Big Data Analysis and its Features: Challenges, Techniques, and Benefits for Organizations

This report discusses the features, challenges, and techniques of Big Data Analysis. It covers the importance of data storage, data analysis, and data security. It also explores the various techniques available for analyzing massive data and how Big Data technology can support organizations. The report concludes with the benefits of Big Data Analysis for organizations.

Information System
and Big Data Analysis

INTRODUCTION
Big data analysis is a large volume which to not easy to handle. It is combination which
structured and unstructured that flocks the company on daily basis. Bid data is used to interpret
the vision that is working for the accomplishment of good decisions in the further actions of the
company. It is also difficult in nature, in the coming time the basic idea of big data has been
reached out to a point in order to approach and stock large mass of information for the analytics.
In the following task, an explanation on big data with its features is being discussed. There are so
many challenges that are include in the the big data analytics that are also essential to be
understood. Furthermore, the main methods which are there to interpret and analyse big data is
also determined. Lastly it covers the point the points which explains how big data technology
could assist organisations(Sreedevi And et. al., 2022).
TASK
Big Data and features.
Big data is defined as an aggregate of data acquired by businesses in order to fine-tune it
for useful information and use in predictive sculpture, machine acquisition forecasts, and other
modern analytics applications. Data might be structured, semi-structured, or unstructured. It has
a lot of mass and has been performing well for a long time. Because of the volume and
complexity, none of the traditional data management technologies can handle it or store it in a
timely manner. It entails a large volume of data, social media analytics, data extraction
capabilities, and real-time data. The action of studying a large volume of data is known as big
data analytics. There is a large amount of mixed digital data. It's all about data size and enormous
data sets,' which sounded like a computer memory unit and a terabyte. This design is all about
Huge Data, and when big data is evaluated, the data is proclaimed as Big Data analytics. Big data
examples include the New York Stock Exchange (NYSE), smartphone apps, and social media
sites like Facebook(Srivastava and Maurya, 2022).
Characteristics of Big Data
 Volume: As the name implies, the volume of big data is continually increasing. When
determining the value of data, the magnitude of the data is critical. Furthermore, whether
the data is independent or not is determined by the amount of data. One of the hallmarks
of large data is its volume(Abanumay and Mezghani, 2022)

 Variety: It simply refers to the structure, semi-structuredness, or unstructuredness of
data. Data is now available via email, Google Sheets, and other platforms, resulting in a
greater diversity than in previous years. Unstructured data comes in a wide range of
formats, each with its own set of challenges, such as analysing it, storing it, and so on.
 Velocity: Simply said, velocity refers to the rate at which data is generated in an
organisation. An significant aspect with regard to data is how quickly information is
processed in order to meet the demands of consumers, as this demonstrates the data's
potential.
 Variability: This simply refers to the data's inconsistency, which can wreak havoc on
the process of successfully and efficiently managing the data(Başak, Kılınç and Ünal,
2022).
Challenges of Big data analytics
There are some of the challenges of big data analysis that have all the appropriate ways of
dealing with the large volume data and it involves the function of holding the data, examining
the big volume of information on some kind of data. In context to the big data, there are some of
the challenges that are important to be considered and they are as follows:
 Lack of knowledge professionals: In the big and international companies there are so
many of the data in the company that is being used in the company working and to
maintain the data, the company need to have some of the data analysing experts who are
just best in the management and interpretation of the data. And some of the experts
include data scientists, engineers who will be the people who will use some of their skills
and manager that big data of the organisation.
 Lack of proper knowledge of massive data: When the company is having so much o
the data about the transactions and dealings of the company and this is because of the
inefficiency of the employees of the company, they are not able to identify the proper
processing of the data, where the data is stored and major importance of the sources of
the company. When the workers of the organisation don't know the importance of
knowledge storage then they will not be capable to maintain the confidential data(Shah,
2022).
 Integrating data from a spread of sources: There are the information of the data which
is collected from various sources like social media, applications, financial reports, e-

mails, presentation and gathering all that data in a presentable form is a hard task for the
employees, also the data collection is very important for the company to have its financial
reports, business position and for solving the issue the company can buy some of the
suitable tools Talend data integration, centerprise data integrator.
 Securing data : This is the process which is very important that the company needs to
keep safe the confidential data of the company that is something to kept with so much of
the security as it is something which cannot be mislead and the employees need to make
sure that they have to be with the perfect skills of securing the data of the company(Chen,
2022).
Techniques that are available to analyse massive data.
The following are some techniques to analyse massive data:
 A/B testing: This method is useful for evaluating what adjustments would help to
improve the default variable by comparing a control group to various test groups. Big
data matches the model, which entails testing broad spread numbers and objectives,
which can only be accomplished when groups have high size numbers. A/B testing is
useful for determining which of two variables is more useful and would perform better in
a market(Manogaran, Thota and Lopez, 2022).
 Machine learning: Because human analysts cannot foresee data, it is critical for
businesses to have a strong understanding of technologies. Machine learning is useful for
data analysis and generating assumptions based on gathered data. Machine learning is
useful for executing operations on larger and more diverse data sets in nature that are
difficult for a human to do. Machine learning enables activities to be completed in
seconds and without error.
 Statistics: With the use of surveys and tests, this tool can collect, sort, and anticipate
outcomes. Over time, the technologies for managing and analysing remote and expanding
data evolve. Aside from techniques and approaches, any type of knowledge is critical(Li,
Liang and Cao, 2022).
 Data combination and fusion: It is thought that there are alternatives that must be
investigated and analysed from a variety of sources. If the data is collected from a single
source, the information is more concentrated and contains more precise understanding.
With the help of data integration and fusion, there are various approaches and strategies

that can be used to analyse huge data. With the help of one channel, it is possible to
discover errors and provide more trustworthy, consistent, and relevant information.
How Big Data technology could support organisation, explain with examples.
Because big data analysis aids in the study and evaluation of data on a bigger scale,
businesses must recognise consumer behaviour patterns in order to meet their needs according to
their preferences, which big data analytics facilitate. Big data analytics could assist businesses in
the following ways:
 Increasing profit margin : Big data technology aids in the collection of large amounts
of data with high quality. It is a type of data that aids in increasing corporate profitability
and generating revenue for growth and expansion. It will also assist in attracting more
customers and clients to the company, forming a continuous chain in the long run and
extending the business life cycle(Li, 2022).
 Improving business decision : It assists businesses in making better business decisions
by sorting important quantitative data into qualitative form. It aids in determining the
areas that need to be addressed in order to boost product sales and demand. Assessment
and correct evaluation of decisions to be taken and executed can help enhance business
decisions. Improving business decisions will aid in the overall work and performance of
the company in an environment that will allow for improved oversight and execution of
operations. It will reduce expenses even further, reduce risks, and ensure stability and
long-term viability in a competitive context. Improving decision-making can help
businesses become more successful and efficient. It aids in the improvement of
innovation, which will aid in standing out in the market and better coping with
competition(Ma and et. al., 2022).

CONCLUSION
In this respective report it can be concluded that big data analyses is the job which is kind
of difficult for the employees and it is also important to make sure that there is proper storage of
the data of the company, there are some of the challenges that are there when the employee is
doing big data analyses and those challenges are important to be discussed in the study of big
data analyses. Also there are some of the techniques that are available for the collection of the
massive data and the how the Big Data technology could support organization.

REFERNCES:
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Poster