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

This report discusses the characteristics of big data, challenges in big data analytics, techniques to analyze big data, and how big data technology can support businesses. It also includes examples of how big data technology can be used in different industries. The report is relevant to the BSc (Hons) Business Management course, specifically the BMP4005 Information Systems and Big Data Analysis module.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Summary Paper
1

Contents
Introduction
What big data is and the characteristics of big data
The challenges of big data analytics
The techniques that are currently available to analyse big data
How Big Data technology could support business, an explanation
with examples
References
Appendix 1: Poster

Introduction
There are various kinds of data which are stored by organisations for their future use.
Such data and information are managed in structure wise, semi structure wise as well
as in unstructured way (Hancock and Khoshgoftaar, 2020). There are huge data which
is managed by a company. These data is related to the purchase history of customers,
inventory history of company, personal data of employees and many others. The
following report covers characteristics of big data, challenges of big data analytics,
techniques that are currently available to analyse big data and the explanation of how
big data technology could support business.
What big data is and the characteristics of big data
There are main six characteristics of big data which are mentioned below-
Volume- It means the size and amount of data and information which is required to
manage by companies for effective functioning of their organisation.
Value- It is considered as one of the most important characteristic of big data because it
will define the value or importance of data which is managed by companies for their
effective management of operations (Hasan, Popp and Oláh, 2020). Companies did not
save each kind of data, they only focused to manage those business data which has
importance in organisational growth mainly which leads to maintain effective
relationship with customers.
Variety- It consist of different and large variety of data which is managed by a company.
Usually there are three varieties of data such as structured, semi-structured and
unstructured data (What are the 5 V's of Big Data?, 2022).
Velocity- It means the speed at which company collect, store and use the data for their
organisational benefit. It is essential for a company to gain high speed to manage their
data effectively because quick management of data will help them to focus on making
quick organisational decisions.
Veracity- The reliability and truthfulness of data is considered as veracity. It is essential
for a company to collect and store actual data rather than any hypothetical data. This
will help executives to make decisions on actual business factors. It will also determines
executive-level confidence.

Variability- It consist of the changing nature of data that companies seek to capture,
manage and analyse.
The challenges of big data analytics
Lack of knowledge professionals- It is essential for a individual who manage big data
of a company to have some specific kinds of skills and talents (Hariri, Fredericks and
Bowers, 2019). Data scientists, data engineers and data analysts are few of the
professionals which must be hired by a company. Normal employees of an organisation
are unable to hire for managing big data sets. Hence, its a challenge for a company to
attract specific skills for this professionals in organisation and pay them high salaries
are costly for the companies.
Lack of proper understanding of massive data- due to insufficient understanding of
data and its management by employees they feed wrong data at wrong place which
further mismanage whole organisational functioning. Data professionals will collect and
manage data but other employees of organisation also take participation in data
management. Due to lack of understanding of importance of data management
employees show their irresponsibility at workplace.
Data growth issues- One of the biggest challenge of managing big data is storing
these huge set of data properly. Set of data grow very rapidly which means every new
day even every new hour a lot of new data is added in previous data sets which make it
complicated for professions to store all data appropriately. The biggest issues with
companies is notices that for collecting and managing new data they underestimate the
importance of managing old data. Hence, their old data get misplaced due to which they
have to suffer for declining their declining in productivity and profitability (Top 6 Big Data
Challenges and Solutions to Overcome, 2020).
Confusion while big data tool selection- there are various kinds of tools and
techniques which are available for a company to manage their big data appropriately.
Hence, companies get confuse that which tool or technique will provide them maximum
benefit in managing their huge data sets. It is essential for a company to analyse which
kind of data they are using and what are the main characteristics of data which they are
using. Then they are required to use those techniques only which best suits to manage

