BSc Business Management BMP4005 Information Systems & Big Data

This report provides an overview of big data, including its characteristics, challenges, and available analysis techniques. It explores how big data technology supports businesses by enabling better decision-making, improving existing products, ensuring data safety, aligning with customer needs, facilitating automation, and promoting effective resource utilization. The report also covers data integration issues, the need for skilled professionals, and growth challenges associated with big data. The document concludes that big data plays a crucial role in defining a company's success by aiding managers in making informed decisions through data analysis and interpretation. This student contributed assignment is available on Desklib, a platform offering a wide range of study resources including past papers and solved assignments.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Summary 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
References p
Appendix 1: Poster p
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Introduction
Big data refers to the data that is large in quantity, and it becomes difficult to process by
using traditional methods. It involves accessing and storing the essential data in terms of
information in order to obtain analytics. In earlier time, it becomes complex to process the
big data without using technology. This project report will cover the basic concepts of big
data along with its features. In addition to that, it will also cover the challenges along with
its solution so that it may contribute in solving and processing such big data by using
technology. With the help of big data technology, it allows the business owners to
generate higher profits along with increased productivity.
What big data is and the characteristics of big data
The term big data may be defined as the indicator that indicates the large amount of data
that are used by data analyst in order to reach at conclusion along with its solution. It
becomes complex to process such larger data such as in processing and interpreting in
order to obtain accurate analytics. There are various sources by which data can be
collected such as by surveys, feedback forms and other digital platforms. The data is
collected by companies to get insight of their customer preference and their buying
patterns so that they can fulfil their demand by producing a product that can satisfy
customer’s needs and wants. Following are the characteristics of big data.
 Volume – There are various data that big data consist and have larger in quantity.
It is needed to have effectively manage all the data and arrange in a structured
manner that makes the process quite easy. The procedure must be in organized
manner so that it becomes easy to process data.
 Variety – The data can be of many types such as structured or semi structured.
The structured data is set in a structured and organized way such as in tabular or
graphical form and makes it easy for user to understand. The unstructured data is
not in a prescribed format and not arranged in a structured manner. The data can
be of homogenous type or heterogeneous.
 Velocity – It may be defined as the speed at which the data is generated or
collected by any source. The data collection process speed is known as velocity. It
helps in fulfilling the demands of clients and customers.
 Veracity – It refers to the truthfulness of data and shows the accuracy or validity
that is being collected. It becomes difficulty to sort data which is not in structured
manner. The validity of data reflects the accuracy.
 Value – It is one of the main element of big data and can be derived from effective
and efficient operations, building strong relationship with customers.
The challenges of big data analytics
Integrating data from various sources – Big data consist of various data
combinations that are taken from different sources and it becomes difficult to
integrate such big data. The data can be collected from social media handles,
email and presentation and reports. It becomes essential to analyze data and
interpret.
 Lack of skilled professional – It is needed to have skilled professional that will
help in understanding the trends and can process data in order to obtain
accurate results. The new professional should be provided with training
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sessions that will help in better understanding about data. With effective training
sessions, the company can lower down their hiring cost of new individuals.
Growth issues – It becomes difficult to process such big data as they continuously
growing in size. So it is needed to have latest technological software that makes
the storing and processing such big data.
The techniques that are currently available to analyse big
data
There are various techniques that can be used to analyze data.
 A/B Testing – This technique is also known as bucket testing. This method is used
to assess the effectiveness of the options that are available. By opting this
method, individual can opt the best alternative from the two options. There are
various steps that are involved in A/b testing such as collection of data and opting
the two best alternative available.
 Language processing – This technique is used in computer science and artificial
intelligence. In this method various algorithm are used to solve specific problems.
Flow chart is used to represent algorithm in a graphical way. It provides the data
that can be easily understand by humans.
 Statistics – In this technique, it involves the collection of data, organized in a
structured manner along with analyzing so that it can provide with effective and
accurate analysis. The decision is based on the secondary and primary data.
Primary data is first hand data and it is collected by individual itself whereas on the
other hand, secondary data is existing data that can be taken from internet and
other magazines and brochures.
 Data fusion and Integration – Data fusion may be defined as the process of
breaking down the data into different parts. Whereas the integration may be
defined as the process of organizing data into a single database.
 Data Mining – It may be defined as the process of finding the correlations and
patterns who is having a large number of data. Data Mining is the process by
which data can be extracted and analyzed. Descriptive and Predictive asre some
types of data mining.
How Big Data technology could support business, an
explanation with examples
Big data helps in understanding various business trends along with understand the
behavior of customer towards company’s product. Following are the ways in which data
analytics supports business.
 Helps in taking decisions –It helps in taking effective decision as it allows to get
data and based on that manager can take effective decisions that will help in
attaining the company’s goals. It becomes difficult for manger to take decisiomn
form such big data. Effective decisions are the basis of every company and it
contributes in attaining objectives in a time frame period.
 Improving existing products – Big data can be used in taking feedbacks from their
customers. The feedback can be either positive and negative and it allows the
company to take effective measures that will help in im0proving its vexisting
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products by bringing innovation. The products should be in favour of customer and
have the ability to satisfy customer need and wants.
 Safety of data - The software that is used in analyzing the data have the feature
to safe. They protect the data from malware and Trojan attacks which may lead to
lost data. It keeps the data safe and secure so that it can be retrieved at time of
needs.
 Align with customer – It is very important for every business to work according to
needs and wants of customer. They should be customer centric and which helps
them to align their activities with customer preference. Big data can be used as a
tools that allows firms to align their task in an efficient manner.
 Facilitates automation – It helps in enhancing the efficiency of firm by using robotic
gadgets that have the capability of doing repetitive work. Companies use various
software that makes the process easier and automated.
 Effective utilization of resources – It is very important for the company to utilize the
resources in such a manner that yields higher profit. The resources must be used
in an effective manner along with minimizing the wastage so that company can
generate higher revenue.
Conclusion
From the above project report, it was concluded that big data plays a crucial role I
defining the success of company. It helps the manager in taking effective decisions by
creating an insight of collected data along with analyzing in order to obtain accurate data.
There are various types of data such as structured and unstructured that helps in
formulating effective business strategies so that company can achieve its objectives in an
efficient manner. There are various software that keeps the data safe from cyber-attack
and from virus. The professionals must be provided with effective training sessions so
that they can be used in successful data analyzing and interpreting.
References
Zomaya, A.Y. and Sakr, S. eds., 2017. Handbook of big data technologies.
Saggi, M.K. and Jain, S., 2018. A survey towards an integration of big data analytics to big insights for
value-creation. Information Processing & Management, 54(5), pp.758-790.
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.
Mishra, D., Luo, Z., Jiang, S., Papadopoulos, T. and Dubey, R., 2017. A bibliographic study on big data:
concepts, trends and challenges. Business Process Management Journal.
Gupta, D. and Rani, R., 2019. A study of big data evolution and research challenges. Journal of
information science, 45(3), pp.322-340.
Qin, S.J. and Chiang, L.H., 2019. Advances and opportunities in machine learning for process data
analytics. Computers & Chemical Engineering, 126, pp.465-473.
Appendix 1: Poster
Paste your digital poster here
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