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

This report covers the characteristics and challenges of big data analytics, techniques used in data analytics, and how big data technology can support businesses. It also discusses the importance of information technology in storing data for business organizations. The course code for this report is BMP4005.

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
Information technology is an essential study of integral networks of software and
hardware that companies and people use to filter, process, distribute, collect and
create data (Li and Chan, 2019). It comprises of various components that go hand in
hand to providing value to a company. In this file, the importance, characteristics and
challenges of big data will be covered that occur in its analytics, providing valuable
information regarding it's functioning.
What big data is and the characteristics of big data
Data is a set of information used by machines and computers to perform
operations, it can be shared with the help of electric signals. Big data is the same as
data but bigger, it contains data in huge volume which keeps on increasing and
cannot be processed by traditional data management tools. There are five Vs that
are described as five characteristics of big data which are as follows:
 Volume: -Volume refers to number and big data comprises of enormous
number of data compiled together, this amount of data does come from
different sources like machines, social media, human interactions, business
network (Khan and et. al., 2019). A flying jet alone generates 10 terabytes of
big data in 30 minutes, facebook generates 500+ terabytes of data per day,
these examples show that big data carries huge volumes of data.
 Variety: -It is obvious that if data is big, it would also have some variety as big
data comes from various sources and can be different forms like structured,
semi structured, quasi-structured, unstructured. Each of these following is
described below-
 Structured data: It can be said that the structured data is categorized by
columns that are essential for its detailing and is in tabular form. Database
in the management system that is relational stores it.
 Semi-structured data: The schema in this type of data is not defined in a
proper manner, online transaction processing systems are created to work
with this type of data.
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 Quasi-structured data: The format in this type of data contains
inconsistent data formats in textual form which are formatted with time and
effort.
 Unstructured data: This type of data contains files that are in the form of
audio files, images, log files, this data is raw and many organisations are
unable to derive the value of such data.
 Veracity: -It means the process of managing data effectively and handling it
in a reliable manner, it contains various ways to translate and filter data (Al-
Mekhlal and Khwaja, 2019). Big data is needed in business development and
the examples of it includes facebook posts containing hashtags.
 Value: -It is the data which essential as it is valuable and reliable source of
big data that is stored processed and analysed.
 Velocity: -It is one of the most important characteristic as it is the speed of
real time taken for data to process. It contains many factors like rate of
change, data sets speeds, activity bursts and its primary aspect to provide
data in demand rapidly. Velocity in big data refers to the speed of data flows
to different sources including business processes, social media sites, mobile
devices and many more.
The challenges of big data analytics
 Lack of professionals: It is not an easy task to and analyse big data, it is
rather a very complex process, it requires highly qualified, skilled individuals
and teams to to work on big data analytics, the tools required in the process
have evolved a lot but many working professionals have not, so there is a lack
of professionals in this area (Hariri, Fredericks and Bowers,2019).
 Lack of proper understanding: Due to inadequate understanding the
companies are failing in the initiative of big data analysis.
 Data growth issues: The quantity of data that exists is increasing rapidly and
its storage and management requires more and more professionals that are
skilled enough to deal with it. As mostly this data in unstructured, its a
challenge to manage and handle it, its mostly in the form of images,
documents, audio and other forms. With growing data it is creating more
challenges to find more skilled professionals that are enough to handle this
issue.
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 Integration of data: Data comes from different sources in corporations like
applications, social media, financial reports, e-mails and more. Combining this
data and organizing it is a hard job, but it is very essential for analysis,
business intelligence, etc.
 Securing data: Securing the data requires a lot of knowledge, most of the
time organisations spend a lot of time in organising, storing and analysing
data and its securing is seen as the last stage (Yahya-Imam and Aranuwa,
2020).
The techniques that are currently available to analyse big
data
Data analysis can be said as the process of tracking down important
information by analysing data, it is time taking and is done through a long process. It
is a meaningful process that helps various companies to take important steps and
make business strategies.
There are various techniques used in data analytics and are as follows:
 Sentiment analysis: When data is mentioned, the focus goes to numbers
which is quantity but qualitative data has its own value which may often be
overlooked and sentiment analysis focuses thoroughly on it. It is a text
analysis of broader category which let's an organisation know how it's
customers feel about it in a textual form, as it analysis their emotion with the
data evaluation of their sentiments (Farouk and Zhen, 2019).