data of the company. For this, the company is suggested to take the help of experts and
business analytics.
Integration of data from a spread of sources- A company receive data from various
sources like social media channels, websites, financial reports and many others. Hence,
its a challenging task for a company to prepare reports on these big data (Mehta and
Pandit, 2018).
Securing data- It one of the biggest issues of managing big data where a company is
required to secure their data collection and management activities from external threats
like hackers. Managing big data sets also include to make the data confidential and safe
from hackers.
The techniques that are currently available to analyse big
data
Regression analysis- Regressions analysis is used to identify the relationship between
various set of variables of data. In simple words, it means the interrelationship between
dependent variable and independent variable (Saggi and Jain, 2018). The main purpose
of regression analysis is to identify how other variables are linked with dependent
variables and impact this variable.
Machine learning- It is one of the well known technique of managing big data where
computers and various kinds of software are used to manage big data of a company. It
work with algorithms to produce assumptions based on data.
Statistics- Such kind of techniques are used in surveys and experiments to collect,
manage and interpret data which researchers have collected in their researches (The 7
Most Useful Data Analysis Methods and Techniques, 2022).
Sentiment analysis- This is the techniques of managing big data where researchers
determine the sentiments of writer or speaker regarding a topic. There are various kinds
of usage for this technique like it improve services at hotel by analysing comments of
customers and working upon them, to identify actual needs and wants of customers and
many others.
Social network analysis- Telecommunication industry is the first who use this
technique and then it is quickly adopted by sociologists (Galetsi, Katsaliaki and Kumar,

2020). This technique is currently applied to analyse the relationships between people
in many fields and commercial activities. This techniques are used to find the particular
individual's influence within a group.
How Big Data technology could support business, an
explanation with examples
It is essential for a business to use modern and updated technology to manage their big
data. This will help the companies to support business and further help them to grow
well with rapid speed. For example, if a supermarket chain like Tesco or Morrison uses
good technology of data management then it will help them to manage their data
effectively so that they will make decisions for the betterment of company. For example,
Tesco get to know about shortage of inventory through their data management so that
they will make decisions to quickly order products from their warehouses. On the other
hand, big data technology will also help these supermarkets as well as other companies
to ciollec6t the information about the purchase history of their customers to identify
which products are more purchased by customers and which are not (Tiwari, Wee and
Daryanto, 2018).. This will further help the companies to manage their customers by
offering those products which are at the top to their priority list to purchase products. Big
data technology also used to figure out the most valuable customers for a company.
Another example is related to tourism industry where the hotels and restaurants can
identify the time where they can see huge crowd and high demand for a certain tourism
spot. This will further help to make decisions by the hotels and restaurants for making
big profits.

CONCLUSION
From the above information it is concluded it is essential for companies to manage their
big data appropriately. There are six main characteristics of big data such as volume,
velocity, variety, value and few others. There are also few of the challenges to manage
big data such as lack of specific professionals, lack of understanding, security issues
and many others. Regression analytics, machine learning, statistics and many others
are some of the effective techniques of managing big data.

References
Galetsi, P., Katsaliaki, K. and Kumar, S., 2020. Big data analytics in health sector:
Theoretical framework, techniques and prospects. International Journal of Information
Management, 50, pp.206-216.
Hancock, J.T. and Khoshgoftaar, T.M., 2020. CatBoost for big data: an interdisciplinary
review. Journal of big data, 7(1), pp.1-45.
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.
Hasan, M.M., Popp, J. and Oláh, J., 2020. Current landscape and influence of big data
on finance. Journal of Big Data, 7(1), pp.1-17.
Mehta, N. and Pandit, A., 2018. Concurrence of big data analytics and healthcare: A
systematic review. International journal of medical informatics, 114, pp.57-65.
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.
Tiwari, S., Wee, H.M. and Daryanto, Y., 2018. Big data analytics in supply chain
management between 2010 and 2016: Insights to industries. Computers & Industrial
Engineering, 115, pp.319-330.
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./>
The 7 Most Useful Data Analysis Methods and Techniques, 2022 [Online] Available through:
<https://careerfoundry.com/en/blog/data-analytics/data-analysis-techniques/>
Top 6 Big Data Challenges and Solutions to Overcome, 2020 [Online] Available through:
<https://www.xenonstack.com/insights/big-data-challenges/>

Appendix 1: Poster