However, keeping up with these technologies is still considered be as
challenging as it was before.
Main problem in data analytic is there is too much data and too many sources by which it
generated and it is too much for the businesses to handle. Collecting meaningful and present
time data, employing the people who lack the skills to manage and analyze the big data can be
very challenging for a company (Duan, Edwards and Dwivedi, 2019). Data security is being
the another obstacle or challenge which is faced by the company. The complexity of the data
offer the major challenge in this system. Whit data mobility is being challenging because the
bandwidth vary from 40 — 100gb but the data is much more and may take time. Various
companies lack the proper understanding of big data while only the data specialist may know
the importance of big data bu not the employee and this can be failure of the company and can
prevent the company to achieve their goals. There is one more challenge which companies
faces is selecting the proper tools for big data analyze. Sometime the companies chooses the
inappropriate tools which result in loss of money time and efforts of the employee.
The techniques that are currently available to analyze big data
There are basically 6 techniques which can be used to analyze big data.
 A/B testing:
This technique is used to compare two groups with variety of test groups, to discern
what changes will give better result for giver variable (Saggi and Jain 2018). Big data can be
fit here to test the huge model where the group has to be big enough to obtain sizable
differences.
 Data Fusion And Data Integration:
By combination of the set of techniques that is used to analyze the integrated data from
various sources. The insights can be more efficient and potentially accurate if developed
through a single source of data.
 Data Mining:
This is a common tool which is used in big data analyses, it extracts patterns by large
sets from combining methods from statistics and also machine leaning.
 Machine Learning:
In the field of artificial intelligence, machine learning can be used to analyze data.
While the people who study computer science knows that it works with algorithms which
produces various assumptions based on data (Qi. 2020). It produces predictions that would be
next to impossible for humans to analyze.
 Natural Language Processing:
This can be described as the language which is used in interaction between human and
computers. This technique is used by people in their daily life and has been around for years
but is often taken for granted (Ge, Bangui and Buhnova, 2018). The tool uses various
algorithms to analyze human language. Various example of NLP that are used in daily life are:
Spell checker, auto-complete.
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 Statistics:
This technique collects, organize and interpret data to survey and experiments. It is a science
for accounting the relevant uncertainties. It is a result of data analysis and its presentation and
interpretation.
How Big Data technology could support business, an explanation with
examples
If said in simple terms the combination of all the processes and tools which are
utilizing and managing large data set. To figure out the more valuable customer business
organization uses big data analysis which also helps businesses to create new and better
experiences, products and services (Oussous and et.al. 2018). Every business organization,
whether small or big doesn't matter, needs data and insights to understand the target audience
and their preferences. It plays a vital role. It helps to anticipate the needs of the customers.
The data needs to be rightly presented and analyzed efficiently. To help business achieve
various goals it is used:
 Dialogue with customers
 Re-develop products
 Perform risk analyses
 Data safety
 Create new revenue streams
Using big data is very important for leading companies so that they can outperform their
competitor. The big data usage is there in almost every sector from IT to healthcare to
education sector. For example Facebook access to large data by its connected services and
various algorithms that might process the data in a very focused way toward the profit, to
bend the user toward the engagement and revenue. Facebook collects big data in terms of bio
metric data or tracking cookies (Karafiloski and Mishev, 2017). It helps the Facebook to
develop their AI which is deep-face to recognize individual to let user know when their photo
is being surfaced. Everyday Facebook mounds with more than 136,000 photos, 510,000
comments, 293,000 status updates are seen in 60 seconds. Which is a very large number to
generate big data and that is just Facebook, imagine Instagram, Tweeter, YouTube, and
various search engines which are being used in daily life generate big data which influence
our choices. The AI turn the data and information to keep the user engage for a greater period
of time to earn more money. Thus Facebook and other social media platform uses big data to
analyze and earn more profit from their customers by giving them relevant ads and products.
Poster
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6
Information Systems and Big Data Analysis
Introduction
Big data can be defined as data that
contains great variety of complex
data receiving from various sources
which is so enormously big.
