Big Data Analytics: Techniques, Challenges & Business Applications

This report provides an overview of big data, its characteristics, and the challenges associated with its analysis. It highlights the importance of big data in today's technological environment, emphasizing its role in various applications such as machine learning and business analytics. The report discusses key characteristics of big data, including value, velocity, volume, variety, and veracity, and explores the challenges of handling large volumes of data from multiple sources, the lack of analytical skills, fake data generation, and data security and privacy issues. Furthermore, it outlines several techniques currently used to analyze big data, such as machine learning, social network analysis, and data mining, providing examples of how these techniques can support business by identifying valuable customers, creating new experiences and products, and managing information effectively. The report concludes by emphasizing the crucial role of big data in business growth and the importance of understanding trends, demands, patterns, and perspectives through vast database analysis.

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Summary Paper

TABLE OF CONTENT
Introduction............................................................................................................................................3
What big data is and the characteristics of big data...........................................................................3
The challenges of big data analytics..................................................................................................4
The techniques that are currently available to analyse big data........................................................5
How Big Data technology could support business, an explanation with examples..........................6
Conclusion.............................................................................................................................................6
REFERENCES......................................................................................................................................7
Appendices............................................................................................................................................8

Introduction
In today's Technological Environment Big data plays a vital role. The world around is
changing very quickly, we all live a data driven age now. As we all know that it is essential to store
data and use it. Data is everywhere around us, we all require data and information in our daily lives.
We can have data from social media comments, profiles of customers, posts, like and dislikes and
purchase data on the e-commerce websites where people can visit for purchasing on daily basis. The
present study will be based on big data and its challenges. Furthermore, the report will highlight
some important techniques that are currently available to analyse big data which could support
business.
What big data is and the characteristics of big data
Big data is a combination of structured, unstructured and semi-structured data which has
been collected by companies that can be mined for information and utilized in the machine learning
projects and many types of analytics applications. As we all know that big data is basically a
collection of important data that is huge in volume (Hariri, Fredericks and Bowers, 2019). With the
change technological world, big data is growing exponentially with time. Basically, in technical
language, it is the use of advanced analytic methods in order to manage large size data sets which
includes batch data, structured data and so on. In the advanced technological world, search data is
basically utilized by the search engines in order to enhance search results. People search everything
on google thus this data can be captured by SEO. For big companies this important data and search
results plays a vital role. Companies nowadays are using this data in the form of customer data,
financial data, figure related to sales etc (Dash, Shakyawar, Sharma and Kaushik, 2019). with the
growing time, data collection is really essential for big firms. From this discussion, it has been noted
that large size of data is produced every second. Therefore, it is known as big data.

Characteristics include:
Value- This is one of the most essential characteristics of big data. As we all know that value
is important because in today's world data should be useful and reliable. Unnecessary data will not
be used by companies and can create poor quality performance (Fu, Liu and Srivastava, 2019). In
short, no matter how fast the data is produced, it should always be realistic and very much useful
otherwise it will lead to loss in revenue of firm. In this world where competition is very high data
scientists convert raw data into important information which can help to retrieve data set. If this
process is a success, then data is said to be valuable in nature.
Velocity- Basically, it is the speed at which the important data is generated which can he
helpful for the future progress. With the help of data processing, the company can meet the demands
and wants of the users. Hence, it is essential to have continuous data flow.
volume – It means the large amounts of data that has been gathered every second in the big
firms. This data is generated by the big companies with the help of different sources which includes
customer logs, comments done by clients on social media platform, videos and images and financial
transactions. In the earlier, time collecting and storing this important data was a big issues for
companies. But now with the time passes company is using distributed systems like Hadoop which
are utilized for collecting data in the company and organizing things (Saggi and Jain, 2018). This
system are used for managing data which has been collected from all these sources. As we all know
that the data size is essential for understanding its value. In addition to this, understanding concept
of volume is also very helpful in determining whether a data collection is big data or not.
Variety- This one is also essential characteristic of big data, it means there are different
sources of data which is available. In the past times, people used to have tools like spreadsheets. But
now with the change in technology data is present in videos, images, PDFs and text files too. Hence,
it is said that data variety is essential to be understood and must take into consideration for its
analysis and storage.
Veracity- This characteristic of big data is linked with the previous one. It is essential for
people to filter out the unnecessary data and information and utilize the important one for processing
which can create good value and better results.
The challenges of big data analytics
Handling large volume of data coming from multiple sources

