Big Data: Characteristics, Challenges, Techniques and Business Support

This report explains the characteristics, challenges, and techniques of big data analysis. It also discusses how big data technologies help support business objectives and functions. The report covers topics such as volume, velocity, variety, and veracity of big data, techniques like machine learning, social network analysis, and more. It also highlights how big data helps businesses communicate with customers, re-develop products, carry out risk analysis, ensure data safety, and create new revenue streams.

Big Data

Table of Contents
INTRODUCTION...........................................................................................................................3
Big data............................................................................................................................................3
Characteristics of Big Data.....................................................................................................3
Challenges of big data analytics.............................................................................................4
Techniques available to analyse big data...............................................................................4
How big data technologies helps to support a business enterprise.........................................5
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7
Appendix: Poster..............................................................................................................................7

INTRODUCTION
This report is regarding to big data in which different types of data, challenges and
techniques of big data analysis is explained. Basically, big data is the data which characterises
huge variety, increasing volumes with more velocity. Big data are more complex data sets and
specially data from new data sources. Documents, emails, customer databases, medical records
and many more comes under big data. Velocity, volume, variety and veracity are main
characteristics of big data. There are some techniques such as: machine learning, social network
analysis and others which are used to analyse the data. This report also includes how big data is
helpful in supporting business objectives and functions.
Big data
Big data are the large, detailed and more diversified sets of information which grows at
increasing rates ever. It can be characterise as structured or unstructured. Structured consists of
already managed information which is frequently numeric in nature. Unstructured is unorganized
set of information including data collected from social media, which helps organizations collect
information on customer needs (Hancockand Khoshgoftaar, 2020). Big data is usually stored in
the databases of computer and it it analysed with the help of a software which is specially
designed to handle large amount of data sets which are more complex.
Characteristics of Big Data
 Volume: Big data is large in volume. It is considered that we create 2.3 trillion gigabytes
of data every day and this volume will only keep increasing day by day.
 Velocity: It is the speed at which data is generated and processed. Today, data is
generated in the real time. This is not because of speed of net only but also because of
presence of big data itself.
 Variety: Big data is available in a wide variety such as: data from watched you tube
videos, records of patients in healthcare, posts we share on social media such as:
Facebook, Instagram and others, chat backups from various social media, etc.
 Veracity: Big data is from original data source. It is accurate, consistent, quality worth
and truth worthiness.

Challenges of big data analytics
 Data Integration: Data is collected from various sources, which makes it hard to collect
the effectiveness of integration process. Many problems are there such as: improper
insights, how data is collected, verified, stored and used.
 Data Complexity: Data is getting more complicated on a daily basis. As data flow in
various forms from various sources such as: salesperson, operations, consumer and
others, its complexity get increasing on and on.
 Data Security: More the data, more will be the risk of its security. Security of data is
needed at all costs. Some of the technologies such as cloud helps to store data on a single
platform so it is easy to access data anytime and anywhere. But it may be a reason of risk
for the security of data as the huge amount of data is at a single platform (Wang and
Wang, 2021).
 Data Mobility: Data being available at everywhere, it need to be moved so that it can
collected then analysed. But as the size of data is very large, its movement become tough.
 Data Value: As the usefulness of data is increasing over longer period of time, its value
is increasing. A data value is a content that fills a space record.
Techniques available to analyse big data
 Association rule learning: It is the method for discovering amazing cor-relations in
between variables in big data-bases. It’s being used to help in placing the products near to
each other intended to enhance sales. It discovers information about the number of
visitors to the web sites and identify who is buying milk, butter, etc.
 Statistical classification: It is a method of approaching that categories under which there
is any new observation. It is also used in assigning documents in to categories
automatically and developing students profile who are taking online classes (Kolisetty
and Rajput, 2020).
 Genetic algorithms: These are used to schedule doctors for emergency rooms in
hospitals, generate a artificially creative contents, practices required to develop fuel
efficient cars, etc.
 Machine Learning: It includes the software that can learn from data. It is helpful in
differentiation of spam and non-spam email messages, determining the best content for
engaging the expected customers.

