Big Data Analytics: Challenges, Techniques, and Business Support

This article discusses the challenges faced by big data analytics, including managing data, talent gap, and securing data. It also covers the techniques used to analyze big data, such as association rule learning, classification tree analysis, and sentiment analysis. Additionally, it explains how big data can support businesses, including talking with customers, developing products, and creating new revenue streams. The article includes references to relevant books and journals.

Business Management
BMP4005
Information Systems and Big Data Analysis
Poster and Summary Paper
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Contents
1

Introduction 3
What big data is and the characteristics of big data 3-4
The challenges of big data analytics 4-5
The techniques that are currently available to analyses big data 5-6
How Big Data technology could support business, an explanation with
examples 6-7
References 8
Appendix 1: Poster 9
2

Introduction
Numerous large organization with wide-ranging business structure and panoramic
functionalities in their business. This also needs to maintain an ample of information and
evaluate their work, sales, employees, customers etc. is called big data. It's not an easy task to
maintain such a big data with the challenges. This is a complex process because there are
various types of big data on different grounds and characteristics of their own. To handle this
complex project many business companies and their analyst uses techniques to manage,
implement, gather and analyze this information. Assembling and executing big data is very
helpful for the companies as it influences and convey many ways in which companies can
modify their working pattern resulting in growth of company’s market share and profits. It is
broadly categorized in two parts: structured or unstructured. Structured data consists of the
data which is already managed by the organization in the form of databases or spreadsheet
whereas unstructured data is unformed and not preset format.
What big data is and the characteristics of big data?
Big data is a collection of large data which are not clarified through traditional methods
The data is collected from different sources. .(Müller,Fay, and Vom Brocke, 2018)
There are five V's of Big Data that explains the characteristics:
 volume: The name itself reflect the size of a data it means that data is in large
volume. It is important to know the size of an information to determine the value out
of the data (Penda, 2019). The companies manage and analyzes the ample of big data.
This data is generated from many source like social networking sites, business
process, machines and engaging of human beings.
 Velocity: it refers to the speed, the speed of generation of data. It examines how fast
the data is generated and meets it requirements and determine the real potentiality in
data. It deals with the speed at which the data flows from different sources. The
streaming of data is large and ceaseless. This considers the rate of change and the
linking of inward data.
 Value: It is an important feature of big data. A value is a dependable data that is
stored, refined and examine. It is just not an amount of data but it is a data which
added the value to the company in their profits. (Osman, 2019)
 Variety: It refers to the generation of data in various forms. In traditional method the
data was collected in spreadsheet or database format but now it is collected in PDF,
email, photos, videos and in many more formats. The big data can be structured,
unstructured and semi structured that are being collected from various sources.
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 Veracity: It means how much data is relate-able. Thus, a most of the part of data is
immaterial due to which the data has to be filtered and find an alternative way to
make the data essential and helpful in business developments.
The challenges of big data analytics
Following are the major challenges to big data analytics:
 Uncertainty in Managing the Landscape of Data: As big data is spreading
continuously, new technologies and companies are developing day by day. It is a
major challenge to the industries to discover which technology will work better for
them. So that there will less chances of potential risk and problems.
 Big data Talent Gap: Due to the complexity of big data there are only a few experts
in this emerging field. People who can understand this detailed and complex field are
very rare. There is very high demand for big data scientist as the data is rising rapidly.
As talented people are far-few and between, there is a talent gap in the industries
which is a big challenge for them(Mehta, and Pundit, 2018).
 Accessing data in to big data platform: Data is growing day-to-day. This indicates
that it is tough for organizations to manage such a large amount of data. So, it is
essential to make data availability convenient and simple. As companies have
boundless data amount on a daily basis. It may pressurize any data practitioner. So
easy data availability will be helpful for owners and brand managers.
 Requirement for synchronization across sources of data: As the sources of data
becoming more diverse, there arise a need to blend data in to an analytical platform. It
may lead to incorrect messages and insights or can create gapes if its synchronization
is ignored. So it's a challenge to big data analytics to synchronize data.
 Acquiring important insights through Big Data analytics: It is important for
companies to gain correct insights through big data analytics. Assessment of proper
department it is also important for this information. As big data analytics helps an
organization in controlling the use of their data and identifying emerging
opportunities. This leads to smarter enterprise moves, more prompt operations,
increasing profits and satisfied consumers(Ghani, Hamid, Hashem et.al, 2019).
 Security and privacy of data: The big the data, big will the potential risk of privacy
and security. The big data used for storage and analysis utilizes the data different
sources. This leads to a higher risk of exposure of data eventually and making it
assailable. Thus, it is a challenge to maintain privacy of data securely.
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The techniques that are currently available to analyses big data
The analytic of various organization use several methods and techniques to evaluate
the big data that has been present in their company like regression synthesis, Monte Carlo
simulation, factor analysis, cohort analysis etc. Some popular and common techniques used
to analyses data are mentioned below:
 Association rule learning: This method is used to identify the correlation between
set of variables in ample of databases. It helps to spot the product in better reach of
the customers to increase the sales. This system is considering to discover the
intruders and de-spiteful action, & to uncover the new relation, it analysis the
biological data.
 Classification tree analysis: Classification Tree Analysis is a variety of machine
learning. It is a kind of decision tree that leads to category decision & composed of
branches and leaves. This helps in decision making by contributing the different
