Information Systems and Big Data Analysis: Techniques and Challenges

Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
1

Contents
Introduction 3
What big data is and the characteristics of big data 3
How Big Data technology could support business, an explanation
with examples 5
Poster
References 7
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Introduction
Big data is a pack of huge volume of data which is difficult to manage. It is of
two types structured data and unstructured data. It is useful in analyzing the the data
to take decisions on the insights of the business concern (Aboelmaged, 2018). Big
data contains complex data which is managed by the software to reach to a
conclusion of the data. In recent time data are collected and processed by the
software to be used by the management. Except of these it also includes some
challenges involved that also needs to be considered while taking this into account.
Key technique for the purpose of big data analysis.
What big data is and the characteristics of big data
Big Data includes gathering of information by the other means that can be
used by the business concern to make use of the data for the improvement and for
taking new decision in the firm (Adamopoulos, Ghose and Todri, 2018). It used by
the firm in the process of machine learning, predictive modeling and other analyses
as well. The can be structured, un-structured and semi-structured data that can
efficiently stored. These data is huge number does software are used to analyses the
data collected from the sources. In this big volume of data is present which needs to
be analyzed. These data have a storage of more than terabytes and petabytes. The
data is examined and different conclusion are extracted from it and used for the
organization's different perspective. In today's environment the each company uses
data of their clients to analyze their choices and the data entered by the customers
for the purpose of shoping is used by the companies to extract different outcomes
from it.
Diagnostic of Big Data
 Volume: It ascertains the number of respondents to be considered for the
purpose of the analyses.
 Variety: It determines nature of the data, whether it is homogeneous or
heterogeneous source. The data is collected from different sources that is
complied in the spreadsheets and database.
 Velocity: It includes generation of data and how it is processed to fulfill the
demand of the consumer. It ascertain the potential of data and the outcomes
that can be concluded from the data available.
 Variability: Data taken varies from person to person as every individual has
different perspective. The data needs to effectively managed so that the
resultant outcomes can portrays the actual results.
The challenges of big data analytics
The challenges of big data analytics
Data handling is one of the key challenges in the field of
technology. Besides, there are few challenges which are observed
while operating with big data (Burrough and Frank, 2020). The description
of the key challenges are explained as given below -
1. Lack of knowledge professionals - Every technology require
skilled manpower to run the new innovations. It requires training and
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