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

This report discusses the characteristics, challenges, techniques, and business support of information systems and big data analysis. It covers topics such as big data analytics, challenges of big data analytics, techniques for analyzing big data, and how big data technology could support business.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
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Contents
Table of Contents
Introduction...............................................................................................................................1
Poster.........................................................................................................................................4
Introduction p
Information system and big data analytics refers to managing data so large and complex
traditional processing. This software is inadequate to capture, manage and process the data.
Big data is used for predictive and user behaviour (Ahmed and et.al., 2021). The report
examines, big data and characteristics of big data. Challenges of big data analytics,
techniques that are currently available to analyse big data. Further, it is explained that how
big data technology could support business.
What big data is and the characteristics of big data p
Big data is the data which contains greater variety, arriving in increasing volumes.
Simply, big data is larger, more complex data sets and new data sources should be applied.
Big data is a term which describes large, hard to manage volumes of data- both structured
and unstructured. Big data is useful for because it helps in transaction processing system,
documents, emails, customer databases. It is essential to use big analytics so that new
opportunities can be identified. The purpose of big data analytics helps companies to
identify new opportunities. There are three different ways of big data are, structured,
unstructured and semi- cultured data. It is important to use big data analytics so that better
plans can be made to identify new opportunities. Big data skills in 2021 is business
knowledge, data warehousing, quantitative aptitude and statistics, data visualisation,
developing big data skills (Fan and et.al., 2021). Big data can be used for predictive and user
behaviour analytics.
There are some characteristics of big data analytics i.e., volume, velocity, variety, veracity and
value. These are the five elements which help in identifying opportunities which is beneficial
for growth of company. Volume is the base of big data analytics as it helps in managing all the
activities. Velocity helps organisation in managing data and companies need information to
flow quickly. Velocity is beneficial for company because it helps in gaining competitive
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advantage and organisation can make plans accordingly. It is important to use big data
analytics so that better plans can be made for achieving goals and objectives of firm and gain
competitive advantage in market. Variety means there are different sources which can be used
by company to collect data. An organisation can obtain data from many sources like, in- house
devices to smartphone GPS technology or what people are sharing on social networking sites
(Gill, Chana and Buyya, 2017). These are some ways by which it can be identified that what
people want and helps in gaining competitive advantage. Nowadays, social media is the
important tool which can be used by companies and make plans accordingly. Veracity means
to provide good quality products to customers and satisfy their needs. Value means whether
customer will like the product or not. Big data analytics is beneficial for company and use to
identify new opportunities in the market. Different plans are made by an organisation to gain
competitive advantage in market, lead to smarter business moves, more efficient operations,
higher profits and satisfy needs of customers. It is essential to use big data analytics so that
better plans can be made for achieving goals and objectives of company. It is essential to focus
on need of customer by conducting market research, determine view of people on social
networking sites.
The challenges of big data analytics p
There are some challenges of big data analytics which companies has to face and they have
to find solution so that better plans can be made and it helps in accomplishing goals and
objectives of firm. Some challenges of big data analytics are:
Lack of knowledge- this is a challenge for big data analytics because people are not having
proper knowledge and this creates problem. It is important to have proper knowledge so
that goals can be accomplished. Lack of knowledge creates problem because a person is not
having relevant data and this creates issue.
Lack of proper understanding- lack of proper understanding is a challenge because when an
organisation is not bale to understand what is to be done then they make plans which not
beneficial for company. Sometimes, people are not understanding what is to be done and
how to use technology (Liang and Liu, 2018).
Securing data – using new technology is good for growth of company but data security is
the challenge. In many companies, managers face difficulty in data security but there are
many software available which can be used for securing data. It is important to use software
so that data can be secured and information cannot get leaked. It is essential to use proper
security so that competitors did not get information.
Using different sources to integrate information- different sources are used by companies
to gather relevant information. This is a challenge for an organisation because they have to
identify different sources which can be used to collect details and it helps in achieving goals
and objectives of company.
