BMP4005 Information Systems and Big Data Analysis Report - Poster

This report provides an analysis of big data and information systems, addressing the characteristics of big data, the challenges of big data analytics (such as lack of knowledge, understanding, and data security), and the techniques currently available for analysis, including A/B testing, data fusion, machine learning, and qualitative/quantitative analysis. It further explains how big data technology supports business through improved decision-making, identification of new opportunities, and enhanced customer experiences. The report emphasizes the importance of leveraging big data to gain a competitive advantage and achieve organizational goals, referencing various sources to support its findings.

BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
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Table of Contents
POSTER..........................................................................................................................................3
INTRODUCTION...........................................................................................................................5
what big data is and characteristics of big data...............................................................................5
The Challenges of big data analytics...............................................................................................6
The Techniques that are currently available to analyse big data.....................................................7
How Big data technology could support business, an explanation with examples.........................8
CONCLUSION................................................................................................................................8

POSTER
Information Systems and Big Data
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).
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,
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.
REFRENCES
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.
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.

INTRODUCTION
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 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 (Nersessian (2018). 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 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
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 (Brayne, (2017).
The Techniques that are currently available to analyse big data
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 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 (Broeders and et.al.,
(2017).
How Big data technology could support business, an explanation with
examples
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. 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).
CONCLUSION
From the above discussion it can be concluded that big data analytics is beneficial for
company because it helps in applying new techniques by which companies can identify choice if
customer. Characteristics of big data analytics, challenges of big data analytics, techniques and
big data technology has been discussed.

REFERNCES
Books and Journals
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.
Nersessian, D., 2018. The law and ethics of big data analytics: A new role for international
human rights in the search for global standards. Business Horizons, 61(6), pp.845-854.
Brayne, S., 2017. Big data surveillance: The case of policing. American sociological
review, 82(5), pp.977-1008.
Broeders, D., and et.al., 2017. Big Data and security policies: Towards a framework for
regulating the phases of analytics and use of Big Data. Computer Law & Security
Review, 33(3), pp.309-323.