Information Systems and Big Data Analysis - Challenges, Techniques and Business Support

This report covers the characteristics of big data, challenges faced while doing data analysis, the techniques using when collecting data and how it is useful for company. It also explores how Big Data technology could support businesses with examples. The subject is Information Systems and Big Data Analysis, and the course code is BMP4005. The report is relevant for students studying business management.

Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
Submitted by:
Name:
ID:
1

Contents
Introduction 3
What big data is and the characteristics of big data 3
The challenges of big data analytics 4
The techniques that are currently available to analyze big data
6
How Big Data technology could support business, an explanation
with examples 7
Poster 7
References 8
2

Introduction
Information system is a set of coordinated elements working together to
collect,process, store etc. It can be defined as the software that helps organization
and analyze data. The motive of the system is to convert raw data into useful data
that can be useful in decision making in industries. There are various components of
system such as hardware, software, data, procedures, people and feedback
(Xiaorong, Shizhun and Songtao, 2018). The advantages of this are communication,
availability, creation of new jobs and globalization and cultural gap. This report will
cover the characteristics of big data, challenges face while doing data analysis, the
techniques using when collecting data and how it is useful for company all are
describe below.
What big data is and the characteristics of big data
It refers to complex and large data sets that have to be used and analyzed to
uncover valuable details that can be beneficial for organization. It is an detail work
that provides cost effective and innovative form of conducting a view of decision
making. It is almost penetrated in every industry today and now a dominant driving
forces behind the success of an business across the globe (Aboelmaged and
Mouakket, 2020). This is mostly used by healthcare, academe, banking,
manufacturing, IT, retail and transportation. The basic 3V's which are listed as
characteristics of Big data are describe below.
1. Variety- it states that integrated, unorganized and semi structured
information which collected from various origin. Whereas earlier it was collected
from spreadsheet and database, but today it comes in a kind of emails, PDF's,
photos, videos, audios, posts and many more. It is one of important distinctive of big
data.
2. Velocity- it determines the accurate time and speed at which data has
been collected. In wide concept it comes with the new challenges for data centers
trying to deal with variety. It typically consider how quickly the data is coming and
stored.
3. Volume- large magnitude of information is being collected on a regular
base from many sources like social group, enterprise operation, device,
communication system, human interaction etc. The operations of gathering data
3

volume has been transformed from TB to computer memory unit with an inevitable
displacement to petabytes, and all the aggregation can,t be save in conventional
systems. This platform gives the manner to an structure to efficiently preserve and
process all that collection and discover out what precious and valuable action. It
gives reward to company in the way of predictive analysis, allows business to make
better decisions, it provides a preview that shows the customer points and allows
company to improve their product and services.
The challenges of big data analytics
This data is becoming increasingly important for competitive advantages from
the data that the company grown familiar with. While old data was largely dealing,
captured from internal sources, the new data is collection of unstructured and
transnational, it is assembled in private and available publicly (Gunavathi, Priya and
Aarthy, 2019). This results that big data have model to create an totally new field for
private and public data centers and new objection come out, that hinder data
accuracy and quality. Their are some challenges which are describe below.
1. Data Integration- generally, company owed information from many origin,
which makes it typical to determine the effectivity of combination procedure. lot of
problem concern unfaithful representation may leads back to, how the data is
collected, verified, stock and used. The major issue is when working with data
delicate or intense industry, whereas small fault can cause effect on the total
process of business.
2. Data complexity- This is meant as a system which has to make the
relationship with an element as well as invariable than ever before. The raw
assemblage is being collected from various sources such as dealings, salesperson,
customers and many others within a structure. It makes that a structure of data is
involved more as compared to earlier.
3. Data security – the need of the data is required to be secured at any cost,
application or activity like cloud which provides platform in order to save the data on
the individual program with an aim that it can be approached anytime and any where
un order to make the functioning of the businesses easy.
4. Data capture- now days the method is on the top edge for industries n data
center. The amount of data obtained from the sources is highly considerable, that
company have become familiar now.
4

