Big Data Analysis: Techniques, Challenges and Business Support

This report discusses the concept of Big Data Analysis, its characteristics, challenges, and techniques. It also explains how Big Data technology can support businesses with examples. The report is relevant for students studying BMP4005 Information Systems and Big Data Analysis.

Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
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Contents
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
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Introduction
Big data analysis refers to those tools and techniques that are used for observing and
collecting the big data. These tools help in gathering the important information from the
data. Along with this, it further helps in arranging the important data that can be used for
the beneficial of user. The big data was first time trace in 1664. With the passage of the
time, the tools and techniques of the big data has become more important as it helps in
dividing the whole data into smaller parts so, it can be easily understood by the user. The
report is going to illustrate the concept of big data analysis. Along with this, the report will
also describe the challenges face under the big data analysis and the report is going to
highlight that how big data analysis acts as a support system in the business.
What big data is and the characteristics of big data
The big data has been considered as the complete information about a particular thing
on a large scale. The data contains even the smallest detail about the thing onto which
the data has been prepared. This data helps in knowing the different aspects that are
relates to the same purpose. For example: a business performs the collects the big
data from the market in order to know the current position of the business, demand by
the customers, competition in the market and how the share can be captured. All of
these aspects are related to the main purpose and that is the success of the business.
Here are the main three characteristics of the big data and that is Volume, Velocity and
Variety. These elements has been considered as the three V’s of big data.
Volume- It refers to the quantity of data. It shows that how much data can be stored.
For example: a business that deals with the number of customers in just one day use
this feature. This helps them to know the over all profit of the day. (Pramanik,
Mukhopadhyay and Pal, 2021). The size of the data has been divided into the bytes
that is known as terabytes and petabytes. If the data is of extreme large rate then it has
been stored in the form of petabytes. Otherwise, the terabytes has been used in terms
of collecting and organizing the data. The volume pf the data decided the quantity and
size of the data.
Velocity- This refers to the speed of the data. The data that are provided and received
over the internet with high are part of the high velocity. For example: Facebook and
twitter posts are generally of high velocity as their reach is high and they travel in a far
speed over the internet.
Variety- This involves all the structured, unstructured and semi-structured data that
has to be arrange with the help of humans or machines. There are different varieties
under which the data has been observed by the user and they have to organize that
data in such manner. So, the data can be easily understood by the user. (Talapatra,
2020) This element helps in define the variety of data so the data can be organize and
process according to the given variety.
The challenges of big data analytics
In current era, the concept of big data has become so popular in every field. The data
leads to generate the important information for the user. But, there are number of
challenges that has been face in the big data analytics and these challenges are needed
to be understand by the user so, they can easily overcome from this.
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The amount of data- This is very important to know that, the amount of data that has
been collected by the user should be useful. The big data analytics results in arranging
that data as well which is completely useless. (Cabrera-Sánchez and Villarejo-Ramos,
2020). Living analysts with large data creates large number of confusions for them and
makes the process more complex. Therefore, the key areas should be pre-decided so
only the focus has been made over those areas in order to collect the data.
Real-time data- In business sector the data that gas been arranged should on the basis
of the current scenario. The old data can lead to the wrong decision and results in the
heavy loss. It is important to keep update with the current data so it can be beneficial.
Visual representation- In order to understand the data in effective manner. The data
should be presented with the help of charts or diagrams. This helps in the better
understanding of the data. Putting the data into theory is a lengthy process. In order to
arrange and organize the big data it is needed tom approach the time-consuming
method. Otherwise, this leads to the high consume of time and energy.
Poor quality data- The quality of the data decides the accuracy of the data. The big data
analytics needs to be performed in such way so the data can be collected with the set
standard of quality. This involves arranging the accurate data. So, the user can use the
data for their purpose in the effective way. (Mubeen, 2017). This requires the
professional knowledge and experience. Therefore, it is important to hire those analysts
that are professional in their field.
