Big Data Analysis and Information Systems for Business Management

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
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outdated data tool can store or else can process considerably.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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