Information Analysis Systems and Big Data - BSc Business Management

This paper discusses big data analysis, its characteristics, challenges, techniques, and how it can support businesses. It also includes a poster summarizing the key points. This is relevant to the BSc Business Management course (BMP4005 Information Analysis Systems and Big Data).

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
Big data analysis is the complex process of examination of the big data for
uncovering the information which is hidden and present in the form of uncover
information. The purpose of this analysis is to find the hidden patterns, correlations,
market trends and customers preferences which informs the business decisions.
What big data is and the characteristics of big data
Big data is the collection of the data in a huge volume and is considered to be the
key factor in the growth and exponential form of time. This is the data which is
considered to be able to present in the form of non-traditional manner. It is not able
to process the efficiency of the business is considered to be the factor which
influences. This is the data which is of a huge size and is considered to be the
statistical tool for achieving the best results in the organization (Ghani and et.al.,
2019). This is the tool which is considered to contain data from social media
platforms such as Facebook, twitter, LinkedIn and other media platforms. The
structure of this data is also the form in which it can be divided. There is big data
which can be structure as well as unstructured depending on the kind of information
which is stored an accessible and processed for being formed and fixed. Following
are the characteristics of the Big Data which influences the following,
 Volume : The volume of the big data is considered to be related to the size of
the organization which can be enormous. The role-played for the
determination of the value out of the data is considered to be particular and
can be considered to be very much dependent on the consideration of the
volume of the data which is essential for the information.
 Variety : The data can be of heterogeneous sources and can have affects on
both the nature of the structure and quality. It can be present in spreadsheets
and data solutions which can impact the growth of the organization. The data
can also be of images, emails, photos and video that impact the monitoring of
the information which is relevant to the different solutions provided.
 Velocity : The velocity of the data impacts the speed of the generation of data
for being processed towards meeting the demands of the real potentials. It is
helpful for meeting the demands of the data which has some connection. It
helps the business in referring to the speed of the generation of data and
impacts the flow of data in the management process.
 Variability : Depending on the type of data it is very variable and can be
inconsistent toward the management of process which is not able to handle
the management of data effectively.
The challenges of big data analytics
The challenges in the big data analytic are,
Lack of knowledge professionals : It has been known that the for running the modern
technologies it is very important for the organization to include the data scientists, data
analysts. Data engineers for the tools to make sense for the giant data sets to make the
company face the lack of massive data professionals.
Lack of proper understanding of big data : The companies can be considered to fail
when the big data is stored in the processing, information and sourcing the data of the
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professionals in order to understand knowledge storage for the keeping the backup
sensitive data.
Data growth issues : When there is a big data presented it becomes a challenge for the
analytic to store the huge data. Not being able to store the data properly can impact the
information of the individual that can be exponentially challenging handling. The
unstructured documents can impact the video, audio and text files which affects the other
sources.
Confusion while big data tool selection : The focus of the business needs to be on the
selection of the simple tool for the giant data analysis storage. For the selection of the big
data tool can help the analysis to be the answer towards seeking the decision-making.
Integrating data from spread sources :The sources which provide the data can be
from social media, ERP customer logs and financial reports, emails, presentation and
reports for creation of the data analysis presentation,
The techniques that are currently available to analyse big
data
The techniques which are used for the analysation of the big data are,
Associated rule learning : This is the method of discovering the interesting correlations
between variables in the large databases. It is considered to be very useful for the major
supermarket chains for discovering the interesting relations between the products, using
data from supermarkets point of sales system.
Classification tree analysis : The statistical classification of the method of identifying
categories can be observed by the organization in the training set of the correcting the
identification of the historical data in other words.
Genetic algorithms : This is the algorithms which inspires the ways in which the
evaluation works for the mechanisms for inheritance mutation and natural selection of the
mechanism which is used for the evolution of the solutions for the problems that require
optimization.
Machine Learning : This is the method in which the software is used for learning from
data for providing the computers the ability to learn without being explicit programmed.
The focus of this is on making predictions for knowing the properties which help in
learning from sets of training data (Tiwari, Wee and Daryanto, 2018).
Regression analysis : This is the technique in which the analysis involves the
manipulation of the same independent variable for seeing how to influence a dependent
variable for describing the value of dependent variable changes the independent
variable. Working in the continuous qualitative data the weight speed and age of the data
is affected in the organization.
Sentiment Analysis : The analysation of the researchers can be considered to be the
determination of the sentiments with the speakers for writing the respect to the topic. It is
also known for improving the services at the hotel chain. It helps in the customization of
incentives and services which is useful for addressing customers.
Social Network Analysis : This analysation is the technique for the first used
telecommunication industry and then quickly adopts the sociological factors which is
useful for the interpersonal relationships. The application of this in the relationship can be
considered to be the representation of the network which ties the relationship between
individuals and understands the different population through data.
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How Big Data technology could support business, an
explanation with examples
Advantage against business :
Big data technology can support the business in different ways for leading the companies
for outperforming competition. It contains new entrants and establishes competition for
the use of data driven strategies in a more complete and innovative form. The use of big
data can be seen in every business sector. Use of such data is most important for the
analysation of the competition.
Dialogue with consumers :
Consumers in the current market have become very smart and understand their priorities
