Comprehensive Report on Big Data Analytics: Challenges and Techniques

This report provides a comprehensive overview of big data, defining its characteristics, exploring the challenges associated with its analytics, and detailing the techniques used to manage and analyze it. It discusses the different types of big data, including structured, unstructured, and semi-structured data, and highlights the five Vs of big data: volume, value, variety, velocity, and veracity. The report further examines the challenges such as lack of knowledge, data growth issues, integration complexities, and security concerns, alongside techniques like predictive analytics, knowledge discovery tools, stream analytics, and in-memory data fabric. Finally, it illustrates how big data supports businesses through better decision-making, customer understanding, smarter products, improved operations, and income generation, providing examples from companies like Facebook, Disney, and Royal Bank of Scotland.

INDIVIDUAL POSTER AND
ACCOMPANYING REPORT

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
MAIN BODY...................................................................................................................................3
Definition of big data and its characteristics-..............................................................................3
Challenges and techniques of big data analytics-........................................................................4
Big data support to businesses-....................................................................................................5
CONCLUSION................................................................................................................................7
REFERENCES................................................................................................................................8

INTRODUCTION
Big data is larger as well as more complex data from the new sources of data. These are very
difficult to manage by data processing software as they are much voluminous. These large
volumes of data can be utilized to address the problems of the organization which were not able
to handle previously. This report will describe the big data as well as its characteristics. Along
with this, it will also discuss the challenges of big data analytics and the techniques which are
presently available for the analysis of big data as well. Later on it will also discuss how can this
big data analysis can support the organizations.
MAIN BODY
Definition of big data and its characteristics-
Data is the characters, symbols, quantities on which the computers operate. These can be stored
and transferred from one computer or device from another. Big data is data which is very large in
volume and is growing very rapidly(Farboodi, M., et.al., 2019). The size of these data is very
large that it is very difficult to manage by the traditional tools of data management and even they
cannot store and process it effectively. Big data is set of technology which has been created to
analyse, store as well as management of these voluminous data. In present era, it is used in
various areas like medicine, environmental protection, businesses, agriculture etc. Big data
analytics helps businesses in various aspects such as helps businesses in better understanding of
consumers, identification of operational issues, managing the supply chains and detecting the
fraudulent activities happens in the businesses. It is very important for the businesses as it helps
organization in assessing their data and identify the new opportunity. That turns into the smarter
business, earns higher profits and customers are satisfied.
Different types of big data-
These big data are classified into three categories-
1. Structured data- These data are highly organized and managed by set parameters and
these data are very easy for working.
2. Unstructured Data- It is usually all the unorganized data and it takes time and efforts to
organize these data.

3. Semi-structured data- It draws a line between structured and unstructured type of data.
These are data which does not follow the data model but it has some structure (Rossi, R.
and Hirama, K., 2022).
Characteristics of Big Data-
There are five Vs of big data:
1. Volume- This feature of the big data includes the amount or size of big data that the
businesses manages and analysis.
2. Value- It is the most important V of the big data from the business point of view. Value
is the term which describes the usefulness of data which has been gathered. To be useful
and valuable the big data needs to be converted into the information or insights. Value of
big data is very significant as it states that how much the Worthy the data is having
positive impact on the businesses.
3. Variety- There are variety of types of data such as unstructured data, semi-structured
data and raw data(Ranjan, J., 2019).
4. Velocity- It includes the speed at which businesses store, manage and receive the data.
5. Veracity- It states the accuracy of the data and its information.
Challenges and techniques of big data analytics-
There are various challenges which are faced using the big data are-
1. Lack of knowledge- Modern technologies and managing tools for large data companies
require highly skilled employees. For these companies hire data scientists, data analysts
and data engineers to work for these big data. The biggest challenge faced by the
companies there is lack of knowledge for working with these big data this is because
there are many tools manufactured to handle the data but there is lack of knowledge in
professionals to handle these data.
2. Data Growth issues- One of the most difficult challenges faced is professionals does not
have much quantity of knowledge to combat with the increasing databases of the
companies. As most of the information in data is unstructured which comes from
documents, audio, texts, videos etc. Most of the Companies uses modern techniques for
handling these big data such as compression, tie ring, de-duplication etc. Companies
often uses data tiers like public cloud, private cloud etc.

