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

This report covers characteristics of big data, challenges of big data analytics, techniques that are currently available to analyze big data, and how big data technology could support business with appropriate examples. Managing big data is essential for companies because it will help them to make effective decisions for their growth. However, managing big data within a company is also challenging because a special type of knowledge is required to manage such a huge amount of data, and companies have to hire these professionals at high salaries. Big data technology can support businesses in many ways by increasing their productivity and profitability.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Summary Paper
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Contents
Introduction 3
What big data is and the characteristics of big data 3
The challenges of big data analytics 4
The techniques that are currently available to analyse big data
5
How Big Data technology could support business, an explanation with
examples 6
Conclusion 7
References 8
Appendix 1: Poster 9
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Introduction
Big data is consist of collecting, storing and managing huge data sets of companies
effectively to achieve organizational goals. Today, most of the companies use
various kinds of software and methods to manage their big data (Hariri, Fredericks
and Bowers, 2019). The data which is collected and stored by companies are related
to customer's purchase history, inventory records, and many others. This data is
used for making decisions for organizational growth. The following reports cover
characteristics of big data, challenges of big data analytics, techniques that are
currently available to analyze big data ,and the reason tow big data technology could
support business with appropriate examples.
What big data is and the characteristics of big data
Big data consist of those data which are too large in size and which are tough to
manage with traditional method of data management. There are few of the
characteristics of big data which are mentioned below-
1. Value- It consist of importance of data. The companies are required to collect
only those data which helps them to improve their productivity and profitability
(Müller, Fay and Vom Brocke, 2018). Most of the companies mainly focus to
collect and manage those data which will help them to maintain good
relationship with their customers.
2. Volume- It consist of amount and size of big data which is analyzed by
company. Usually big data consist of those data which are stored in huge
numbers even at millions.
3. Velocity- It include the speed at which any company collect, store and
manage data for their organizational use (What are the 5 V's of Big Data?,
2022). It is important for companies to increase this speed and maintain the
speed high so that they can perform their operations quickly by taking quick
decisions which are based on such big data sets.
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4. Variety- It consist of different data types which are required to store by
companies. There are different kinds of data such as structured, unstructured
and semi-structured.
5. Veracity- It consist of truthfulness of data. The companies are required to
store actual data and avoid to collect hypothetical data because it collected
data sets are used by executives to make decisions for organizational benefit
and in case wrong data is collected by employees than executives will also
make ineffective decisions which are based on wrong data collected by
company.
6. Variability- It consist of changing nature of data. A company can feed new
data in their records and even modify the existing data according to their
requirements.
The challenges of big data analytics
1. Lack of professional knowledge- Normal employees are unable to run the
technology of big data management. Hence, companies are required to hire
special professional employees for management of big data of their
companies. Data scientists, data engineers and data analysts are few of the
main professions of big data (Shankarnarayan and Ramakrishna, 2020).
Companies are required to pay high amount of salaries to such employees.
Hence, its challenging and costly for companies to find special skilled
employees for managing their big data.
2. Lack of proper understanding of big data- Employees of a company are
unable to understand the importance of data for their organizational growth
and hence few of the time they avoid to store important data for company in
their database. Proper training and meetings should be held on timely basis to
enhance employees knowledge about importance of big data management for
organizational growth.
3. Data growth issues- This is one of the biggest issue for managing big data
where companies store data in a huge number and this data continues to
grow and hence, due to management of such big amount of data sets,
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companies ignore to store some data which may consist important for future
use.
4. Confusion while big data tool selection- There are various kinds of tools
used to manage big data (Boubiche and et. al., 2018). Companies get
confused to use which tool for their organizational benefit. Hence, they are
required to analyze the advantages and disadvantages of big data tools and
then use that specific tool which have more advantage for their organisation.
5. Integrating data from a spread of sources- There are various kinds of
sources of data collection line social media, feedback, customer's purchase
history and many others. The company is require to analyze the correct and
true source of data collection and did not forget to analyze the reliability of
data. Another big challenge for the company is to combine the data from
various sources and prepare a single report.
6. Securing data- Another challenge for managing big data is to keep it secure
and safe from external threats. Managing big data through internet sources
will also increase the chance of hacking data by external hackers and use the
data to damage organizational productivity and reputation. Hence, it is
important for the company to secure their data related to their customers,
employees and organisation effectively.
7. Finding and fixing data quality issue- Data management can be harmful for
company if data quality is bad which means the data which is saved by
companies are collected from wrong source or collected incomplete. This can
harm the overall productivity and profitability of the company because
incomplete data collection activities will not provide appropriate direction to
employees.
