Information Systems and Big Data Analysis: Characteristics, Challenges, Techniques, and Business Support

This report provides an overview of information systems and big data analysis, including its characteristics, challenges, techniques, and how big data technology could support businesses. It discusses the importance of skilled data collectors, the challenges of selecting the best techniques for big data, and the role of big data in improving production and service areas. The report also covers various techniques available to analyze big data, such as A/B testing, mining information, data combination and fusion, technical learnings, and measurements. Course code: PRO412, BMP4005. Subject: Information Systems and Big Data Analysis. College/University: Not mentioned.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
Submitted by:
Name:
ID:
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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 analyse big data
4
How Big Data technology could support business, an explanation
with examples 5
Poster 6
References 6
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Introduction
Big data means collection of data which contains variety of elements which
occurs in great numbers and have more velocity. It is also known as 3 V's. For new
data sources big data is quite complex and large. It helps businesses in different
aspects and it also helps in market analysis of the business. It is part of the
management to manage the data available which helps management in known
different aspects of a business. Data is stored and transferred in electrical signals
and it is recorded by the use of optical, mechanical and magnetic recording media
(Madhlangobe and Wang, 2018). This report consists of various explanation such as
characteristics of big data, tools, challenges and how big data supports business in
managing the available data.
What big data is and the characteristics of big data
Big data is concerned with the collection of data which is basically of two
types organised and unorganized. The information is collected and stored in
electronic form to analyse information of the market available in the organised form
to determine different aspects of the business concern. It can be seen that it has a
very huge data which can not be analysed by an individual with his analytical skills.
Characteristics of Big Data is as follows:
 Variety: It contains variety of forms of data such as structured, semi
structured and unstructured that is collected by the help of different sources.
Data is collected from the past data which is readily available in the market.
Nowadays data is available in various forms such as photos, videos, emails,
etc (Cheng and et.al., 2018).
 Volume: The information which is readily available is very huge which is
collected and stored by the firms in order to use it for the purpose of further
research on the existing product or the new product that the company wants
introduce in the market. The data is collected from sources such as internet,
media, visualisation, digitalisation, etc. For example, Facebook uses data
which is uploaded by the users of the company, it can contains huge amount
of data because it is used as a communication platform thus, it contains a
huge amount of information.
 Veracity: In this primarily it is concerned with the relevancy of data. It
provides various ways through which data can be collected and modified
according to the need of the organisation. It is defined as a process of
managing in the data available effectively. It is one of the essential part of
business development .
 Value: It is one of the essential characteristics of bug data. It helps in
compiling raw data effectively and also eliminate the data which is not useful
for the organisation in the process of analyses which helps in achieving the
goals determined by the organisation.
 Velocity: It plays an important role in data analysis as compared with the
other factors that influences the process of analyses. It helps in analysing the
data available in real time basis which generate the information on instant
basis. It facilitates linkage of incoming data set and change in range of the
data. It generally deals with the flow and speed of the data (Wamba and et.al.,
2020).
The challenges of big data analytics
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It is a matter of fact that all the available data is not use thus firstly the data is
filtered that fulfils the purpose of study. In the following management needs to
anticipate the future hindrances that organisation will face in the future. Below are
the challenges of big data analysis:
 Inadequacy of professional education: It is very important for any
organisation to have professionals and talented employees to perform various
activities in the organisation. Business organisation needs to have skilled data
collectors because of the data collected contains some value and analyst
reform these data to an information which is used by the management in
order to make decision.
 Insufficiency of appropriate understanding of big information: The
inability to collect useful information and data that would enable business in
planning effective decisions and planning their future operations may lead to
situations that would cost losses and unwanted expenses as well. If a
company lags behind in efficient decision making then it might lack in
development of innovative techniques that would serve as a competitive
advantage over other present in the market. Inadequate information about
working of a company at times might lead to inaccurate decision making.
 Securing data: Information collected by companies is larger in size than
compared to any small firms therefore it confuses the manager as well as
business to understand type of data that is necessary to be kept and which
part is not that useful. It is thus difficult and counted as a complex activity to
deal with in case of large scale organisation. Such events at times leads to
inefficiency and ineffectiveness in working of company.
 Complex activity to deal while selecting best techniques for big data: It is thus
a difficult situation to tackle at the time of decision making for choosing the
best method that would help the company to reflect better results and facilitate
efficient decision making as well. Hence, it is considered a challenging task
for manager to use accurate method that would be helpful in examining
complex and large scale data through which decisions are carried out (Wang,
2021).