3. Integration of data- Data is integrated from various resources to convert it into valuable
information. Sources can be like from e-mails, customer logs, financial reports etc. To
combine and integrate all the data for organizing reports can be a very difficult task for
the professionals. Integration of data is very critical part for analysis, business
intelligence as well as for reporting. So for meeting up this challenge this tool will be
very effective.
4. Securing data- Securing these massive data is very difficult the professionals having
high skills can only work with these big data. The companies are much engaged in
storing, analysing as well as understanding the data sets that they leave securing data at
last stages. But this not the sensible move as not securing data can lead to a very problem
for the organization.
Modern techniques of big data analytics-
1. Predictive Analytics- One of the most frequently used tools in businesses is predictive
analytics. This tool is used to eliminate the risks of decision-making in the businesses.
This tools hardware and software solutions can be used to evaluate and deployment of
predictive scenarios for processing the big data (Rabhi, L., et.al., 2019). By evaluating
this, it enables companies to get prepared for the coming problems and can be solved by
understanding them.
2. Knowledge discovery tools- This tools helps the businesses to organize the big data
which are stored at various sources. These sources can be such as APIs, DBMS and
related platforms. These tools allow the businesses to isolate and can use the information
for the company's advantage.
3. Stream Analytics- The data stored of the organizations can be stored on the various
sources. This software is very useful for filtering, analysis and aggregation of big data.
4. In-memory Data fabric- This technological tool helps in the distribution of data to
different sources like Dynamic RAM, Solid state storage drives. This enables in low
latency of the access as well as low processing of big data at the connected nodes.
Big data support to businesses-
The big data can help the businesses in many ways such as:
1. Making better business decisions- It enables the business to take smarter decisions
which related to data. Every company must have access to data for improvement of

decision-making. IT departments are not only responsible for the handling these data,
every business user be able to explore and integrate the data when needed. This company
access is usually known as democratization. For example, as a single sales steam of the
organization cannot understand and evaluate that why the sales are dropped or reduced so
everyone in that business is responsible for giving the answer to this problem. By going
through the data, they identified that because of some calculation this problem occurred.
2. Understanding the customers- It is very important for the organizations to understand
their customers for their satisfaction. Big data enables the organization to understand the
customers and through which they can serve them. For example, Facebook uses knows
about the life of the customers and by using this big data it helps them to serve the
customers. Disney also taking advantage of big data for the growth of the organization by
understanding the behaviour of the customers when they visit theme parks and from this
they can offer more to them.
3. Providing smarter products- By evaluating the data the organizations can provide
better services or products to the customers. Having big data with the organizations helps
in providing them with better services to the customers for their satisfaction. For
example, Royal Bank of Scotland also uses big data for delivering better services to its
customers as they know lot about their customers (Marinakis, V., et.al. 2020).
4. Improvement in operations of businesses- Many of the other business sectors are using
automation technology and becoming more efficient (Choi Wallace and Wang, 2018).
This increased use of automation is underpinned from big data HR software company
People-doc launched a Robotic Process Automation Platform, which runs in the
companies and also listens to the processes which can be automated.
5. Generation of income- Big data is not only limited to improving decisions,
understanding the customers but it can also help in increasing the revenue or adding more
income for the organizations. For example, Amex is taking advantage of the
organizations stored data to maintain a strong relationship between the customers and the
businesses. Like this company has added new technology to its merchant services like
online trend analysis as well as benchmarking tools which are used to compare their
working with their competitors in the market.

CONCLUSION
This assessment report has discussed the types of big data and its challenges in the organization.
Along with this, it has also discussed the characteristics of big data and techniques currently
available to deal with this. Further, it enables to understand about the support of big data to the
businesses.

REFERENCES
Books and journals
Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data analytics in operations management.
Production and Operations Management. 27(10). pp.1868-1883.
De Mauro, A., et.al. 2018. Human resources for Big Data professions: A systematic
classification of job roles and required skill sets. Information Processing &
Management.54(5). pp.807-817.
Farboodi, M., et.al., 2019, May. Big data and firm dynamics. In AEA papers and proceedings
(Vol. 109, pp. 38-42).
Marinakis, V., et.al. 2020. From big data to smart energy services: An application for intelligent
energy management. Future Generation Computer Systems.110. pp.572-586.
Rabhi, L., et.al., 2019. Big data approach and its applications in various fields. Procedia
Computer Science.155. pp.599-605.
Ranjan, J., 2019. The 10 Vs of Big Data framework in the Context of 5 Industry Verticals.
Productivity.59(4).
Rossi, R. and Hirama, K., 2022. Characterizing big data management. arXiv preprint
arXiv:2201.05929.
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