The techniques that are currently available to analyse big data
p
There are different techniques which can be used to analyse big data so that better plans
can be made for achieving targets of company. It is essential to apply techniques because it
helps in identifying new opportunities and profit can be earned. Techniques to analyse data
analytics are:
A/B testing – this technique is a marketing technique which involves comparison between
two versions of a webpage and application. This is used by organisation to identify that
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which performs better. In this A/B testing, A refers to control or original testing variable and
B refers to variation or a new version of original testing (Yan and et.al., 2018). A/B testing is
beneficial for an organisation because it helps in improving user engagement, improved
content, reduced bounce rates, increased conversion rates, increased conversion rates,
higher conversion rates, ease of analysis, quick results.
Data fusion and data integration- this technique is used for combining data residing in
different sources and providing information in a systematic way to the users. Data fusion
means to collect data from different sources and it is not sure that data is accurate,
consistent or useful for the user. Data fusion is important because it is the process of
integrating multiple data sources so that more relevant and accurate information can be
collected. Data integration and fusion technique is available easily, fast connections,
increased efficiency, better customer and partner experience.
Machine learning – big data analytics make a sense of data by uncovering trends or
patterns. This can be divided into different categories like, incoming data, recognise
patterns, helps in translating data into insights which is beneficial for business operation.
Machine learning technique is used by companies to make future plans and to maximise
data’s potential value. It is important for an organisation to apply techniques so that they
can make better plans for future (Zhang, Huang and Bompard, 2018).
Qualitative and quantitative analysis- it means to involve hard data, actual numbers and
subjective description. Qualitative analysis means to measure something by its quality
rather than quantity. It is important to measure data in terms of quality as well as quantity
so that better information can be gathered. It is essential to focus on gathering correct
information. This technique is beneficial because it helps in providing valuable data for use,
user needs are satisfied and correct information is being collected. It is important to analyse
performance so that better plans can be made and goals of company can be accomplished.
How Big Data technology could support business, an explanation
with examples p
Big data technology is supporting business because this is a combination of all the
processes and tools which is related to utilising and managing large data sets. It also help
business to identify new opportunities, create new experiences, services and provide new
products to customers. Big data analytics technology is useful for organisation as it helps in
determining market situation and better plans can be made. For achieving targets, it is
essential for organisation to focus on satisfying need of customer and provide them good
quality products. With the help of big data analytics recommendations and suggestions can
be collected from people and improvements can be made accordingly (Shahbaz and et.al.,
2019). It is essential to use this technology because it helps in identifying choice of people
and different techniques. Big data analytics helps companies to increase sales. Big data
analytics is used to deliver your target audience with services which require analysing needs.
This is beneficial for company in generating more revenue and better products can be
provided to customers.
Big data analytics provide in-depth intelligence to customer lifecycle and identify new
ways to encourages sales of organisation. It is essential to make plans for future growth of
company and which is beneficial in increasing sales of firm. Big data analytics help in
identifying market situation and better plans are made for achieving goals and objectives of
company. It is important to for business to apply big data analytics so that goals of firm can
be achieved. It helps in determining market situation and better strategies can be applied.
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This helps in increasing sales of company because choice of customer is identified and it
helps in generating more revenue (Verma, Bhattacharyya and Kumar, 2018).
Poster
p
POSTER
4

5
Information Systems and Big Data
Analysis
INTRODUCTION
Information system and big
data analytics refers to
managing data so large and
complextraditional processing.
This software is inadequate to
capture, manage and process
the data. Big data is used for
predictive and user behaviour
(Ahmed and et.al., 2021).
REFRENCES
Ahmed, S., and et.al., 2021. Indirect determination of serum creatinine reference intervals in a Pakistani pediatric population using big dat
analytics. World Journal of Clinical Pediatrics. 10(4). p.72.
Fan, C., and et.al., 2021. February. Advanced data analytics for enhancing building performances: From data-driven to big data-drive
approaches. In Building Simulation (Vol. 14, No. 1, pp. 3-24). Tsinghua University Press.
