Information Systems and Big Data Analysis: Challenges, Techniques, and Business Support

This project discusses the challenges, techniques, and business support of big data analysis in information systems. It explains the characteristics of big data and how it can be analyzed to improve business decisions. The poster and accompanying paper cover topics such as A/B testing, statistics, natural language processing, data mining, sentiment analysis, and association rule learning. The paper also explores how big data technology can support businesses in making better decisions, collecting better knowledge of the market, and developing and innovating new products.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
Submitted by:
Name:
ID:
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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
As the world is changing, almost everything is data driven where big data is the
collection of huge data which is structured or unstructured data collected by the
organizations used in machine learning, predictive modeling or other advanced
analytic applications. This project will talk about the types of data, principles,
challenges and techniques of big data analysis. It further explains the importance of
Big data in functions of business. Big data analytics helps the company to identify
new opportunities which can bring new customers, higher profits and efficient
operations.
What big data is and the characteristics of big data
Big data
Big data is a modern analytics trend which allows companies to make more data
driven decisions than even before. Big data is useful for all size and sectors of
companies. Big data is the collection of large data which can be both structured or
unstructured which is analyzed to improve the decisions and boost confidence on daily
basis to make strategic business moves. Big data is useful for the information which
can be processed using traditional tools or processes.
Characteristics of big data
Big data depicts the large collections of data which are expensive and complex . New
software development have made it possible to track the data. Characteristics of big data
are volume, variety and velocity which are explained below -
 Volume- Volume refers to the huge volume of data which is collected and
generated every now and then in the organization. The data is mainly
generated through various sources such as social media, customer logs,
financial transitions and videos. The main problem arises in the case of
storing and processing the large volume of data.
 Variety- The term variety refers to the various types of data, earlier it used to
be in the form of databases or spreadsheets but now the data is available in
form of photos, videos, text, files, PDFs or audio files.
 Velocity- Another most important feature is its velocity which means the
speed at which the data is created or generated. It is dependent on how fast
the data is going to be processed in order to meet and fulfill the demands of
customers or clients by analyzing and processing the data.
The challenges of big data analytics
Big data is the huge collection of data which can be structured or unstructured that is
collected from different customers, purchase data, ledgers, search history or employee
records. Many business leaders realize that they do not have access to the insights
which are required to do their job properly. There are various challenges which are faced
by big data analytics are explained below-
 Lack of skilled professionals- Companies are investing time and money in
the recruitment of skilled professionals. One of the biggest challenges is lack
of knowledge professionals as companies need skilled professionals to use
and implement the modern technologies and large data tools.
 Lack of understanding of massive data- Employees or workers might now
know about data, storage and its processing. Data professionals know about
the what is exactly happening while others can not. Seminars and various
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big data workshops can be help and arranged for all the workers for large
data projects.
 Collecting data from a spread of sources- Companies need to solve the
data integration problem by selecting the tools properly. As there are various
tools which are available such as ArcESB, IBM InforSphere, Talend Data
Integrtion or Xplenty. Data is collected from various sources such as e-mails,
ERP applications, customer logs, presentations or reports which are created
by employees. It can be a challenging task to merge all this data and keep it
organized.
The techniques that are currently available to analyse big
data
The whole world is driven and affected by the data and it is monitored every second
which can be analyzed by the reserved cart of shopping, google maps or Netflix habits.
There are many techniques to analyze data sets. The various techniques and ways
that are available to analyze the big data are as follows-
 A/B testing- A/B testing is the most effective and easy method to
ascertain the best content to convert visits mainly into sign-ups. The
experiences and learnings from A/B testing can be applied to additional
experiences. It also helps in increasing the sales volume which leads to
build the trust in brand and retain the existing customers.
 Statistics- This is a technique which is used to collect the data to keep it
organized and interpret the data within the experiments.
 NLP(Natural language processing)- It is the branch of computer science
as the automatic manipulation of natural language such as speech,
software and text.