However, it cannot be processed in
traditional style or software won't be
able to manage them. It is mainly
used to study the behavior of the
user based on the data that is been
provided by them.
What big data is and the characteristics of big data
The concept of big data is kind of new but can be traced back to 1960s and 70s.
Throughout the mid 2000 people began to realize how data is generated through
Facebook, you tube and various online services (Berdik, and et.al. 2021). The
data is then processed by Hadoop the development of such framework was
important for the growth of big data because this software is easy to work with
and is cheaper for storing the data. Years have passed but the volume of the data
that the users are generating is still high and not only the humans are doing it but
the various objects and devices which are connected through the internet gathers
and send data which enable the company to read customer usage and patterns
and performance of the product.
The challenges of big data analytic
 Main problem in data analytic is there is too
much data and too many sources by which it
generated and it is too much for the businesses
to handle
 Data security is being the another obstacle or
challenge which is faced by the company
 The complexity of the data offer the major
challenge in this system
 . With data mobility is being challenging
because the bandwidth vary from 40 — 100gb
but the data is much more and may take time
Techniques
 Data Fusion And Data Integration
 Data Mining
 Natural Language Processing
 Statistics
 Machine Learning
Big Data technology could support
business
 Every business organization, whether small or
big doesn't matter, needs data and insights to
understand the target audience and their
preferences.
 It plays a vital role.
 It helps to anticipate the needs of the
customers
Using big data is very important for leading companies so
that they can outperform their competitor.
The big data usage is there in almost every sector from IT to
healthcare to education sector
Conclusion
There’s absolute confidence that Big Data will hold to play an
vital position in many exceptional industries across the world.
It can certainly do wonders for a commercial enterprise
organization. In order to acquire greater benefits, it’s vital to
educate your personnel approximately Big Data control. With
right control of Big Data, your commercial enterprise can be
greater effective and efficient
References
Berdik, D. and et.al. 2021. A survey on blockchain for
information systems management and security. Information
Processing & Management.58(1). p.102397.

Conclusion
There’s absolute confidence that Big Data will hold to play an vital position in many
exceptional industries across the world. It can certainly do wonders for a commercial
enterprise organization. In order to acquire greater benefits, it’s vital to educate your personnel
approximately Big Data control. With right control of Big Data, your commercial enterprise
can be greater effective and efficient. This report shows the various challenges and uses of big
data analysis.
References
Berdik, D. and et.al. 2021. A survey on blockchain for information systems management and
security. Information Processing & Management.58(1). p.102397.
Duan, Y., Edwards, J.S. and Dwivedi, Y.K., 2019. Artificial intelligence for decision making
in the era of Big Data–evolution, challenges and research agenda. International
Journal of Information Management.48. pp.63-71.
Ge, M., Bangui, H. and Buhnova, B., 2018. Big data for internet of things: a survey. Future
generation computer systems.87. pp.601-614.
Hariri, R.H., Fredericks, E.M. and Bowers, K.M., 2019. Uncertainty in big data
analytics: survey, opportunities, and challenges. Journal of Big Data.6(1). pp.1-16.
Karafiloski, E. and Mishev, A., 2017, July. Blockchain solutions for big data challenges: A
literature review. In IEEE EUROCON 2017-17th International Conference on Smart
Technologies (pp. 763-768). IEEE.
Oussous, A. and et.al. 2018. Big Data technologies: A survey. Journal of King Saud
University-Computer and Information Sciences.30(4). pp.431-448.
Qi, C.C., 2020. Big data management in the mining industry. International Journal of
Minerals, Metallurgy and Materials.27(2). pp.131-139.
Saggi, M.K. and Jain, S., 2018. A survey towards an integration of big data analytics to big
insights for value-creation. Information Processing & Management.54(5). pp.758-
790.
Sivarajah. U. and et.al 2017. Critical analysis of Big Data challenges and analytical methods.
Journal of Business Research.70. pp.263-286.
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