Big Data: Analysis Techniques, Challenges, and Business Applications

This report provides a comprehensive overview of big data analysis, highlighting its characteristics, challenges, and techniques. It discusses the exponential growth of data and the limitations of traditional data management tools in handling it. The report details key characteristics of big data, including volume, variety, velocity, value, and veracity. It also addresses challenges such as lack of understanding, data growth issues, and tool selection confusion, along with techniques like A/B testing, data fusion, and data integration. Furthermore, the report explores how big data technology supports business by improving decision-making, understanding consumers, enhancing operations, generating income, and delivering smarter products and services, with examples from companies like Walmart, Disney, and American Express. The conclusion emphasizes the crucial role of big data in business organizations for effective data management and improved efficiency.

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Contents
Introduction................................................................................................................................3
Characteristics of big data......................................................................................................3
The challenges and techniques of big data analytics.............................................................4
How Big Data technology could support business................................................................5
References..................................................................................................................................7
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Introduction
Big data can be defined as a collection of data that grows exponentially day by day
over time, despite its very large number. This data is so large and complex that traditional
data management tools cannot store or process it properly. With the help of this power, we
can improve performance while effectively identifying all unrealized opportunities that exist
in the industry (Haoxiang and Smys, 2021). The entire report is based on bid data analysis.
This report described the importance of big data and its properties. This report also describes
the challenges of big data analysis and the techniques currently available for big data
analysis. Finally, this report focuses on big data technologies that help business
organizations with good explanations and examples.
Characteristics of big data
The term big data can be described as organized or chaotic data based on valuable
collected data. Data can be collected from clients, servers, or transactions such as sales,
purchases, and employee history. Information gathered from a variety of sources not only
helps company management improve its structure, but it also helps them make effective
decisions. Examples of bid data include the New York Stock Exchange and Facebook, a
social media site. There are various types of characteristics related to the shape of big data,
some of which are described below:
 Volume: - Basically, this is the size that is considered to be the most prominent
feature of the dataset. Data was stored in big data systems ranging from petabytes to
exabytes (Grant, 2021). This large amount of data is used in advanced processing
techniques. Instagram and Twitter are examples of huge amounts of data. The person
spends a lot of time posting photos, comments, likes posts, playing games, and so on.
With the help of this data, analysis and insights can be performed with great potential.
 Variety: - this characteristic of big data states that, big data includes varieties of data
in various format and this also entails that in which manner the data is organized and
ready to use. In comparison to the traditional forms of data like phone numbers and
addresses, in the recent times data is present in the form of photos, videos, and audios
and many more.
 Velocity: - This characteristic is related to the rate at which data is accumulated and
also affects whether the data is large or periodic. Note that almost all data is
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evaluated in real time and it is very important that the system handles the pace and
amount of data generated (Yoo, Park and Chung, 2021).
 Value: - This feature is considered another important factor to consider. Not only is
it important to maintain the amount of data processed, but in order to generate
insights, the data needs to be reliable, valuable, processed, stored and analyzed.
 Veracity: - This characteristic of big data is related to both the reliability and quality
of the collected data. Big data is indisputable if the data is accurate and unreliable.
Data is updated in real time, so it is very important to verify the authenticity of the
data and balance it at all levels of collection.
The challenges and techniques of big data analytics
For organizations, supply data not only helps manage risk, but also plays an important
role in improving decision-making performance (Shin and Hwang, 2022). At the same time,
this not only brings accountability within the company, but also improves its financial
position and helps both employees and management to predict performance. The challenges
of big data analysis are:-
 Lack of proper understanding of big data: - Organizations cannot take advantage
of big data due to poor understanding. Employees may not understand what the data
is and how it is processed, stored and important. The management don't have a clear
picture in their mind. To overcome this challenge, business owners need to hold big
data seminars and workshops for those who work with data.
 Data growth issues: - Proper storage of all of these vast amounts of data is
considered one of the most important challenges associated with big data (Li and
Zhang, 2022). Data is stored in data centers and fast-growing corporate databases. As
the data grows day by day, it becomes more difficult to process. To address this
challenge, organizations must choose compression, tiering, and deduplication.
 Confusion while selecting the tool: - Most companies are confused when choosing
tools for big data analysis and storage. There are many kinds of questions in the
minds of company employees, and I can't find the answer. This leads to poor decision
making, poor technology choices, wasted time, money and effort. To address this
challenge, companies must either accept suggestions from consultants or hire
experienced professionals.
