Report on Information System and Big Data Analysis Techniques

This report explores the crucial role of information systems in managing and analyzing big data, which is increasingly vital for businesses. It discusses the characteristics of big data, including value, velocity, variety, volume, veracity, visualization, and volatility, highlighting the importance of each. The report identifies challenges in big data analytics, such as the lack of understanding complex data, integrating data from multiple sources, and ensuring data security. Various techniques for analyzing big data, including machine learning, A/B testing, and statistical analysis, are examined. Finally, the report emphasizes how big data technology supports business growth by enabling better marketing strategies, improved data security, and more competitive business functions. The conclusion underscores the need for managers to adopt advanced technologies for effective data management.

Information system and
big data analysis

Contents
INTRODUCTION...........................................................................................................................1
Big data and its characteristics:....................................................................................................1
Challenges of big data analytics and techniques to analyse big data:..........................................2
How big data technology supports business:...............................................................................4
CONCLUSION................................................................................................................................4
REFERENCES................................................................................................................................5

INTRODUCTION
Big data analyses refers to those process under which the large amount of data are controlled an
managed by the organisation. The level of data are increasing day by day as the number of
companies and their customers in the market are increased. There are many organisations are
present in the market which has huge number of customers. These customers has creates huge
data which is required by the company to stored and managed in effective manner. The
importance of data are very high in the organisation because it is used by the company in their
marketing functions which is one of the most important and valuable aspect of the company.
The management of the data are very important for the success of the marketing framework of
the organisation. It ,is required for the company to manage their data so that it can protected from
the data loss and cyber attacks happened on the organisation because large number of customers
are based on this data. This report is based on the concept of big data under which different
characteristics of big data are stated in this report.
Big data and its characteristics:
There are different types of data are present which are classified in three sections which
is unstructured, unstructured and semi structured. All these types of data has separate importance
and valence in the organisation. Structured data refers to those data which are processed in
structured form and becomes able to stored in specific format. It is one of those data which can
be easily accessed and stored by the user and becomes effective for the usage of the data.
Unstructured data refers to those type of data which does not in the form of format and not
accessible for the user. There are different types of characteristics which helps to understand the
concept of big data in effective manner (Nica, Janoškova and Kovacova, 2020). The brief
discussion related to various characteristics of big data are given below:
 Value: It is one of the great characteristic of the data under which it does not matter the
amount of data and their speed of processing but it is important to have some value for
the user. It is very important for the processed data that they are reliable and useful in the
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organisation. It is very important for the management of the organisation that they
develop effective strategies to manage this types of data.
 Velocity: This characteristic of the data are related to the speed of processing and
generation of information. It is very important for the company that they have analyse the
velocity of the data so that it is justified it is useful or not for the company.
 Variety: It is not important for the data that it comes from the single source or same
nature. There are different types of sources of data are present which can be used to
extract them. There are large number of data are extracted from these sources which are
of unique nature (Rezaee, Dorestani and Aliabadi, 2018).
 Volume: This characteristic of the data are related to the amount of data which is
generated in the organisation along with specific period of time. It is possible for the
company that they have many sources of data which can creates huge amount of data an
it becomes important for the company to manage this data.
 Veracity: It is very important for the usage of the data that it becomes accurate and true.
The veracity of the data are important part of the accuracy of the company because it
helps them to waste their efforts in wrong information.
 Visualization: It refers to the presentation of data in effective and attractive form so that
it becomes easy to understand and usage in the organisation. There are different types of
new and advance methods are present in the market which can be used to make the data
effective such as maps, charts, graphs and many more.
 Volatility: Volatile is related to the changing rate of the data and their life span. The
nature of the data are dynamic and it becomes change at each stage of their process. It is
possible for the data that their nature changes from origin to the process.
