Big Data Analysis Report: Techniques, Challenges, and Business Impact

This report provides a comprehensive overview of Big Data, encompassing its definition, characteristics, and various techniques used for analysis. It delves into the challenges faced in processing and interpreting large datasets, including issues related to data storage, skilled professionals, tool selection, data safety, and understanding. The report highlights key techniques such as Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA), explaining their roles in simplifying and improving data quality. Furthermore, it explores the significant role of Big Data technology in enhancing product quality, enabling better decision-making, optimizing resource allocation, and identifying potential customers for businesses. The report concludes by emphasizing the importance of Big Data in understanding consumer behavior and maximizing a firm's profitability.

BIG DATA

Table of Contents
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
Big data.......................................................................................................................................3
Characteristics of Big Data.........................................................................................................3
Challenges in the analysis of Big Data.......................................................................................5
Techniques of Big Data...............................................................................................................6
Role of Big Data Technology in the business.............................................................................7
CONCLUSION................................................................................................................................8
REFERENCES................................................................................................................................9
APPENDIX....................................................................................................................................10

INTRODUCTION
In the above report Big data and its techniques are discussed. Big data is a collection of
unstructured or semi-organised data that converted into structured form before using it. Big data
is useful techniques which provide important information regarding customer and help company
to provide maximum satisfaction to them (AbdelAziz and et.al., 2020). This report include
various challenges faced by a firm while implementing big data technique. The currently
available data techniques in the market and the role of big data in organization is discussed in
this organization. Big data help a business to improve its product and services according to the
needs and wants of the consumer.
MAIN BODY
Big data
In the modern era, technologies improvement and innovation are take place on big scale
while managing these technologies used by many firm to provide growth for their business.
These technologies resulting increase in the data mass form various sources like marketing,
government etc. Companies using big data techniques to enhance business operations, improving
consumer support, Making various marketing strategies and so on (Aldridge, 2019). It will
ultimately enhance the company's performance and profits. Firm use big data techniques to hold
a potential position in the competitive market and it help them to make faster and much advised
business decisions. Companies or organization uses big data for storage purpose which hold
verity of data with vast volume. In, previous time companies do not have enough spaces to store
data, they have to delete previous data to store new data which can remove important important
data also. Big data is a cost effective technique which is most secured from other techniques.
Characteristics of Big Data
Big data is a combination of organised, semi-organised, unorganized information that is
collected by organization which is used in machine learning projects, other analytic models or
applications. It is a common 6technique used by firm to understand consumer behaviour, wants
and needs of them. Big data explain by 6 major characteristic that is called as 6V's, they are
Velocity, variety, value, veracity, variability. Discussion on these characteristic are below:

 Velocity: Big data is continuously changing because of innovations in the sector of
technology. Velocity refers to the measurement of time taken to convert unstructured
data into structured form.
 Volume: It refers to the amount of data which is collected by a firm. It mainly focuses on
the size, quantity and capacity of data processing. A company manipulate and visualize
this data to achieve organizational goals (Baig, Shuib and Yadegaridehkordi, 2019).
 Variety: This V explain the vast variety of data which is being recorded and it is
recorded in unstructured or semi-structured form. This cover various type of data
because it gathered from various sources. This recorded data still need to be processed,
summarised and filtration according to the need of organization.
 Value: It refers to the quality of data which provide better results according to the firms
needs. It is used to filler most important data from the stored data which provide quality
information to the company about their products, consumers and competitors.
 Veracity: It refer to the process of data which is consider questionable, impure or
conflicted data and also give data about events where organization not sure how to deal
with them. It shows that the firm critically evaluate the data truth and authenticity.
 Variability: It measure the used of data to an extent and how fast it convert into results
and also explain the quantity and shape of data (Dubey, Kumar and Agrawal, 2021).
Variability refers to the consistency of data in the changing technological environment.

Challenges in the analysis of Big Data
While processing big data a firm facing many problems regarding their vast quantity. It is
hard to tailor for organizational use. Analysing and developing big data is needed new skills as
compare to previous data analytical technologies. Some of major problems are briefly discussed
below:
 Lack of skilled professionals: For running today's impelling, modern technologies the
business needs some skilled professionals. These can be data analysts or data scientists.
They are needed so that the big data present with the business can be used efficiently
and effectively by the business to make some sense out of it and gaining from the
enormous data available in the business (Ikegwu and et.al., 2022).
 Problem of data growth: The most important issue that has been increasing related to
big data is the problem of storing this amount of data sets. It is an issue for the
organisations to store these in the data centres or databases of the business as the
numbers are increasing rapidly. As these increase with such accelerated range, the
challenge to store and analyse the data and use it effectively for the business operations
is becoming a task for the business organisations.
 Confusion in tool choice: Due to the amount of data with the companies, sometimes it
becomes very difficult for the businesses to determine which tool should be used by the
business to assess and extract information from the lot of data available. This leads to
companies selecting irrelevant tools for the information extraction and usage of big data
and this poor selection of tools and strategies results in ineffective decision making and
extraction from the whole data.
 Data safety: Securing these data from the hackers and irrelevant users is very crucial for
the business. But with this size of data amount that exists with the businesses it becomes
difficult for the companies to keep a check for big data security. Also with the various

