BMP4005 - Big Data's Role in Supporting Business Strategy

This report provides a comprehensive overview of big data, including its characteristics (volume, velocity, variety), the challenges associated with its analysis (lack of skilled professionals, understanding, and tool selection), and the techniques currently available for analyzing it (A/B testing, data blending, data mining, machine learning, natural language processing, and statistics). It further elaborates on how big data technology can support business by enabling better decision-making, more efficient operations, and the identification of new opportunities, citing examples like MongoDB, Redis, and Cassandra. The accompanying poster summarizes key aspects of big data, its history, challenges, characteristics, and technologies. Desklib offers more solved assignments and past papers for students.

Business Management
BMP4005
Information Systems and Big Data Analysis
Poster and Accompanying Paper
Submitted by:
Name:
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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
The term massive data is a bit of a misnomer since it implies that the existing data is
somehow tiny, or else the only challenge is its utter size. It is a term that elaborates the large,
hard-to-manage data volumes in both structured and unstructured forms. It can be analyzed
for insights that can develop the decisions and provide confidence for outlaying the moves in
business. Here, this report will cover the concept of big data as well as its characteristics as it
can help to enhance the marketing techniques and determine the problems in real-time. This
report will also cover the various summons which are arising in the analysis of large data. In
this, there is a discussion about several currently available techniques to determine the
concept of big data. This report will also involve information about the big data technology
that can empower the business as it generally aids the organization in determining the
upcoming opportunities (Patel and Sharma, 2020).
What massive data is and the characteristics of massive data?
The concept of giant data is a gathering of data that is generally vast in volume yet
growing rapidly with time. It is particularly a data with large size and complexity that no
outdated data tool can store or else can process considerably. It is also considered as a data
with big size. The concept of massive data can also have explained as leveling up a variety of
evidence assets that can demand the cost effective, the innovative ways of the data processing
that allow increased insights, making decisions, and the automation of the processes. Three
main characteristics can define massive data such as velocity, variety as well as volume. The
velocity generally refers to the speed of the processing of information. An increased velocity
is essential for the performance of the process of the massive data. In context with volume, it
refers to the amount of data that an organization contains. Data can be measured in units
called Gigabyte (GB), Zettabytes (ZB), and the Yottabyte (YB). The theory of variety in
massive data includes the various types of massive data. It can impact the performance of the
organization in a positive manner. It is lacking, which affects the organization as it is the
most common or most significant problem faced by the massive data (Lazarescu and
Gheorghe, 2018).
The challenges of gaint data analytics
The oppositions of big data can involve the best way of handling the various amounts of
information that contains storing and measuring the broad set of data on the number of storage of
pieces of information. Big data consists of many challenges which will come across the way
during the handling of big data and are as mentioned below:
Lack of knowledge in professionals: This involves the skilled data professionals so as to
run such kinds of modern technologies as well as the wide varieties of data tools. The
professionals here include such as data scientists and analysts as well as data engineers so as to
work with the tools. The most one of the challenges in big data which any of organization can face
is the hindrance of the deficiency of big data professionals.
Lack of understanding about big data: Most organizations can fail in the initiatives of big
data and this is all because of lack of understanding. The workers of the organization might not
have been informed about what data is as well as its storage, essentiality, and sources. If the
workers do not understand the necessity of big data, then they will not maintain the backup of
such sensitive pieces of information regarding the organization.
Confusions while selecting the big data tools: Many organizations often get confused
while selecting the big data tools for analysis and storage. This confusion sometimes leads to
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making inappropriate decisions which in results leads to wastage of money, time, efforts as well
as the working hours. It is essential to hire experienced professionals as they contain much more
information about these tools.
Techniques which are currently available for analyzing the big data.
It involves various methods available to determine the massive data, such as A/B testing,
the blending of data and data integration, data mining, machine learning, usual language
processing and statistics. In context with A/B testing, it includes the comparison of a regulate the
group with a variety of test groups to discern what treatment or modifications will enhance the
provided objective variables. In context with the fusion of data, it involves a set of techniques that
determines and integrate the information from various sources as well as the solutions. Data
mining a mainly a most common tool that is used among big data analytics, the concept of data
mining usually extracts the pattern from a variety of information sets by associating the methods
from statistics as well as machine learning in between the database management (Big Data
Analysis Techniques, 2019).
How the Massive Data technology could help the business, an explanation with examples?
A massive information is a particular indication which is used to illustrate the broad
assemblage of the information. The information is generally big in size as well as elevated
exponentially with time. These simply can specifies the big data which is quite hard to investigate,
stock as well as transform with the help of tools of management. In context with massive data
technologies, it is generally the utilized software which incorporates the mining of information, its
storage, sharing, information visualization as well as the framework of information consisting the
tools as well as the technologies that are used to investigate and transform the information as well.
The massive data technologies generally aid the organizations support their knowledge as well as
they use the technologies to determine the upcoming or new opportunities. It can ultimately lead
to more innovative business moves; the more sufficient operations, the more significant benefits
as well as, the happier consumers. It includes examples such as MongoDB, Radis, and Cassandra
(Li, 2019).
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Poster
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Big Data Analysis and information system.
HISTORY.
 The concept of massive data is
being used since early 1990s.
 Still, it is not exactly known till
about who firstly used the term,
most of the people credits the John
R Mashey for making this term so
popular.
BIG DATA.
 The concept of big data is basically a collection
of data which is generally huge in volume, yet
growing exponentially with time.
 It is specifically a data with having large size as
well as complexity which no traditional data tool
can store it or else can process it significantly.
CHALLENGES OF BIG DATA.
 Lack of knowledge in professionals
 Lack of understanding about big data
 Confusions while selecting the big data
tools
CHARACTERISTICS OF MASSIVE DATA.
 Velocity
 Volume
 Variety
TECHNIQUES TO ANALYSE THE BIG
DATA.
 A/B testing
 fusion of data and data integration
 data mining
 machine learning
 natural language processing
 statistics
REFERENCES
Books and Journals
 Patel, J. A. and Sharma, P., 2020. Online
Analytical Processing for Business
Intelligence in Big Data. Big Data, 8(6), pp.501-
518.
 Lazarescu, I. and Gheorghe, G., 2018.
Predictive modeling organizational change
–using Big Data. Risk in Contemporary
Economy, pp.224-228.
 Okuyucu, A. and Yavuz, N., 2020. Big data
maturity models for the public sector: a review
of state and organizational level
models. Transforming Government: People,
Process and Policy.
BIG DATA TECHNOLOGY.
 A massive information is a particular
indication which is used to illustrate
the broad assemblage of the
information.
 It includes examples such as
MongoDB, Redis, and Cassandra.

