Information Systems and Big Data Analysis for Business Report

This report provides a comprehensive overview of big data analysis and its applications in business management. It begins with an introduction defining big data and its significance, followed by an exploration of its key features, including volume, variety, velocity, veracity, and variability. The main body of the report delves into the challenges associated with big data, such as a lack of data understanding, data growth issues, security concerns, organizational resistance, synchronization problems, and cost management. It then examines various big data techniques, including machine learning, regression analysis, genetic algorithms, and A/B testing. The report further illustrates how big data technology can assist businesses in customer communication, risk analysis, data security, product redevelopment, and revenue generation, with practical examples. Finally, the report includes a poster summarizing key findings and a detailed references section.

Information Systems
and Big Data Analysis

Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
Big data and its features -............................................................................................................3
Big Data Challenges -.................................................................................................................4
Big data techniques presently available - .................................................................................5
How the technology of the big data can assist the business , with examples - ..........................6
POSTER ..........................................................................................................................................7
REFERENCES................................................................................................................................8

INTRODUCTION
This report will evaluate the business management with the help of big data analytics to
achieve organisational objectives. Big data sets has a very large group of volume that involve
the enormous sets of data that include the social media analytics. Big data is so large that none
of the traditional data management tools can store it or process it efficiently. (Soares, 2022) This
process involves data analysis, cleansing, visualization , integration and management. By using
big data accumulated process in their system company can enhance their operations. Big data
helps in growing an exponential rate of a company. It helps in taking decisions for the
betterment of the company.
MAIN BODY
Big data and its features -
Big data is the most important system for the company in development of technology,
computer system, industry and business. It is a phase which is very enormous in considering a
group of data sets that is very complicated to process by using the process of legacy data
application. It is a data which is very huge and complicated to deal with the process of
conventional data application software. The evaluation of big data represents the summons like
segmenting and previously permitting for only monitoring and segmenting. It does not only
refers to the data but it also refers to the multiple ideology, tools and techniques. Big data helps
organisations and teams to perform multiple operations on a single platform.
Features -
 Volume - Volume means the size or area of the data set. Volume refers to the size of
data generated and stored in a big data system. Its range of volume justifies whether it
should be considered as 'big ' or not (Guha, and Kumar, S., 2018). In order to identify the
that a specific type of data is under the establishment of big data identity or not, it totally
reliable on volume.
 Variety - It is all related to structured or unstructured data that has a probability to bring
out it either by peoples or computers or can also say that through machines. It reflects the
diversity of all the data types and it makes the data very huge. Variety gets generated

from various sources. But in today's time data comes in a various form like images,
audio, videos and etc.
 Velocity – Velocity means the speed of the data . From a research , velocity refers that
how fast the data can be generated and how rapidly its going on and will determines the
capacity of the data .
 Veracity - Veracity means quality of data that is being analysed(Lee, S. and Huh,
2019). It is one of the feature of the big data that analyse the characteristics like noise ,
abnormality any kind of disturbance . Basically it defines the data that how perfect or
accurate it is .
 Variability – Variability refers to the few different things and number of inconsistencies
in the data. It is totally different from the variety in the data. It basically refers to the data
which gets change continuously.
Big Data Challenges -
While dealing with big data analytics some of the major challenges comes in between
here are the challenges given below-
 Lack in understanding of data - Companies are not able to understand things properly
its all because of incomplete information and some misunderstanding or lack of
communications. Employees might not know what data is. Its storage, processing,
importance and sources(Zhang, and et, al., 2021). Perfectionist in data may be know
about what's going on but the others in the company might not have any idea. All levels
of organisation must inculcate a basic understandings of knowledge concepts.
 Issues in data growth - This is the foremost pressing challenges of massive data which
are capturing these big sets of knowledge properly, the quantity of knowledge being
stored in data centres and then the biggest challenge comes in between to analyse and
store all the information. Data sets increase rapidly with the time and it is getting more
difficult to apply.
 Security of Data – For company, security of these big sets of knowledge is the most
important challenge comes in between while dealing with it. Sometimes the companies