Challenges of big data analytics
There are some challenges of big data
analytics which companies has to face and they have
to find solution so that better plans can be made and
it helps in accomplishing goals and objectives of firm.
Some challenges of big data analytics are:
Lack of knowledge- this is a challenge for big data
analytics because people are not having proper
knowledge and this creates problem. It is important to
have proper knowledge so that goals can be
accomplished. Lack of knowledge creates problem
because a person is not having relevant data and this
creates issue.
Lack of proper understanding- lack of proper
understanding is a challenge because when an
organisation is not bale to understand what is to be
done then they make plans which not beneficial for
company. Sometimes, people are not understanding
what is to be done and how to use technology (Liang
and Liu, 2018).
Big data and characteristics of big
data
Big data is the data which contains greater
variety, arriving in increasing volumes. Simply,
big data is larger, more complex data sets and
new data sources should be applied. Big data is
a term which describes large, hard to manage
volumes of data- both structured and
unstructured. Big data is useful for because it
helps in transaction processing system,
documents, emails, customer databases. It is
essential to use big analytics so that new
opportunities can be identified. The purpose of
big data analytics helps companies to identify
new opportunities. There are three different
ways of big data are, structured, unstructured
and semi- cultured data.
Techniques
A/B testing – this technique is a marketing technique
which involves comparison between two versions of a
webpage and application. This is used by organisation
to identify that which performs better.
Data fusion and data integration- this technique is
used for combining data residing in different sources
and providing information in a systematic way to the
users. Data fusion means to collect data from
different sources and it is not sure that data is
accurate, consistent or useful for the user.
Machine learning – big data analytics make a sense of
data by uncovering trends or patterns. This can be
divided into different categories like, incoming data,
recognise patterns, helps in translating data into
insights which is beneficial for business operation.
Machine learning technique is used by companies to
make future plans and to maximise data’s potential
value.
Big data technology
Big data technology is supporting
business because this is a
combination of all the processes and
tools which is related to utilising and
managing large data sets. It also help
business to identify new
opportunities, create new
experiences, services and provide
new products to customers. Big data
analytics technology is useful for
organisation as it helps in determining
market situation and better plans can
be made. For achieving targets, it is
essential for organisation to focus on
satisfying need of customer and
provide them good quality products.

References
Ahmed, S., and et.al., 2021. Indirect determination of serum creatinine
reference intervals in a Pakistani pediatric population using big
data analytics. World Journal of Clinical Pediatrics. 10(4). p.72.
Fan, C., and et.al., 2021. February. Advanced data analytics for enhancing
building performances: From data-driven to big data-driven
approaches. In Building Simulation (Vol. 14, No. 1, pp. 3-24).
Tsinghua University Press.
Gill, S. S., Chana, I. and Buyya, R., 2017. IoT based agriculture as a cloud
and big data service: the beginning of digital India. Journal of
Organizational and End User Computing (JOEUC). 29(4). pp.1-23.
Liang, T. P. and Liu, Y. H., 2018. Research landscape of business
intelligence and big data analytics: A bibliometrics study. Expert
Systems with Applications. 111. pp.2-10.
Shahbaz, M., and et.al., 2019. Investigating the adoption of big data
analytics in healthcare: the moderating role of resistance to
change. Journal of Big Data. 6(1). pp.1-20.
Verma, S., Bhattacharyya, S. S. and Kumar, S., 2018. An extension of the
technology acceptance model in the big data analytics system
implementation environment. Information Processing &
Management. 54(5). pp.791-806.
Yan, H., and et.al., 2018. Industrial big data analytics for prediction of
remaining useful life based on deep learning. IEEE Access. 6.
pp.17190-17197.
Zhang, Y., Huang, T. and Bompard, E.F., 2018. Big data analytics in smart
grids: a review. Energy informatics. 1(1). pp.1-24.
p
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Introduction
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What big data is and the characteristics of big data
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The challenges of big data analytics
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The techniques that are currently available to analyse big
data
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How Big Data technology could support business, an
explanation with examples
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Poster
References
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