 Data mining- It is one of the common tools to analyze the data form
different sources which are more efficient and accurate. It is the process of
finding patterns and correlations within the large sets to predict outcomes.
 Sentiment Analysis-It is an approach to natural language
processing(NLP) and it is powered by the smart language algorithms. It
also helps to get the real mood behind the messages to determine whether
the data is positive, negative or neutral.
 Association rule learning- These are the set of techniques which are
used for discovering the relationships that is association rules among the
large set of items. It is an essential part of data mining which is a procedure
of search for items or events. It is based on machine learning method for
finding the relationship between variables of large database.
How Big Data technology could support business, an
explanation with examples
Data plays a main role in understanding the target customer's preferences and
demographics. As the data can be captured with the help of camera, cellphones, or
other touch points. If analyzed properly, then these data could be very helpful to
support business so that the business perform better. Big data is one of the main
resources for driving smart changes where big data technology can support business
in different ways are explained below-
 Making better business decisions- On the basis of data or assumptions,
big data provides support to the business in order to make better decisions.
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The companies of all sizes requires a strategy for maintaining the big data
and in planning how to use, collect or protect it. With the proper data
analytics technology the decisions can be taken at faster pace. Business
are now able to make smart and proper decisions.
 Data is the asset of the company- Almost every business generate and
maintain data these days like a company or business has a social media
presence or a website it is required to maintain the data and protect it
properly.
 End goals- When the big data solutions are implemented successfully then
the organization can be properly aligned with the end goals for example
greater profit, good market share and a strong brand recognition.
 Collect better knowledge of market- Big data enables the company to
collect better market and customer intelligence. The companies will need to
be more creative and particular about creating the privacy policies for the
systems and security needed to protect their user data.
 Right data- Proper maintenance of data helps to achieve the desired goals of
the organization. Identifying, tracking and capturing the right data will be
main for the whole big data process. If the wrong data is used then it can
create an adverse effect on the daily operating of the company.
 Re-develop products- Big data helps to collect and use feedback to
understand how consumers understand about the company's products and
services. Social media allows to get the feedback of consumers on the basis
of demographic and geographical groups. Big data helps in testing the
variations of high end computer aided designs.
 Automation- Big data is also supports in discovering the hidden opportunities
which are not known for the company. Complex data can also be used to
develop and innovate new products or to enhance the existing one.
 In-depth insights- Proper arrangement of big data helps to get the in depth
details of the working of the company like customer preferences, choices or
knowledge about the company's product.
Poster
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References
Books and Journals
Arena, F. and Pau, G., 2020. An overview of big data analysis. Bulletin of Electrical
Engineering and Informatics, 9(4), pp.1646-1653.
Galetsi, P., Katsaliaki, K. and Kumar, S., 2020. Big data analytics in health sector: Theoretical
framework, techniques and prospects. International Journal of Information
Management, 50, pp.206-216.
Hou, R. and et. al., 2020. Unstructured big data analysis algorithm and simulation of Internet
of Things based on machine learning. Neural Computing and Applications, 32(10),
pp.5399-5407.
Lee, S. and Peng, T. Q., 2020. Big Data, Analysis of. The International Encyclopedia of Media
Psychology, pp.1-9.
Lv, Z. and Qiao, L., 2020. Analysis of healthcare big data. Future Generation Computer
Systems, 109, pp.103-110.
Nomura, A. T. G., de Abreu Almeida, M. and Pruinelli, L., 2021. Information model on pain
management: An analysis of big data. Journal of Nursing Scholarship, 53(3), pp.270-
277.
Reis, T., Bornschlegl, M. X. and Hemmje, M. L., 2021. Toward a Reference Model for Artificial
Intelligence Supporting Big Data Analysis. In Advances in Data Science and
Information Engineering (pp. 561-572). Springer, Cham.
Wang, J. and et. al., 2020. Big data service architecture: a survey. Journal of Internet
Technology, 21(2), pp.393-405.
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