stages of storing ,analysing and extracting informations from the big data, organisations
ten to keep data safety as the last step that proves to be harmful for the business as this
can be a position where malicious hackers and wrongdoers can use this data for
unauthorised purposes resulting in various issues and problems for the company.
 Lack of understanding: Since the amount of data with the companies in big data is very
high, hence sometimes the entity fails to understand the meaning and implication of that
data. Employees of the organisation are not trained to handle this amount of data,
resulting in misinterpretation of the data with the company loosing the importance of
data that is important and sensitive for the business.
Techniques of Big Data
Various techniques are used to analysing and summarising big data in more efficiently
manner. These techniques are helpful in These techniques are discussed below:
 Principle Component Analysis (PCA): This methodology is used in the conversion of
inter-related variables into a bunch of Unrelated variables (Seng and Ang, 2018). This
convert large data set into a very small group that contain still quality of information.
Small data sets is easy to analyse and visualize and also reduce the time and efforts of the
company. It plays an important role to decrease the number of variables and preserving
more and more information.
 Linear Discriminant Analysis (LDA): This methodology is most popular or common
dimensionality reduction techniques used for eliminating issues in machine learning. It
convert information in low quantity and increase the quality of data.

Role of Big Data Technology in the business
 Enhance the product quality: Big data provides the business with various kinds of
information that can be used by the business for its benefit to gain more and more
customer base (Wang and Wang, 2020). This knowledge that exists with the
organisations has the potential to create strong bonds wit the existing customers and
attract new customers by producing and providing the products that are being demanded
in the markets.
 Better decision making: Big data provides businesses with the instruments that are
needed for making effective and smart decisions for the business because these decisions
are based on a set of extracted data and not on any assumptions. For this, the business
needs to give the access of the data to all the members of the organisation who need it to
make better and effective decisions.
 Optimising the resources to create income: Big data is not only used for improving the
products, services or to grasp better customer understanding, it can also be used to
optimise the other less researched areas and can use this data accordingly to generate
more revenue for the business by tapping on the scarce resources that can be utilised
with the help of this big data. This in today's times create ample opportunities for the

business to use and create exciting opportunities with the help of AI to benefit and
expand the business operations.
 Tap potential customers: Big data can play very critical role to tap and connect the
business with new customers as with the help of that data, the organisation can know
more about what is being demanded and what is needed for their target customer base
and can utilise this big data accordingly for their own benefit of understanding the
customers and connecting and providing them with the needed products and services
(Wang and Lu, 2020).
CONCLUSION
From the above report it can be concluded that Big data plays important role in analysing
consumer behaviour, attitude etc. This technique can fulfil the customer requirements and
maximise the profitability of a firm. Big data is a group of multiple data sets which contain
variety of data with huge volume. Their are many challenges faced by any company to analyse
this data and they need professional to perform this technique. Big data enhance the quality and
quantity of data without deleting past data and improve the performance of the company.

REFERENCES
Books and Journals
AbdelAziz, A.M and et.al., 2020. A parallel multi-objective swarm intelligence framework for
big data analysis. International Journal of Computer Applications in Technology. 63(3).
pp.200-212.
Aldridge, I., 2019. Big data in portfolio allocation: A new approach to successful portfolio
optimization. The Journal of Financial Data Science. 1(1). pp.45-63.
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.
Dubey, A.K., Kumar, A. and Agrawal, R., 2021. An efficient ACO-PSO-based framework for
data classification and preprocessing in big data. Evolutionary Intelligence. 14(2).
pp.909-922.
Ikegwu, A.C and et.al., 2022. Big data analytics for data-driven industry: a review of data
sources, tools, challenges, solutions, and research directions. Cluster Computing, pp.1-
45.
Seng, K.P. and Ang, L.M., 2018. A big data layered architecture and functional units for the
multimedia Internet of Things. IEEE Transactions on Multi-Scale Computing
Systems. 4(4). pp.500-512.
Wang, S. and Wang, H., 2020. Big data for small and medium-sized enterprises (SME): a
knowledge management model. Journal of knowledge management.
Wang, W. and Lu, C., 2020. Visualization analysis of big data research based on Citespace. Soft
Computing. 24(11). pp.8173-8186.

APPENDIX