Nowadays, due to large volume of data from multiple sources it creates issues for companies
to understand the bigger picture and analyse of data is challenge (Lv and Qiao, 2020). Sometimes
filtering data from multiple sources can be challenging too because it requires effective data
analysts.
Data integration
companies can have collected data from multiple sources, which sometimes makes it hard to
keep monitor the effectiveness of the integration process. A single mistake while managing data may
prove fatal to the business success of the overall procedure.
Lack of important analytical skills
Nowadays companies are hiring those people who is having analytical skills. In some cases
data analysts are expected to come withy all the important skills to handle the issues. Firms ignore
the need for training as they want to save cost this creates difficulty that their employers are facing
now. Lack of analytical skill in this world can create long list of challenges for both company and
staff.
fake data generation –
This is one of the biggest challenge related to big data, because with the increase in usage of
technology there is a increase in fake data generation. Some users on social media creates fake
account by filling wrong address, wrong identity which creates issues while collecting data.
data security and privacy issues
when working with massive data sets, it can be really challenging for companies to identify
the source of a data breach (Cui, Kara and Chan, 2020). Nowadays, it is essential to secure the
available data of users and customers and even employees at all costs. The technologies' creation
such as the cloud helps company and people to store important information on a single platform so
that it can be accessed anywhere and anytime when needed. It makes life much easier than ever
before but at the same time storing all this important information in one place has also given rise to
security and privacy issues. Thanks to technology and advanced tools which can be used to manage
data integration process and data flow in smoothly manner without any issues.
The techniques that are currently available to analyse big data
In order to create business value, big companies are using effective techniques to analyse big
data.
Machine learning

Basically it is a method and software that can learn from data. With the help of machine
learning the company is able to determine the best content for engaging customers (Wang and et.al.,
2020) It gives computers and devices the ability to learn without being explicitly programmed. With
the help of this technique one can find out difference between spam and non spam email messages.
social network analysis
Basically, it is a method that is used by telecommunication industry, then with the time
passes it has been used by many sociologists to study about interpersonal relationships. In today's
modern world, this technique it applied to analyse the relationships between people in many fields
and activities related to commercial. With the help of social network analysis, one can determine the
importance of a particular people within a group (What is big data analytics and why it is important,
2022). By using this technique one can also understand the social structure of customer base which
helps to make connections.
Data mining
it is one of the most important tool which has been used by the company to big data analysis.
With the help of data mining one can extract patterns from large data sets by combining techniques
from machine learning and statistics. For example- customer data is mined in order to find out which
segment of customers are most likely to react to a particular offer.
How Big Data technology could support business, an explanation with examples
 Big data technology helps organization to figure out the most valuable customers.
 With the help of big data technology, businesses can also create new experience and
products. For example- in the industry like tourism sector and hotel industries, with the help
of big data travel agencies and big hotels can know about the times when there are more
visitors and also find out about the demand for a certain spot. On the basis of that hotels can
make decision-making process successful with the context of operations.
 With the help of big data technology, big organizations can use analytics in order to manage
informations. (Examples) includes- Cassandra and Redis which can be used to make
computations quicker for better results in NoSQL.
Conclusion
To conclude, big data plays an important role as it help in the growth of business in the world
full of competition. From the study, it has been concluded that big data concept and its characteristic

helps enterprise and organization owner understand the trends, demands, patterns and perspective
through which vast database can be gathered and generated.

REFERENCES
Books and Journals
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.
Cui, Y., Kara, S. and Chan, K. C., 2020. Manufacturing big data ecosystem: A systematic literature
review. Robotics and computer-integrated Manufacturing. 62. p.101861.
Wang, J. and et.al., 2020. Big data service architecture: a survey. Journal of Internet
Technology. 21(2). pp.393-405.
Lv, Z. and Qiao, L., 2020. Analysis of healthcare big data. Future Generation Computer
Systems. 109. pp.103-110.
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.
Fu, W., Liu, S. and Srivastava, G., 2019. Optimization of big data scheduling in social
networks. Entropy. 21(9). p.902.
Dash, S., Shakyawar, S.K., Sharma, M. and Kaushik, S., 2019. Big data in healthcare: management, analysis
and future prospects. Journal of Big Data, 6(1), pp.1-25.
Zhu, L., Yu, F.R., Wang, Y., Ning, B. and Tang, T., 2018. Big data analytics in intelligent transportation
systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 20(1), pp.383-398.
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Appendices

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