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Challenges of big data analytics and techniques to analyse big data:
It is possible for the company that they have analysed the data so that it becomes useful to be
used in the organisation. There are different types of challenges are present which can be faced
by the company in their working and the brief discussion related to the same are given below:
 Lack of proper understanding of complex data: As the level of data are increasing day
by day in the market which makes the business more complex. There are some
companies are present in the market which does not have proper learning platforms in
their organisation so the management has weak understanding about the concept. It is ver
important for those organisation that they have developed these platforms ion their
organisation so that this problem can be minimised.
 Integration of data from different sources: One of the greatest problem which are
faced by the companies in big data analyses is the multiple sources of the data. There are
large number of sources of data are present in the organisation which increases the
volume of the data so it becomes negative for the organisation to manage this data
(Nakashima, Kon and Yamaguchi, 2018).
 Securing data: The security of the data is one of the biggest threat faced by the company
in data management. There are different types of cyber attacks are happened on the
servers of the company and theft the data for negative purposes. It is very important for
the management of the organisation that they develop effective security system in the
organisation which makes them possible for the organisation to secure their data.
There are various techniques that are used in analysis of big data and some of these
techniques are described as follows:
 Machine learning: There are different types of machinaries and technology are
developed in the market which can be used by the company to analyse and stores large
amount of data in their organisation. These machines has different types of features
which can be enjoyed by the organisation such as security, easily access and many more.
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 A/B testing: It is one of the amazing technique of data management which is used by the
company to differentiate the data on the basis of different factors on the basis of the
nature. The A/B testing technique helps the management of the organisation to increases
their sale.
 Statistics: It refers to those technique of the data analyses under which the data gathered
from different sources and represent them in statistical form so that it becomes important
for them to understand the concept of data effectively in the organisation.
How big data technology supports business:
The big data technologies has played important role in the growth and success of the
organisation. It helps the organisation to grab different types of opportunities from the market
which is very popular in the market. There are many technologies are present in the market
which makes the big data more understandable and reliable for the company. The management
of the organisation has used this data in their different types of business functions such as
production, marketing and many more which can make the process of the organisation more
competitive and productive in nature (Ru and Koo, 2021). The effective marketing framework of
the company enables the company to attract the customers towards them. The new and advanced
technology of data management has some attractive and useful tools of the security of the data in
the organisation. These technologies helps the management to make their data more secure and
productive for their organisation.
CONCLUSION
It is concluded fro this report that the management of the data is one of the important and
valuable aspect of the organisation which has required extensive focus from the management. It
is very important for the managers to use new and advanced technology for the management of
the data are concluded in this report. There are different types of characteristics which is very
important to differentiate are concluded in this report.
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REFERENCES
Books and Journals
Ru, H. and Koo, S.H., 2021. Big data analyses on key terms of wearable robots in social network
services. International Journal of Clothing Science and Technology.
Nakashima, K., Kon, J. and Yamaguchi, S., 2018, January. I/o performance improvement of
secure big data analyses with application support on ssd cache. In Proceedings of the
12th International Conference on Ubiquitous Information Management and
Communication (pp. 1-7).
Rezaee, Z., Dorestani, A. and Aliabadi, S., 2018. Application of time series analyses in big data:
practical, research, and education implications. Journal of Emerging Technologies in
Accounting, 15(1), pp.183-197.
Nica, E., Janoškova, K. and Kovacova, M., 2020. Smart connected sensors, industrial big data,
and real-time process monitoring in cyber-physical system-based
manufacturing. Journal of Self-Governance and Management Economics, 8(4), pp.29-
38.
Duft, G. and Durana, P., 2020. Artificial intelligence-based decision-making algorithms,
automated production systems, and big data-driven innovation in sustainable Industry
4.0. Economics, Management and Financial Markets, 15(4), pp.9-18.
Jelonek, D., Stępniak, C. and Ziora, L., 2018, April. The meaning of big data in the support of
managerial decisions in contemporary organizations: review of selected research.
In Future of Information and Communication Conference (pp. 361-368). Springer,
Cham.
Shrivastava, S. and Marshall-Colon, A., 2018. Big data in agriculture and their analyses.
In Encyclopedia of Food Security and Sustainability (pp. 233-237). Elsevier.
Colley, S. and Evans, J., 2018. Big Data Analyses of Roman Tableware: information standards,
digital technologies and research collaboration. Internet Archaeology, (50).
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