The techniques that are currently available to analyse big
data
1. Association rule learning- This is the technique which will help to analyze
the correlations between dependent data and independent data by a company
(Cabrera-Sánchez and Villarejo-Ramos, 2020). Retailing industry was the first
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who use this techniques within their supermarkets to analyze relationship
between products and customers interest.
2. Classification tree analyses- This is the technique where companies identify
that their which document is related to which category (7 Big Data Techniques
That Create Business Value, 2022). Hence, this technique classify the
documents automatically according to its classification. This technique is
further used to develop profiles of students who take online courses.
3. Genetic algorithms- This is the technique which is used to identify the
specific solution of the give problem. It mainly used for examine time slots to
manage tasks properly and to maximize the organizational profit. This method
is used in hospitals to schedule doctors for hospital emergency rooms.
4. Machine learning- This is the technique which uses advance and updated
software for managing big data for companies. This technique will help the
company that whether they are collect6eing reliable data from reliable source
or not. For example, this technique will help the company to distinguish
between spam and non-spam e-mails.
5. Monte Carlo simulation- This is the technique which is used to generate
models for possible outcomes of decision making by executives (Naqvi and
et. al., 2021). This will help to forecast the outcome by a decision. For
example, a company took a decision to expand their company to new market
than with the help of this technique company can analyze various kinds of
opportunities as well as risk of expanding their business.
How Big Data technology could support business, an
explanation with examples
Big data technologies can support any business to achieve their
organizational goal and have effective growth. Today most of the companies use
updated and advance software for managing their big data effectively so that they
will record each and every kind of essential information within their data base which
ca be use din decisions making. Customers purchase history is recorded by the
companies through effective tools and software and this can be further used to
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analyze that which product is more purchased by customers and which is not so that
company will invest appropriately. On the other hand, big data technologies will also
help the company to forecast the risks of decision which they are planning to make
for their organizational growth. Hence, with the help of big data technology a
company can forecast the outcomes of any decision and analyze the advantages
and disadvantages for a company to make particular decision. For example, a
company like Tesco which is a well kn own retailing company of UK is using
advance technology for managing their big data effectively. They want to expand
their supermarket and launch a supermarket store within new location. Here, the
executives of company will analyze the data which they have already stored and
managed well with the help of big data technology to examine which products and
services will be invested high at new store to gain maximum profit.
Conclusion
From the above information it is concluded that managing big data
is essential for companies because it will help them to make effective
decisions for their growth. There are various characteristi9cs of big data
such as volume, velocity, value and many others. It is also concluded that
managing big data within a company is also consist challenging because a
special type of knowledge is required to manage such huge amount of
data and company have to hire these professional at high salaries. There
are few techniques to manage big data such as Association rule learning,
Classification tree analyses and few others. It is also concluded that big data
technology can support business in many ways by increasing their productivity and
profitability.
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References
Boubiche, S. and et. al., 2018. Big data challenges and data aggregation strategies
in wireless sensor networks. IEEE access, 6, pp.20558-20571.
Cabrera-Sánchez, J.P. and Villarejo-Ramos, Á.F., 2020. Acceptance and use of big
data techniques in services companies. Journal of Retailing and Consumer
Services, 52, p.101888.
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.
Müller, O., Fay, M. and Vom Brocke, J., 2018. The effect of big data and analytics on
firm performance: An econometric analysis conside
Naqvi, R. and et. al., 2021, June. The nexus between big data and decision-making:
A study of big data techniques and technologies. In The International
Conference on Artificial Intelligence and Computer Vision (pp. 838-853).
Springer, Cham.
Shankarnarayan, V.K. and Ramakrishna, H., 2020. Paradigm change in Indian
agricultural practices using Big Data: Challenges and opportunities from field
to plate. Information Processing in Agriculture, 7(3), pp.355-368.
Online
What are the 5 V's of Big Data?, 2022 [Online] available through:
<https://www.teradata.com/Glossary/What-are-the-5-V-s-of-Big-Data#:~:text=Big%20data
%20is%20a%20collection,variety%2C%20velocity%2C%20and%20veracity./>
Top 6 Big Data Challenges and Solutions to Overcome, 2020 [Online] available through:
<https://www.xenonstack.com/insights/big-data-challenges/>
7 Big Data Techniques That Create Business Value, 2022 [Online] available through:
<https://www.firmex.com/resources/blog/7-big-data-techniques-that-create-business-
value/>
Appendix 1: Poster
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