Information Systems and Big Data Analysis: Business Management BMP4005

This report provides a detailed overview of big data and its characteristics, emphasizing its growing importance in today's technology-driven world. It explores the challenges associated with big data analytics, such as lack of understanding, issues related to data growth, confusion in tool selection, data security, and integration of data from different sources. The report also highlights various techniques available for analyzing big data, including data fusion, data mining, machine learning, and A/B testing. Furthermore, it explains how big data technology can support businesses through effective data management, enhanced customer engagement, and ensuring data privacy, providing examples like Hilton, H&M and Amazon. The report concludes that big data is crucial for businesses, helping them efficiently manage data, increase customer trust, and gain a competitive advantage.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
Submitted by:
Name:
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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
Poster p
References p
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Introduction
Big data refers to collection of data which is in large in volume and growing
continuously with time. It It consist large size and complexity of data which can be
stored by traditional data management tool in effective manner (Ghasemaghaei and
Calic, 2019). This project report consist description of big data along with its
characteristics. It includes different challenges related to data analytic and includes
different techniques used in current business environment. Moreover, it consist
different ways in which data is used in business.
What big data is and the characteristics of big data
Big data is one of field which treats different ways for analyses, extract or deal with
different data set which are too complex and large in order to dealt with traditional
data processing application software (Ghasemaghaei, 2020). In today's competitive
technology world, big data is emerges as a tool which create impact on many
industry specially in IT industry. There are different types of Big Data technology
which consist Spark, Hive, SQL, Cloud and Spark. It is one of software which helps
to store as well as manage big data which is related to business. It is one of
technology which consist assessment of different factors related to data storage,
data management which are important for purpose of growth of an organization and
also develop synchronization of big data. There are mainly two types of data like
operational data and analytical data. It is important in organization as it ensure
effective resource management and also leads to improve operational efficiency. It
optimism development of products and which helps business to drive revenue and
getting growth opportunities.
Characteristics of Big Data:
Variety: Variety in big data refers to unstructured, structure as well as semi
structured data which is gathered from different source (Grover and Kar, 2017). In
past, data can be collected only from data vases as well as spreadsheet but in
today's time, data can be collected in different forms like videos, emails, audios,
PDF, SM posts, photos and others. Verity in data is important and is one of feature of
big data.
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Velocity: Velocity refers to speed with which data can be created in real time. In a
wider prospect, it comprise linking of incoming set of data with different speeds,
activity bursts and rate of change.
Volume of data: Volume is one of characteristic of big data as it indicate data in
huge volume which can be generated from different source on daily basis like
business process, social media platform, human interaction, machine work and many
more. These large numbers of data can be stored in warehouses.
The challenges of big data analytics
There are different organization which can be stuck in process of managing big data.
These big data are important for growth and success of company (Gupta and Rani,
2019). It is important for them to protect this data from different things. It can be due
to various reason which are as follows:
Lack of understanding of Big Data: Due to insufficient understanding, many time
companies fails to big data initiates. Employees of companies sometimes does not
have understanding about data, ts importance, process, storage as well as source.
Only data professional have proper knowledge of data and other people does not
have clear understanding of it.
Issues related to growth of data: Issues related to growth of data is another
challenge for big data. It is difficult to store these large size of data properly. There is
increase in data which is stored in different data centers and also in data of an
organization which is increased day by day. These data is growing on continuous
basis and it is difficult for company for handle these types of data.
Confusion of Big data tool selection: It is one of another challenge for big data
selection which create confusion for selection of best tool for purpose of big data
analysis along with storage (Manogaran and Lopez, 2017). In order to select best
tool for big data, it is important for them to have understanding of big data.
Securing data: Security is another issues for big data as it is difficult for a company
to protect these large amount of data. Business are often engage in storing,
understanding as well as analyzing of different data set which create different stage
of data security. These data can be lost due to mistake of a person or can be access
through unauthorized use which create problems for business.
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Integration of data from different sources: There are different types of data which
came from different source in business like financial reports, ERP application,
customer logs, social media pages, Presentation as well as reports developed from
employees (Shah, Steyerberg and Kent, 2018). In this combination of different data
for purpose of preparation of report create challenge for business.