5. Data scale – This challenge mainly provides explanation to the department
that they are being accountable for the centre of data and high technology. The
administrative department is mainly scared that the operation is high in cost and not
preventive if the scale at which company run is considerable.
6. Data mobility- there is a change in the web on perpetual basis and this
may be tracked to the quality of data. There is a constant coming of the network
whch helps in making work quicker as compared to the requirement of the
information of cyberspace but there is enhanced movement of the data by 100 times.
7. Data value- This provides that that earlier the value of the information
which is collected have minimum value but nowadays, priority is given to the data by
ventures nowadays (Maroufkhani and et. al., 2020). this all shows that a more data
has to be collect for long term period and the information have to be available, as
the analytic can have the wanted effect.
8. Data Analytic - organization should come up with the methods to collect
more data, so company can later collective into big data secretary. Being
competitive and extensive transaction, both device and acquisition and analytical
campaign vast amount of fast speed during the analytic activity of huge information.
The techniques that are currently available to analyze big
data
These information sets are tangled that it becomes difficult information for
operation to work on conventional systems. The various techniques using currently
for analyzing big data are describe below.
1. Association Rule learning- it is a technique of building interest between
variants in big database (Lin and Yang, 2019). It was first used by supermarkets
chain to discover interest between product and data from market point of sales. It
helps in analyzing biological data and monitor system logs.
2. Machine Learning – It gives computer the ability to learn without being
implicitly programmed and focused on making prediction based on set of training
data. It helps in identifying between spam and non spam and determine the chance
of wining a case, or setting legal billing rates.
3. Regression analysis – it involves influencing some individual variant to
check how it influences a dependent variables. It works best with continues
5

numerical data like speed or age. It helps in identifying level of customer satisfaction
and the number of supporting calls received.
4. Sentiment analysis – it helps in improving service by analyzing user
comments and determines what customer really thinks based on opinions from
social media (El‐Hasnony and et. al., 2021). It assist in knowing the mind set of
buyers so that company can produce goods in accordance to need and wants to the
buyers.
5. Social network analysis – it is a method that was used by business to
understand social state. Now, it is used to examine the relationship between
individuals in many field. It helps in understanding the social structure of a users and
insight the value or determiner of a specific single in a unit.
How Big Data technology could support business, an
explanation with examples
It is a accumulation of all the procedure and instrument belongs to consuming
and managing huge assemblage sets. It assist in analyzing and figure outing the
valuable customers, creating new experiences,services and products (Patel, Shah
and Shah, 2020). These data points can explain about company behavior,
personality and life events.
 This can change the small industries do business as data collection and
representation becomes more accessible.
 New innovations and cost effective technologies are constantly improving
that makes easy process for an organization.
 modality sum-up of data help teams to proceed quick process of data analysis
and make quick decisions.
 The proper aligned data with the business influences in achieving goals,
brand recognition and market share.
 It supports the creation and preparation of a more robust IT infrastructure by
giving professionalism.
 It Resource organization to get a good idea of market area or expected
customer.
 Assist business for researching with new ware and better marketing technique
to introduce product in target market segment.
 Assisting with using new optimizing of pricing scheme.
6

data analysis can help in providing content on which specific customer are
most curious in, and that detail can be used further to point user with much
particular through company website drive.
Poster
7

8

References
Aboelmaged, M. and Mouakket, S. 2020. Influencing models and determinants in big
data analytics research: A bibliometric analysis. Information Processing &
Management, 57(4) p.102234.
El‐Hasnony, I.M.,and et. al., 2021. Leveraging mist and fog for big data analytics in
IoT environment. Transactions on Emerging Telecommunications
Technologies, 32(7), p.e4057.
Gunavathi, C. Priya R.S. and Aarthy, S.L. 2019. Big data analysis for anomaly
detection in telecommunication using clustering techniques. In Information
Systems Design and Intelligent Applications (pp. 111-121) Springer,
Singapore.
Lin, H.Y. and Yang S.Y., 2019. A smart cloud-based energy data mining agent using
big data analysis technology. Smart Science 7(3) pp.175-183.
Maroufkhani, P., and et. al., 2020. Big data analytics adoption: Determinants and
performances among small to medium-sized enterprises. International
Journal of Information Management, 54 p.102190.
Patel, D. Shah, D. and Shah M. 2020. The intertwine of brain and body: a
quantitative analysis on how big data influences the system of sports. Annals
of Data Science 7(1) pp.1-16.
Xiaorong, F., Shizhun, J. and Songtao, M., 2018 March. The research on industrial
big data information security risks. In 2018 IEEE 3rd International
Conference on Big Data Analysis (ICBDA) (pp. 19-23) IEEE.
9