The techniques that are currently available to analyse big
data
The tools and techniques refer to those elements that helps in collecting, arranging and
organizing the big data in such manner that can become useful for the user. In current
era, the technology has touched the high standard and it helping in the process of big
data analytics. Here are those techniques that are helping in the big data:
Machine learning- This technique of analyzing the Data is the part of artificial
intelligence. It is that method which has been derived from the computer science. This
works with the computer algorithms and produces the data on the basis of human
assumptions. (Sowmya and Suneetha, 2017). This technique helps in predicting those
thigs that is impossible for the human analyst. It has been found from various studies
that, the machine learning has been proven much efficient in the terms of arranging the
data in useful manner as the predictions has been proven mostly right under this.
A/B testing- Under this technique, the data has been controlled in different forms such
as dividing them into text, pictures and layouts. The large number of data has been fitted
into these models. This technique requires the highly skilled analyst. It also involves the
comparing of data with the variety of groups. This has been done in order to know the
changes that can be make into the available information.
Data fusion and data integration- Under this technique the data has been arrange
from the different sources and then the data has been developed under the single
source. This has been done in order to relate the data towards main purpose.
(Reddy, 2020). This technique results in arranging the different forms of data in a
combined manner so, the data can become more effective while using.
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Data mining- This refers to setting the data with in the mining extract patterns. This
involves focusing on that segment from which the more data can be arranged or become
useful. In business sector, this technique has been used more as this helps in knowing
the group onto which the focus has to be made and the data can be arranged.
How Big Data technology could support business, an
explanation with examples
The big data technology has been considered as the most useful for especially the
business sector. This big data technology helps in deciding the product, services and
decision making of the business. With the help of big data the firm are able to decide
their targeted market. Observing and arranging the big data results in providing the
guidance to the business about their position in the market. Along with this, it further
helps in knowing the competitive scenario in the business. (Wamba and Mishra,
2017). Over all it helps in knowing every single aspect that can harm the business.
The data helps in understanding the loss that can puts a heavy impact on the
business. For example: TESCO is one of the leading firm that produces the large
number of products in different varieties and ranges.
The firm has the large number of consumers in all over the world. The big
data technology helps in knowing the demand and taste of the consumers from
different part of the world. According to the data, the firm produces the goods and
services. As a result, the customers get highly satisfied and they buys more products
and services from the company. This leads to the higher profit for the business.
Along with this, the big data technology further helps in knowing those factors that
will affect the business. In order to reduce the level of risk. The company is using the
concept of big data. (Tranter, 2017). As a result, the company is highly effective in
terms of managing their customers and losses. In terms of competitive market, the
big data technology helps in knowing about the strategies of the marketing of the
competitor firms. And on the basis of that the company can establish more powerful
strategy. Currently, the business sector is on a high pace of growth as the world is
now a globalized economy. Therefore, it is important to know the key information that
affects the business. And the business technology data helps in knowing all the
information by putting the information into a systematic manner. So, it can be
understand by the business organizations and can be used for the benefit of the
business.
Poster
(The poster has been attached with the file)
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References
Cabrera-Sánchez, J.P. and Villarejo-Ramos, Á.F., 2020. Acceptance and use of big data
techniques in services companies. Journal of Retailing and Consumer Services. 52.
p.101888.
Mubeen, et.al., 2017, May. Reducing the risk of customer migration by using bigdata
clustering algorithm. In 2017 2nd IEEE International Conference on Recent Trends in
Electronics, Information & Communication Technology (RTEICT) (pp. 992-996). IEEE.
Pramanik, P.K.D., Mukhopadhyay, M. and Pal, S., 2021. Big Data Classification:
Applications and Challenges. Artificial Intelligence and IoT: Smart Convergence for Eco-
friendly Topography. 85. p.53.
Reddy, et,al., 2020. Analysis of dimensionality reduction techniques on big data. IEEE
Access, 8, pp.54776-54788.
Sowmya, R. and Suneetha, K.R., 2017, January. Data mining with big data. In 2017 11th
International Conference on Intelligent Systems and Control (ISCO) (pp. 246-250). IEEE.
Talapatra, et.al., 2020. PROBLEMS IN DATA ANALYTICS AND ITS
SOLUTIONS. Journal of Mathematical Sciences & Computational Mathematics, 1(3),
pp.344-353.
Tranter, E.M., 2017. How big data analytics helps improve business
performance (Bachelor's thesis, University of Malta).
Wamba, S.F. and Mishra, D., 2017. Big data integration with business processes: a
literature review. Business Process Management Journal.
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