Techniques currently available to analyst big data
Big data technology plays a very important role for organizations as it helps them
effectively analyze, store, and understand data. With the help of these techniques, the
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decision can be made correctly and the risk can be reduced. There are different types of
techniques available for big data analysis forms, some of which are described below.
 A/B test: - This big data approach helps enterprise organizations focus on comparing
different types of test groups (Pathak, Krishnaswamy and Sharma, 2021). The
controlled dataset extends the provided target variables to identify appropriate
processing or changes. With the help of this technique, company management can
perform all tasks very accurately.
 Data fusion and data integration :- The emphasis was on combining a set of
techniques that can be used to effectively analyze data from different sources. This
technique provides successful insights into accurate and effective data collected from
individuals.
How Big Data technology could support business
For all types of business leaders, it is very important to have valuable data and
insights to understand the customer base and identify their preferences. The impact of big
data on supporting business organizations is as follows:
 Develop better organizational decision: - Big data helps enterprise organizations
make effective decisions based on real data rather than assumptions (Punia and et. Al.,
2021). This is called data democratization because it helps users explore the data. For
example, Walmart's data access was granted to employees in a highly controlled
manner through Walmart's DataCafe.
 Understanding the consumer: - Big data analytics help the management of business
organisations in effectively understand their customers' needs and requirements and
provide the services they desire. Disney, for example, uses big data to better
understand visitor behaviour. The bracelet will be distributed and will serve as a
badge or entry key to the amusement park.
 Enhance the business operations: - All types of business organizations are
automated on a daily basis, solely due to the use of big data within the business
organization. For example, chatbots are used to maintain interaction with company
employees.
 Generate income: - Big data analytics not only help improve the performance of
business organizations and manage decision-making processes, but also help increase
business sales and revenue (Sun and Huo, 2021). For example, American Express
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uses big data analytics to track company transactions and customers while building
strong customer ties.
 Delivering smarter products and services: - With the help of this data, it helps to
effectively identify customer needs and design products intelligently and
automatically. For example, the Royal Bank of Scotland uses it to provide its
customers with excellent service that saves time and money.
CONCLUSION
From the above report, it has been concluded that big data is playing an important role in the
business organizations as it help the management in effectively managing the entire data,
which can be used in bringing the effectiveness and efficiency in the business organization.
There are several features of big data has been also discussed. While using big data, the
business organizations will use face several challenges and these challenges can be removed
through developing several strategies. Along with this, some of big data techniques are also
discussed which are found as crucial in any business organization.
Poster
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References
Books and Journal
Grant, E., 2021. Big data-driven innovation, deep learning-assisted smart process planning,
and product decision-making information systems in sustainable Industry
4.0. Economics, Management, and Financial Markets, 16(1), pp.9-19.
Haoxiang, W. and Smys, S., 2021. Big Data Analysis and Perturbation using Data Mining
Algorithm. Journal of Soft Computing Paradigm (JSCP), 3(01), pp.19-28.
Li, Y. and Zhang, J., 2022. Classroom teaching of tourism management using multimedia big
data analysis. Journal of Intelligent Information Systems, pp.1-16.
Pathak, S., Krishnaswamy, V. and Sharma, M., 2021. Big data analytics capabilities: a novel
integrated fitness framework based on a tool-based content analysis. Enterprise
Information Systems, pp.1-35.
Punia, S.K and et. al., 2021. Performance analysis of machine learning algorithms for big
data classification: Ml and ai-based algorithms for big data analysis. International
Journal of E-Health and Medical Communications (IJEHMC), 12(4), pp.60-75.
Shin, E. and Hwang, H.S., 2022. Exploring the Key Factors that Lead to Intentions to Use AI
Fashion Curation Services through Big Data Analysis. KSII Transactions on Internet
and Information Systems (TIIS), 16(2), pp.676-691.
Sun, Z. and Huo, Y., 2021. The spectrum of big data analytics. Journal of Computer
Information Systems, 61(2), pp.154-162.
Yoo, H., Park, R.C. and Chung, K., 2021. IoT-based health big-data process technologies: A
survey. KSII Transactions on Internet and Information Systems (TIIS), 15(3),
pp.974-992.
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