The techniques that are currently available to analyse big
data
Data Fusion: Data Insights are potentially accurate and efficient by combining
different set of techniques which helps to analyses as well as integrate data from
various sources as well as solution.
Data mining: It is another tool which is used in anlytics of big data. Data mining
helps to extracts different patterns from big size of data with combining from
machine learning as well as statistics in data base management (Surbakti, Wang,
Indulska and Sadiq, 2020). Data is mined for determining segment which is likely to
react to different offers of company.
Machine learning: It is one of commonly used term in artificial intelligence field and
also used for data analysis (Taleb, Serhani and Dssouli, 2018). It is emerged in
computer science which work in algorithms in computer in order to product
assumptions of data. It offer different prediction which is impossible for analytic of
human.
A/B testing: It is another technique which consist comparison of control group with
different variety of test group that helps to discern different changes and treatment
for purpose of improvement in given objectives.
How Big Data technology could support business, an
explanation with examples
In today's competitive environment, big data helps business to grow and handle
customer data. There are different organization which get competitive advantage on
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basis of big data technology. It support to business with use of different factors
which are mentioned below:
Management of Data: Management of data is important for a business which can
be done with help of big data. It consist use of different advance systems and
software which helps business to manage big data effectively. Thee data can be
managed with big data technology and make it available for them for long period of
time. For instance, Hilton is one of largest hotel chain in world which is managing
customer data effectively in different parts of world with use of big data technology.
Customer engagement: Big data also helps business in increasing engagement of
customers with help of synchronization of data. It is important for IT department to
handle data effectively as per numbers of customers. It is essential priority of
business which helps business with increase in values along with customer
satisfaction that is important to develop trust in business. For instance, H&M is one
of retail organization which is increasingly engaging their customers and also
enhancing their satisfaction with use of big data technology
Privacy of data: Privacy of data is one of important component for both customers
and company. It consist use of big data technology which is important for maintaining
data privacy of customers. It helps to increase faith and loyalty of customers in
company. For instance, Amazon is an organization which is providing different
product and service to their customers is maintaining strong privacy priority which
helps them to protect private information of customers and also increase
CONCLUSION
From above mentioned project report, it can be concluded that Big data is one if
important technology for business which helps them to protect data of company
efficiently. It also helps company to manage data in a way which increase trust of
customers on organization. Big data consist different features like volume of data,
velocity of data as well as variety of data. It includes different challenges for business
like lack of understanding, growth and many more which create impact on
performance of company. There are different tool can be used by company for
purpose of data mining, data fusion, data integration and others. There are different
ways in which big data provide support to business like management of data and
privacy of data and many more.
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Poster
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References
Ghasemaghaei, M. and Calic, G., 2019. Can big data improve firm decision quality?
The role of data quality and data diagnosticity. Decision Support
Systems, 120, pp.38-49.
Ghasemaghaei, M., 2020. The role of positive and negative valence factors on the
impact of bigness of data on big data analytics usage. International Journal
of Information Management, 50, pp.395-404.
Grover, P. and Kar, A.K., 2017. Big data analytics: A review on theoretical
contributions and tools used in literature. Global Journal of Flexible Systems
Management, 18(3), pp.203-229.
Gupta, D. and Rani, R., 2019. A study of big data evolution and research
challenges. Journal of Information Science, 45(3), pp.322-340.
Manogaran, G. and Lopez, D., 2017. A survey of big data architectures and machine
learning algorithms in healthcare. International Journal of Biomedical
Engineering and Technology, 25(2-4), pp.182-211.
Shah, N.D., Steyerberg, E.W. and Kent, D.M., 2018. Big data and predictive
analytics: recalibrating expectations. Jama, 320(1), pp.27-28.
Surbakti, F.P.S., Wang, W., Indulska, M. and Sadiq, S., 2020. Factors influencing
effective use of big data: A research framework. Information &
Management, 57(1), p.103146.
Taleb, I., Serhani, M.A. and Dssouli, R., 2018, July. Big data quality: A survey.
In 2018 IEEE International Congress on Big Data (BigData Congress) (pp.
166-173). IEEE.
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