Big Data Analytics: Characteristics, Challenges, Techniques and Business Support

This report discusses the characteristics, challenges, techniques and business support of Big Data Analytics. It explains what Big Data is and its six defining characteristics. It also covers the challenges faced in Big Data Analytics such as data integration, complexity, security, lack of knowledge and understanding. The report further explores the techniques currently available for analyzing Big Data such as regression, clustering, statistical description, R programming and more. Finally, it explains how Big Data technology can support businesses with examples such as better decision making, understanding customers, improving business operations, generating revenue and more.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying
Paper
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Contents
Introduction p
What big data is and the characteristics of big data p
The challenges of big data analytics p
The techniques that are currently available to analyse big data
p
How Big Data technology could support business, an
explanation with examples
p
Poster p
References p
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Introduction
Big data defines as the large volume of the data. In today's time the
term is very popular among all companies. It has began in the year
between 1960-70. till that time the use and need of the big data is not that
much, but from 2004 the need of big data has been started. Because in that
era there were many social apps has been introduces to the people. Big
data helps to store data in large size at the same place. There are many
technologies and frame work introduced for the development of the big data
(Wang and et.al., 2020). They contains set of large data. Now the
companies are able to store data in bulk with full safety. Big data helps
organizations mainly if they engaged in the IT sector. This is very helpful
because this is the time where companies must have stored all the data
related to their business or customers. In this report it has been discussed
of characteristic, technologies, challenges, importance of the big data.
What big data is and the characteristics of big data
The term is defined as the data which are large in the size. In simple words
the computer stores the data in a electronic signal sign. Few data is called
data and the data is in huge in size that is called big data. It is a modern
way of collecting and storing data. It has many characteristic that has been
discussed as.
Characteristic:- there are six features which defines the big data in brief
these are volume, variety, velocity, value, veracity, variability
(Ghasemaghaei, 2021).
 Volume:- Big data stores huge size data which created by many
social app platforms, from different organization activity, gaming,
government data. Volume defines the size of the data which a
system coverts from signals given in computer. Example of this is
cookie, cloud facility to stores the data for the future safety.
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 Variety:- This means the types of storage methods such as,
structured, unstructured and semi- structured. It is recorded in the
table form.
◦ Structured- in this all the files are stored in a organized way like
in documents, PDF
◦ unstructured- all the raw files, photos, videos, small fines, all
these are unorganized files which do not know how to convert
them in computer signal language.
◦ Semi structured- In semi structured data it includes, social
media apps, internet search and videos.
 Velocity:- It means process in which people willing to convert data
into final results for which they are looking after. Velocity means
the speed by which data has been record and give instant results.
This can be the reason of differentiation from companies
competitors. Speed plays an very important role in data
processing.
 Value:- The V defines as the accuracy and reliability of the data.
Value defines the correctness and able to connect with them for
whom you are looking in the data.
 Veracity:- It means the authenticity which the data is providing
about information. It is very important that the data should be
correct and this ensures about that.
Variability:- This big data V ensures the speed of changing the
information that the person want to be change.
The challenges of big data analytic
 Integration of data:- Data are very sensitive. It will be very crucial
when the data are leaked because data sometimes process and
transfer into different source. Data are hard to process in a effective
manner (El Alaoui, Gahi and Messoussi 2019).
 Complexity of data :- It is very hard to store data without errors.
Data are very complex in nature. In any organization a system is
used by their employees, mangers, staff members so the data are
very big in size and it will face complexity.
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 Security of data :- Many database platforms allow users to get all
the information at the same place foe better working condition it save
times. But along with this they also face some serious problems
related to security. The data must be secure so the information
cannot be leaked.
 Lake of proper knowledge:- Big data needs to be collect, analyze,
store, secure by the professional only. Every person cannot handle
these large data for this company only trust on highly professionals
(Attaran,Stark and Stotler 2018).
 Lack of understanding:- In the companies there are lack of proper
understanding of the huge data and the importance of the storage
for Handel massive situation in the future.
 Data growth problems:- The database companies who stores all
the data from different sources they face issues related storing of
very huge data some of them are unstructured manner like in videos,
photos, documents etc.
 Issue while selection of tool:- Companies want to select the best
tool for their company there are various tools available for the big
data it is difficult to select the right one tool.
The company want to take full credit of their money, time , energy for pick
up the best choice.
The techniques that are currently available to
analysis big data
Big data helps in converting the data into information. They transferring
theses information into an software where all the information are stored
at a safer place by which people can get excess to it whenever they want
(Al-Abassi and et.al., 2020) There are various technologies available to
analyses big data some of them are discussed below.
 Regression:- This technique plays an important role in analyzing big
data. It shows the every possible variable in particular data.
Regression method shows the link between dependent and
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independent variable. This is helpful in measuring the data.
Company can make more assumptions related to data.
 Data clustering:- This technique is useful for the statistical
professionals for the analyzing of biological data, image patterns,
machine language. Clustering divides the data according to the
structured or unstructured information.
 Similar matches:- It measures the similarity between two data
information by using some methods. Similar matching mainly
detect when similar data, pictures, documents are found. It is
usable when in data duplication, data saving, data errors (Jan and
et.al., 2019).
 Statistical description:- It is based on the features of the data, by
setting up the data signals. And by that signals system information
is covert by data.
 R programming:- This is the free trial software that is used by
many companies to store and analyses the data on the given
information.