are so engaged in storing and analysing the data sets of the company which they forward
security of data for future.
 Organisational Resistance- It is one of the important challenge faces by the business
leader of the company and also they have to handle the present inappropriate
organisational resistance. (Peters, and et, al., 2020). this is also creating an impact on the
others sides of the business. It is a complication for the companies but they can solve the
problem in a best possible way .
 Synchronization – It is a technology which plays an very important in the big data
implementation. The biggest problem comes in between is data consistency and
integration of data .
 Handling Costs - In handling the big data system the company have to manage a lot of
expenses. Companies mostly money dependable on these things like hiring employess,
data analyst, technology, development cost etc. .
Big data techniques presently available -
Data analytics techniques are given below-
 Machine learning – It is a technique used by the company to categorise or describe the
prescribed outcome of a certain data (Madrid, and et, al., 2019). It used to analyse or
forecast the trends of the business. With the help of this technique . A network can
constantly learn new things.
 Regression Analysis – It predicts the continuous value based on the variables. It is one
of the important technique that organisation used. It is used to establish a relationship
between the independent variables and dependent variables. It involves a various
variations like linear, multiple linear, and non-linear.
 Genetic Algorithm – It is totally based on evolution that circulate around using
mechanism. Basically, it is a technique that is used to find correct or appropriate answers
to solve the problems that is used in computing. It is a search based algorithm.

 A / B testing – This is an experiment on the two variants to watch which variant
performs the best on the basis of a given metric (jiang and et, al., 2019). It is an
optimisation technique often used to understand how an altered variable affects audience
or user engagement. It is a common method used in marketing, web design to improve
campaigns and goal conversion rates .
How the technology of the big data can assist the business , with examples -
 Communication with customers - In today's segment, consumers are so sharp and
intelligent that they know their preferences. This is the key advantage of the big data
system that describes the interaction or relationship with consumers. Big data permits a
organisation to reach customers in a better serving way. It help businesses to have face
face conversations with consumers.
 Risk analysis – It refers to the analysis of firm which will affect the business report. Big
data helps to identify and forecast risk that can harm the business(Lu, and et, al., 2019).
With the help of proliferation of cybercrime, big data analysis can help to detect patterns
that indicate a potential cybersecurity threat to the business.
 Security of data - Big data tools & techniques allows to cover the whole data of the
company. There are many challenges comes in between to secure the data of the
company because it is confidential data . Attacks, threats, cyber crime that could harm the
business in any kind of prospect .
 Re- development of products - It helps organisation in a best possible way to gather
and use that feedback. It helps organisation to re produce the product according to the
consumer preferences with a re develop or new launched products and implement them
efficiently(Kolisett and Rajput, D.S., 2020).
 New Revenue System – It provides insights from evaluating the consumers and market.
But however, this data is not only valuable for the firm and also for the other
parties(Palacio, and López, 2018).

POSTER

REFERENCES
Books and Journals
Soares, R.R., 2022. The evolving field of Big Data: understanding geographic information
systems analysis and its transformative potential in ophthalmic research. Current
Opinion in Ophthalmology, 33(3), pp.188-194.
Guha, S. and Kumar, S., 2018. Emergence of big data research in operations management,
information systems, and healthcare: Past contributions and future roadmap. Production
and Operations Management, 27(9), pp.1724-1735.
Lee, S. and Huh, J.H., 2019. An effective security measures for nuclear power plant using big
data analysis approach. The Journal of Supercomputing, 75(8), pp.4267-4294.
Zhang, and et, al., 2021. Big data analytics and machine learning: A retrospective overview and
bibliometric analysis. Expert Systems with Applications, 184, p.115561.
Peters, and et, al., 2020. Product Decision-Making Information Systems, Real-Time Big Data
Analytics, and Deep Learning-enabled Smart Process Planning in Sustainable Industry
4.0. Journal of Self-Governance & Management Economics, 8(3).
Madrid, and et, al., 2019. Big data and machine learning in critical care: Opportunities for
collaborative research. Medicina intensiva, 43(1), pp.52-57.
Jiang and et, al., 2019. Big data analysis based network behavior insight of cellular networks for
industry 4.0 applications. IEEE Transactions on Industrial Informatics, 16(2), pp.1310-
1320.
Lu, and et, al., 2019, May. Geographic information systems and big data driven framework for
planning and design of smart cities. In 2019 4th International Conference on
Information Systems Engineering (ICISE) (pp. 6-10). IEEE.
Kolisetty, V.V. and Rajput, D.S., 2020. A review on the significance of machine learning for
data analysis in big data. Jordanian Journal of Computers and Information Technology
(JJCIT), 6(01), pp.155-171.
Palacio, A.L. and López, Ó.P., 2018, May. From big data to smart data: A genomic information
systems perspective. In 2018 12th International Conference on Research Challenges in
Information Science (RCIS) (pp. 1-11). IEEE.