categories. Statistical classification is a tool used to identify category that a new
measure dwell to. This mechanically allot documents to categories.
 Genetic algorithms: Genetic Algorithms is usually used in optimisation trouble
wherein we have to maximise or minimise a granted objective function value under a
given set of constraints. This is influenced by the means of process - through
mechanics such as inheritance, mutation and natural selection.
 Machine learning: This is used to determine the best content to occupy the
customers. It allows software to forecast the outcomes without being explicitly
programmed. It supports the company in predicting the trends and development of
new trends. It is an artificial intelligence, the work of human being can be done
through the machines.
 Regression analysis:The purpose of regression analysis is to identify how the
variables affect the dependent variable, in state to determine course and structure.
This is specially helpful for making predictions about upcoming trends. It works best
with ceaseless quantifiable data like weight, speed or age. This is necessary to know
that regressions can only be used to ascertain either there is a relationship between a
set of variables or not,as a result they don't reflect about any consequences. It is
unfeasible to write conclusions supported on this analysis alone.
 Sentiment analysis: The main goal of this analysis is to explicate and sort the
emotions impart with text data. From a business point of view, this helps you to
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determine how your consumer feels about several feature of your brand, commodity,
or service. This helps the researchers to identify the sentiments of speaker and writer
with the regards of a content or title. In a nutshell, it helps to synthesis the sentiments
of people.
 Social network analysis: It is a tool the understand the social structure of a customer
base. This technique was first used by the telecom industry then native by social
scientist to study the social abstraction. It is used to assess the relation between people
on many grounds and for commercial activities. In this the Nodes symbolise
individuals within a system, while ties symbolize the relation between the individuals
How Big Data technology could support business, an explanation with examples.
 Talking with consumers: In today's world, customers are clever and understand their
precedency. Before purchasing anything, they look around and explore everything.
Even they consult with business through different channels of social media. Big data
permit a business to chart such customer in a far-reaching way. This let businesses to
pursue in one-on-one conversation, in real time with customers. In hard competitive
times, you need to treat customers how they want(Galetsi, Katsaliaki, and
Kumar,2020). An example is about a customer entering a bank. When he enters the
organization, the employee can use big data to check his profile. The clerk can learn
about the preferences and desires of the customer. This will help the clerk to advice
applicable product or service to the customer.
 Develop Products again: Big data is one of the best way to cod and use response. It
will help the businesses to understand how customer perceive their products or
services. Thus, a company can make necessary changes accordingly and re develop
the particular product or service.
Example: A company asks for feedback after selling or delivering the product with
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?
 Perform risk analysis: Big data analytic allows business organizations to scan social
media feeds, newspaper reports and analyze. It will help the company in knowing the
present scenario of market tends so that, it can keep up with speed permanently on the
latest trends and improvement in the industry)( Lv,2019).
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 Safety of Data: Big data tools allows a company in mapping whole data base across
the organization. This helps them to analyze all kinds of internal threats. This data
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.
 Creating new revenue streams: Big data helps a company in providing insights from
customers and analyzing markets. This information will helps the company to produce
desired product and thus enable businesses to make more revenue.
Example: Google uses the data on current search and historical terms to recommend
search idea to users earlier they finish typing. This will help the users to select their
search by selecting suggestion.
Conclusion
From the above analysis it is concluded that how big data analytics faces challenges
such as managing data, talent gap, making easy availability of data, synchronize data
sources, getting important sights and to secure the data and how it is helpful for
business organizations through various ways such as: in dealing with customers,
make changes in products according to their demands, analyzing the risks, making the
storage of data safe and creating revenues and what are the different techniques used
in analyzing the data.
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References
Books and Journals
Baig, M.I., Shuib, L. and Yadegaridehkordi, E., 2019. Big data adoption: State of the art and
research challenges. Information Processing & Management, 56(6), p.102095.
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.
Ghani, N.A., Hamid, S., Hashem, I.A.T. and Ahmed, E., 2019. Social media big data
analytics: A survey. Computers in Human Behavior, 101, pp.417-428
Grover, V., Chiang, R.H., Liang, T.P. and Zhang, D., 2018. Creating strategic business value
from big data analytics: A research framework. Journal of management information
systems, 35(2), pp.388-423
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.
Lv, S., 2019, December. Design of the automobile marketing system based on the big data.
In International conference on big data analytics for cyber-physical-systems (pp.
1713-1719). Springer, Singapore.
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.
Müller, O., Fay, M. and Vom Brocke, J., 2018. The effect of big data and analytics on firm
performance: An econometric analysis considering industry characteristics. Journal of
Management Information Systems, 35(2), pp.488-509.
Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S.A., Montesano, N., Tariq, M.I., De-la-Hoz-
Franco, E. and De-La-Hoz-Valdiris, E., 2022. Trends and future perspective
challenges in big data. In Advances in intelligent data analysis and applications (pp.
309-325). Springer, Singapore.
Osman, A.M.S., 2019. A novel big data analytics framework for smart cities. Future
Generation Computer Systems, 91, pp.620-633.
Pengda, Z., 2019. Characteristics and rational utilization of geological big data. Earth Science
Frontiers, 26(4), p.1.
Sestino, A., Prete, M.I., Piper, L. and Guido, G., 2020. Internet of Things and Big Data as
enablers for business digitalization strategies. Technovation, 98, p.102173.
Wang, B. and Wang, Y., 2021. Big data in safety management: an overview. Safety
science, 143, p.105414.
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Appendix 1: Poster
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