 Link prediction:- It predicts the information that is already saved
that the person is trying to save. It is divided into two node based
and network based. Recently this technique has gain some
popularity because it is easier for the person when system
automatic predicts the existence of the node by using of network.
 Causal analyses:- mainly it is done by the use of regression
method. The change on the information is done by the casual
analyses.
How Big Data technology could support business,
an explanation with examples
 helps in better decision making:- Decision are taken by the
company are on facts not on prediction. The big data tools helps
them to rely on the information they have stored so that they can
take better business decisions (Choi, Wallace and Wang2018).If
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there is permission of excess to use the data it will be helpful in
smarter decision making ability within the company.
 Understanding the customers:- It is true that the more you able to
understand the customers the more chances you get to treat them in
the way they like . Big data helps company to know better their
customers and their wants. Very famous example here is Facebook,
Facebook understand its customers very well according to the
information they serve their customers by giving updates, notification
of the posts they liked (Dong and Yang, 2020)
 Serve best services:- By the use of big data the companies able to
know their loyal customers better. They stored their information
regarding their needs, wants and preference so by this they provide
them best quality product and services in order to satisfy customers.
 Improve business operations:- There are many tools available to
enhance the work ability in the business. Here many techniques are
used in order to fulfill the needs of the employees one of the
example is chat boot. This helps in ask the information from
employees. All the data have stored in a software which is used for
the increase efficiency in the business operations (Hofmannand
Rutschmann, 2018)
 Generating revenue:- Big data not only use in customers
satisfaction and smoothing operation but also to generate income
out of this.
 Innovative products:- Big data helps company to keep providing all
the new version, new technologies that will helps them in creating
new products for the customers.
 Customers retention:- Big data helps companies for providing the
information about what type of products customers are demanding. It
helps them in retention of the existing customers (Lai, Sunand and
Ren, 2018)
Q1 introduction to big data :-
The term is described as the the data which are huge in size. There
are three types in data such as structured data, semi-structured data and
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unstructured data. This term has been introduced in late 1960-70 era this
was introduced by John Mashey. When social media apps have launched
the need of storing data safely was the propriety for the companies. This is
the modern way of recording and processing the data. Big data helps
companies in adopting new technologies related to data storing.
Q2 What big data is and the characteristics of big data
The term consists of large and very complex data. The use
of big data is for record and processing of the data to get final
result. They stores the data in the form of electronic signals.
There are six characteristic of the big data such as volume,
variety, velocity, value, veracity and variability. The volume
features helps companies in capturing the data which is in huge
size, variety means different types of data – organized,
unorganized and semi- organized. Velocity tells the speed in the
data processing. Value means the authenticity of the data a
company can rely on. Veracity means accuracy while processing
the data into information.
Q3 The challenges of big data analytic
Though big data is very useful and important for any company but
apart from this it face many challenges also such as lack of knowledge,
lack of awareness, lack of understanding, security of data. In big data there
is always a risk of theft of data . So the data should be store through a very
secure software. Employees do not know how and where the data should
be stored. Some company do not aware about the power of big data.
Q4 The techniques that are currently available to analyze big data
There are various techniques, the companies used for the analyze
the big data. Such as hadoop ecosystem, Artificial Intelligence, NoSQL
Database, R Programming, regression, clustering, statistical analyses
theses are the some techniques by which company can evaluate the
performance of the big data. By using different techniques and methods the
organization able to find out the real analyses of big data by using they can
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able to find out the need of change , reducing errors. Through big data
company increases efficiency in the business operation and by this they
able to retain their customers.
Q5 How Big Data technology could support business, an explanation
with examples
Big data is very important in context to the company. By this the
company get to know more about their customers, their needs and wants.
When customers provide their data about their choices, the company
converts then into real information and hence know about their preferences.
By this they have better understanding with their loyal customers and help
them to retaining those customers. Big data transfers the processed data
to the company so that they can change their products according to the
customers satisfaction.
Poster
References
Wang and et.al., 2020. Big data service architecture: a survey. Journal of Internet
Technology, 21(2), pp.393-405.
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Ghasemaghaei, M., 2021. Understanding the impact of big data on firm performance:
The necessity of conceptually differentiating among big data
characteristics. International Journal of Information Management, 57,
p.102055.
El Alaoui, I., Gahi, Y. and Messoussi, R., 2019, April. Full consideration of Big Data
characteristics in sentiment analysis context. In 2019 IEEE 4th International
Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 126-
130). IEEE.
Attaran, M., Stark, J. and Stotler, D., 2018. Opportunities and challenges for big data
analytics in US higher education: A conceptual model for
implementation. Industry and Higher Education, 32(3), pp.169-182.
Al-Abassi and et.al., 2020. Industrial big data analytics: challenges and opportunities.
In Handbook of big data privacy (pp. 37-61). Springer, Cham.
Jan and et.al., 2019. Deep learning in big data analytics: a comparative
study. Computers & Electrical Engineering, 75, pp.275-287.
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.
Dong, J.Q. and Yang, C.H., 2020. Business value of big data analytics: A systems-
theoretic approach and empirical test. Information & Management, 57(1),
p.103124.
Hofmann, E. and Rutschmann, E., 2018. Big data analytics and demand forecasting in
supply chains: a conceptual analysis. The International Journal of Logistics
Management.
Lai, Y., Sun, H. and Ren, J., 2018. Understanding the determinants of big data analytics
(BDA) adoption in logistics and supply chain management: An empirical
investigation. The International Journal of Logistics Management.
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