The techniques that are currently available to analyse big
data It can be considered as an important task in businesses that considers to search for
different methods that help to assess big data. It further helps in better decision making for
the organisation and promote expansion and growth of the same. Types of methods that
can help are listed below:
1. A/B testing: It can be described as a method that concentrate on comparison of
various units to determine the collected data from different areas and places. This
method provides results to company that is being collected from respective areas.
2. Mining information : This is a tool that is used by different businesses for analysing
big data in simpler terms. This serve as a guide for marketers who feel that a mistake
has been committed which must be corrected well in time so that it doesn't affect
the working of company in any adverse ways. It makes sure that the mistakes and
negative results are not repeated on a frequent basis (Balamurugan and et.al., 2020).
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3. Data combination and Fusion: It is a technique that focus on pool of methods which
would contribute in carrying out analysis of big data. This also gives an idea about
accurate and reliable information collected from particular areas.
4. Technical learnings: It is related to best use of updated, advanced and innovative
techniques that would help to collect applicable data. It is useful to have a analysis as
what is prevailing in market and how the company can perform better than it. It
helps in development and growth of a organisation & convert the productive
information in such a way that it helps in decision making process of a firm.
5. Measurements: It is a tool that helps to manage and predict details and future
with the help of carried out surveys. It is well-advised as an useful method that
helps to provide necessary information to its business operators. It helps to
convert complexity in data collected in a more simpler and precise form that
would be easier to understand (Zhao-hong and et.al., 2018).
How Big Data technology could support business, an
explanation with examples
This is considered as an necessary concept for companies to assess proper data set
that would help in effective and efficient decision making within the organisation. Big
data thus has an necessary role in working and functioning of companies. Some
factors that reflects how big data can serve as a support system in case of
organisations:
 Improve the areas of production and service: Big data serve as a tool that
helps to understand what areas are to be improved in a company that would
help the it to grow and develop as well. If it is being observed by business that
there is no additional benefit available with enterprise then it is necessary for
them to plan something that would help them to provide an competitive
advantage over others already present in market. It would thus help to
increase demand and supply of various products and services in market
(Shaw, Rowland and Machova, 2021).
 Information security: Big data analytics uses various methods that helps to
keep data and related information safe and secure. There are many tools that
contribute in conducting analysis of market which is useful for company to
plan future based operation's and activities thus it is necessary for the
organisation to plan activities in that accordance only.
 Helps to enjoy in competitive benefits: Big data provides guidance for the
company to understand its weaker areas of other competitors that would
serve as an advantage over them ro cover market and attract more demand &
generate more sales.
Poster
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References
Balamurugan, S., and et.al., 2020. Call for Special Issue Papers: Big Data Analytics
for Agricultural Disaster Management: Deadline for Manuscript Submission:
February 1, 2021. Big Data, 8(6), pp.544-545.
Cheng, Y. and et.al., 2018. Building big data processing and visualization pipeline
through Apache Zeppelin. In Proceedings of the Practice and Experience on
Advanced Research Computing (pp. 1-7).
Madhlangobe, W and Wang, L., 2018. Assessment of factors influencing intent-to-
use Big Data Analytics in an organization: pilot study. In 2018 IEEE 20th
International Conference on High Performance Computing and
Communications; IEEE 16th International Conference on Smart City; IEEE
4th International Conference on Data Science and Systems
(HPCC/SmartCity/DSS) (pp. 1710-1715). IEEE.
Shaw, S., Rowland, Z. and Machova, V., 2021. Internet of Things smart devices,
sustainable industrial big data, and artificial intelligence-based decision-
making algorithms in cyber-physical system-based
manufacturing. Economics, Management and Financial Markets, 16(2),
pp.106-116.
Wamba, S.F. And et.al., 2020. The performance effects of big data analytics and
supply chain ambidexterity: The moderating effect of environmental
dynamism. International Journal of Production Economics, 222, p.107498.
Wang, L., 2021, December. Intelligent Analysis of Accounting Information
Processing Under the Background of Big Data. In 2021 2nd International
Conference on Big Data Economy and Information Management
(BDEIM) (pp. 461-464). IEEE.
Zhao-hong, Y. and et.al., 2018, April. A literature review on the key technologies of
processing big data. In 2018 IEEE 3rd International Conference on Cloud
Computing and Big Data Analysis (ICCCBDA) (pp. 202-208). IEEE.
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