before making any purchase. Communication between business and consumer though
social media has become very effective for the organization and is helpful for the
engagement of real-time one-on-one conversation with the consumers. Bank is a very
effective example for this support, as customers interaction with the back establishes
with the clerk who checks profile in real time for customer's preferences and desires.
This allows the business in advising the relevant products and services to the customers.
Re-Develop Products :
Big data technology is a very effective way of collecting and using feedback. This helps
in understanding the ways in which the customers provide services and products. It is
considered to be the re-development of the products which are used for analyzation of
the unstructured social media text (Zhu and et.al., 2018). This is disintegration of the
feedback for various geographical locations and demographics. McDonald's is a great
example which ditched plastic straws for the consideration of the huge customer
feedback against the use of plastic straws in UK.
Data safety :
This is a product which allows the business in the management of the entire data
landscape across the company. Big data technology allows the business to analyze all
the different kind of data related threats which can result into sensitive information safe
which is appropriate in the manner and stored according to the regulatory requirements.
Risk analysis :
The big data is a way in which the businesses are able to analyses all the different types
of risks which are present in the company. The economic and social factors play
important role for the determination of the role which accomplishes the predictive analytic
for allowing the scan of social media feeds and newspaper reports.
Poster
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INFORMATION SYSTEMS AND BIG DATA
ANALYSIS
INTRODUCTION
Big data analysis is the complex
process of examination of the big
data for uncovering the information
which is hidden and present in the
form of uncover information. The
purpose of this analysis is to find
the hidden patterns, correlations,
market trends and customers
preferences which informs the
business decisions.
BIG DATA
Big data is the collection of the data in a huge volume
and is considered to be the key factor in the growth and
exponential form of time. This is the data which is
considered to be able to present in the form of non-
traditional manner. It is not able to process the
efficiency of the business is considered to be the factor
which influences. This is the data which is of a huge
size and is considered to be the statistical tool for
achieving the best results in the organization.
CHARACTERISTICS OF BIG DATA
Volume: The volume of the big data is considered to be related to the
size of the organization which can be enormous.
Variety: The data can be of heterogeneous sources and can have
effects on both the nature of the structure and quality.
Velocity: The velocity of the data impacts the speed of the generation of
data for being processed towards meeting the demands of the real
potentials.
Variability: Depending on the type of data it is very variable and can be
inconsistent toward the management of process which is not able to
handle the management of data effectively.
THE CHALLENGES OF BIG DATA
ANALYTICS
Lack of knowledge professionals: It has been known that the
for running the modern technologies it is very important for the
organization to include the data scientists, data analysts.
Lack of proper understanding of big data: The companies
can be considered to fail when the big data is stored in the
processing, information and sourcing the data.
Data growth issues: When there is a big data presented it
becomes a challenge for the analytic to store the huge data.
Confusion while big data tool selection: The focus of the
business needs to be on the selection of the simple tool for the
giant data analysis storage.
Integrating data from spread sources: The sources which
provide the data can be from social media, ERP customer logs
and financial reports, emails, presentation and reports for
creation of the data analysis presentation,
THE TECHNIQUES THAT ARE CURRENTLY
AVAILABLE TO ANALYSES BIG DATA
Associated rule learning: This is the method of discovering the interesting
correlations between variables in the large databases.
Classification tree analysis: The statistical classification of the method of
identifying categories can be observed by the organization in the training set of the
correcting the identification of the historical data in other words.
Genetic algorithms: This is the algorithms which inspires the ways in which the
evaluation works for the mechanisms for inheritance mutation and natural
selection.
Machine Learning: This is the method in which the software is used for learning
from data for providing the computers the ability to learn without being explicit
programmed.
Regression analysis: This is the technique in which the analysis involves the
manipulation of the same independent variable for seeing how to influence a
dependent variable for describing the value of dependent variable.
Sentiment Analysis: The analyzation of the researchers can be considered to be
the determination of the sentiments with the speakers for writing the respect to the
topic. It is
Social Network Analysis: This analyzation is the technique for the first used
telecommunication industry and then quickly adopts the sociological factors which
is useful for the interpersonal relationships.
Big Data technology could support
business
Advantage against business:
Big data technology can support the business in different ways for
leading the companies for outperforming competition.
Dialogue with consumers:
Consumers in the current market have become very smart and
understand their priorities before making any purchase.
Re-Develop Products:
Big data technology is a very effective way of collecting and using
feedback.
Data safety:
This is a product which allows the business in the management of
the entire data landscape across the company.
Risk analysis:
The big data is a way in which the businesses are able to analyses
all the different types of risks which are present in the company.
The
CONCLUSION
Big data is a factor which is very important for any organization in order
to influence growth into their operations. This different techniques, tolls
and types of big data has been discussed.
REFERENCES
Ghani, N.A., Hamid, S., Hashem, I.A.T. and Ahmed, E., 2019. Social
media big data analytics: A survey. Computers in Human
Behavior. 101. pp.417-428.
Tiwari, S., Wee, H.M. and Daryanto, Y., 2018. Big data analytics in
supply chain management between 2010 and 2016: Insights to
industries. Computers & Industrial Engineering. 115. pp.319-330
Zhu, L., Yu, F.R., Wang, Y., Ning, B. and Tang, T., 2018. Big data
analytics in intelligent transportation systems: A survey. IEEE
Transactions on Intelligent Transportation Systems. 20(1). pp.383-
398.

References
Ghani, N.A., and et.al., 2019. Social media big data analytics: A survey. Computers in
Human Behavior. 101. pp.417-428.
Tiwari, S., Wee, H.M. and Daryanto, Y., 2018. Big data analytics in supply chain
management between 2010 and 2016: Insights to industries. Computers & Industrial
Engineering. 115. pp.319-330
Zhu, L., and et.al., 2018. Big data analytics in intelligent transportation systems: A
survey. IEEE Transactions on Intelligent Transportation Systems. 20(1). pp.383-398.
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