 A/B testing: This method includes randomized experiment where two
components or variables are compared with each other like a part of statistics,
it is helpful as it is variated and let's an organisation understand things with
two different perspective or styles. Different consumers behave differently so
having more than one variant helps the firm in a marketplace, it helps in
figuring out the target audience and understand what is best strategy for the
business.
 Classification: In this method there are dataset which are categorized by a
common factor, for example the amount of people that posted picture on
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social media with happy hashtag or the amount of people that used the
hashtag sad will all be categorized in the same dataset. It finds a common
factor in the commodities and helps in piling them together based on that.
 Social network analysis: This method involves organizing of human
relations in the form of graphs, these graphs are referred as Sociograms and
all the lines and points that are stored in the matrix are known as Sociomatrix.
It helps in identifying trends related to brands and marketing, evaluate human
interactions, emanate sentiments of consumers towards services and
products (Choi Wallace and Wang, 2018).
How Big Data technology could support business, an
explanation with examples
 Better customer insight: Customer insight can come through many ways but
one of the best ways is with the help of big data. Big data taking help from
social media to websites can bring a lot of insights about customers and helps
in building customer personas with their profiles. With all this information big
data helps in creating the best products suitable for customers and providing
them with the best services possible which results in brand loyalty from
customer's side.
 Risk management: Risks are everlasting and should not be taken lightly by
any organisation big or small, there are many methods to analyse risks and
big data is an effective one. It helps the companies to list the number of risks
and categorize them according to their level, so that business can avoid
confronting situations and provides the business with social and economic
insights.
 Knowledge about competitors: Big data can provide adequate amount of
knowledge related to the customers of another firm or direct competitors in the
marketplace, it can provide stats about their sales, pricing of their products,
lists of products and services, their customer base, good and bad reviews of
their products and services and more.
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 Personalized marketing: With the help of big data organisations can target
customers directly by knowing their preferences and the customers that are a
decent match for the campaign, can be aimed to promote the products and
services the organisation or the company offers (Maheshwari, Gautam and
Jaggi, 2021).
 Identify trends: With the help of big data trends can be analysed which
ultimately would help in manufacturing and developing products and services
that would sell fast in the market as the need and want can be identified with
the use of big data. It also helps the organisation to identify how often the
given trends change and the time they might give the business to prepare for
the next.
 Improve business operation: With proper business knowledge, any firm can
bring necessary changes that are needed to improve the functioning of
respective firm. Big data helps in that by providing business insights like cost
savings, boost productivity, employee satisfaction and outside factors like
fraud detection, risk management, cybersecurity. All these components
ultimately give the organisation protection against these threats and help it to
improve its business operations.
CONCLUSION
From the above report it has been concluded that information technology
plays a very important role for the business organisation in storing their data in large
manner. This report concluded the characteristics of big data and challenges faced
by the organisation in relation to big data. In addition to this, this report also
concludes the techniques which can be used by the organisation to analyse the big
data.
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References
Li, T.C. and Chan, Y.E., 2019. Dynamic information technology capability: Concept
definition and framework development. The Journal of Strategic Information
Systems, 28(4), p.101575.
Khan, N and et. al., 2019, May. The 51 v's of big data: survey, technologies,
characteristics, opportunities, issues and challenges. In Proceedings of the
international conference on omni-layer intelligent systems (pp. 19-24).
Al-Mekhlal, M. and Khwaja, A.A., 2019, August. A synthesis of big data definition and
characteristics. In 2019 IEEE International Conference on Computational
Science and Engineering (CSE) and IEEE International Conference on
Embedded and Ubiquitous Computing (EUC) (pp. 314-322). IEEE.
Hariri, R.H., Fredericks, E.M. and Bowers, K.M., 2019. Uncertainty in big data
analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1),
pp.1-16.
Yahya-Imam, M.K. and Aranuwa, F.O., 2020. An empirical study on big data
analytics: Challenges and directions. Intelligent Computing and Innovation
on Data Science, pp.669-677.
Farouk, A. and Zhen, D., 2019. Big data analysis techniques for intelligent
systems. Journal of Intelligent & Fuzzy Systems, 37(3), pp.3067-3071.
Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data analytics in operations
management. Production and Operations Management, 27(10), pp.1868-
1883.
Maheshwari, S., Gautam, P. and Jaggi, C.K., 2021. Role of Big Data Analytics in
supply chain management: current trends and future
perspectives. International Journal of Production Research, 59(6), pp.1875-
1900.
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Appendix 1: Poster
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