References
Books and Journals
Patel, J. A. and Sharma, P., 2020. Online Analytical Processing for Business Intelligence in
Big Data. Big Data, 8(6), pp.501-518.
Lazarescu, I. and Gheorghe, G., 2018. Predictive modeling organizational change –using
Big Data. Risk in Contemporary Economy, pp.224-228.
You, Y., 2018, July. Big data-driven innovation: an empirical research on sustainable
development of business model. In International Conference on Applications and
Techniques in Cyber Security and Intelligence (pp. 907-914). Springer, Cham.
Li, Y., 2019, April. Research on Financial Risk Prediction and Prevention Countermeasures Based
on Big Data. In 2019 11th International Conference on Measuring Technology and
Mechatronics Automation (ICMTMA) (pp. 564-567). IEEE.
Okuyucu, A. and Yavuz, N., 2020. Big data maturity models for the public sector: a review of
state and organizational level models. Transforming Government: People, Process and
Policy.
Xiaoyan, H., Rong, J., Xiaoming, H. and Luxiao, W., 2020, June. Thoughts On The Ecological
Environment Management Innovation Driven By Big Data. In 2020 International
Conference on Big Data, Artificial Intelligence and Internet of Things Engineering
(ICBAIE) (pp. 1-4). IEEE.
Online:
Big Data Analysis Techniques, 2019 [Online] Available through:
https://www.getsmarter.com/blog/career-advice/big-data-analysis-techniques/
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