 Social network analysis: It is used to analyse the relation between individuals from
various fields and commercial activities. It also finds out the importance of a particular
individual within a group and the minimum number of the ties which are directly required
to connect two individuals.
How big data technologies helps to support a business enterprise
 Communicating with consumers: Customers are much smarter and understands their
priorities in the present world. They look around and explore everything, before they
make a purchase. They eventually communicate to business organization through
different social media channels. Big data helps an organization to identify such customer
in a far-reaching manner. This enables the enterprises to connect with these customers in
a real-time, one-on-one conversation. In tough competitive periods, a company need to
treat customers how they want. It increases a chance of impressing customer and
promoting them to make a purchase. An example related to this is about a client who is
entering a bank. When he or she enters the bank, the clerk can check his or her profile
with the help of big data. The clerk can get to know about the desires and preferences of
that customer. This will help him to advice the customer for relevant product or service.
 Re-Develop Products: Big data is one of the best way to collect and use feedback. It will
help the businesses to understand how customer distinguish their products or services.
Thus, a company can make necessary changes accordingly and develop the particular
product or service again. Example: A company asks for feedback after selling or
delivering the product and asks some questions such as: “Do you like the product quality?
Did the product was delivered at time? How much will you rate the quality of product out
of five? and Will you like to purchase more products from us? This will allow the
company to collect the feedback and help them in making the required changes (Castillo-
Zúñiga Luna-Rosas, Rodríguez-Martínez and et. al., 2020).
 Carrying risk analysis: Big data analytics allows business organizations to analyse and
scan social media feeds and newspaper reports. It will help the company in knowing the
present scenario of market tends so that, it can permanently keep up with speed on the
latest trends and developments in the industry. It will also allow the company to know
about any factor which may be a risk for its data or the organization.

 Data Safety: Big data tools allows a company to hatch the entire data base across the
company. This helps them to analyse all kinds of internal threats. This information will
help the company to keep the important information safe. It is important to protect the
sensitive information in an appropriate manner and store accordingly.
 Create new revenue streams: Big data helps a company in providing insights from
analysing market and customers. This information will help the company to produce
desired product and thus enable businesses to make more revenue. Example: Google uses
data on historical and current search terms to recommend search suggestions to users
before they finish typing. This will help the users to select their search by selecting
suggestion (Shekhawat Sharmaand Koli 2019).
CONCLUSION
From the above report it can be concluded that big data is diversifying day by day and it
is becoming a challenging task to analyse, store, secure and valuing such a huge amount of data.
How the available techniques such as: association rule learning, classification tree analysis,
genetic algorithms and others for the analysis of big data analyses data from multiple sources.
Big data can be characterised as: volume, velocity, variety and veracity. Then there is the
explanation about how big data techniques such as: communicating customers, making safety of
the storage of data and others will help to support a business enterprise.

REFERENCES
Books and Journals
Hancock, J.T. and Khoshgoftaar, T.M., 2020. CatBoost for big data: an interdisciplinary
review. Journal of big data, 7(1), pp.1-45.
Wang, B. and Wang, Y., 2021. Big data in safety management: an overview. Safety science, 143,
p.105414.
Kolisetty, V.V. and Rajput, D.S., 2020. A review on the significance of machine learning for
data analysis in big data. Jordanian Journal of Computers and Information
Technology (JJCIT), 6(01), pp.155-171.
Castillo-Zúñiga, I., Luna-Rosas, F.J., Rodríguez-Martínez, L.C., Muñoz-Arteaga, J., López-
Veyna, J.I. and Rodríguez-Díaz, M.A., 2020. Internet data analysis methodology for
cyberterrorism vocabulary detection, combining techniques of big data analytics,
NLP and semantic web. International Journal on Semantic Web and Information
Systems (IJSWIS), 16(1), pp.69-86.
Shekhawat, H., Sharma, S. and Koli, R., 2019, February. Privacy-preserving techniques for big
data analysis in cloud. In 2019 Second International Conference on Advanced
Computational and Communication Paradigms (ICACCP) (pp. 1-6). IEEE.
Appendix: Poster