Big Data Analysis: How Technology Supports Business Operations

This report provides a comprehensive overview of big data analysis, beginning with a definition of big data and its key characteristics: volume, variety, and velocity. It delves into the challenges associated with big data analytics, including data management uncertainty, talent gaps, platform integration, synchronization across diverse data sources, and extracting meaningful insights. The report outlines various techniques for analyzing big data, such as A/B testing, data fusion, data mining, machine learning, natural language processing, and statistical analysis. Furthermore, it explains how big data technology supports business through better decision-making, customer understanding, improved services, streamlined operations, and revenue generation, providing practical examples to illustrate these points. The report concludes with a poster and references.

INFORMATION SYSTEMS
AND BIG DATA ANALYSIS

TABLE OF CONTENTS
Introduction......................................................................................................................................1
What big data is and the characteristics of big data.........................................................................1
The challenges of big data analytics................................................................................................1
The techniques that are currently available to analyse big data......................................................2
How Big Data technology could support business, an explanation with examples........................3
Poster...............................................................................................................................................5
REFERENCES................................................................................................................................6

Introduction
Big data is a kind of data that contains greater variety, arrives in increasing volume as well
as with more velocity. Big data has been in use since 1990s. however there is no clarity about
who used this term for the first time buy most commonly Jhon R. Mashey is given credit for this.
In last few years overall volume of data which is being used and generated has increased and is
mostly used for decision making process because of which big data has come into picture.
What big data is and the characteristics of big data
What is big data
Big data is an extremely large dataset which is analysed conceptually for revelling
patterns, associations, trends identification etc. In other words, it is a large, complex data set
from varied sources that are stored at a single place. These data sets are of so large quantity that
they cannot be managed by traditional data processing software’s.
Characteristics of big data
Volume, variety and velocity are some of the main characteristics of big data that together helps
in defining what big data is. All the three characters are as follows:
 Volume: today high volume of data is being stored exponentially. Today unlimited amount of
data is being created, analysed and stored. It is one of the main characteristics of big data
which is required by organizations. It is useful for organizations such as banks as it directly
helps in storing different kinds of data of customers safely and securely.
 Variety: when large volume of data is being stored, it being one of the main challenges
associated with it which is storage of variety of data. it is another main characteristic of big
data. big data provide a feature of storing large and different variety of data or information
together. It is an important feature or characteristic of big data which is required by
enterprises so that they can store all variety of data together.
 Velocity: it is another characteristic of big data i.e., all the variety of large volume data which
is generated at high velocity need to be handled. Big data helps in accommodating large
velocity of data together in an appropriate manner.
The challenges of big data analytics
There are many different kinds of challenges that are associated with big data analytics.
Some of the main challenges of big data analytics have been explained below in detailed manner:
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Data management landscape uncertainty: big data is expanding continuously and there are
variety of new companies as well as advance technology that are being developed day to day.
Due to this, it becomes one of the main and biggest challenge for companies to identify
introduction of which technology would be best suitable for them that would introduce lowest
amount of risk and problems for the company.
Big data talent gap: Big data and its use is growing exponentially but despite of this, there are
very less number of experts who have complete knowledge of his field due to its complexity.
People who understand this complexity and its intricate nature are quite few due to lack of talent
and gap in terms of knowledge and talent of people in this industry.
Getting big data into big data platform is another kind of challenge. Organizations today need a
single platform where they can store large amount of data on regular basis. Simple, accessible
and convenient source of platform which is available for storing data is another challenge of big
data analytics.
Need of synchronization across varied sources of data: it is one of the main type of challenge
which is associated with big data analytics due to diverse variety of data sets because
incorporation of this type of data into an analytical platform is a bit difficult. If this challenge is
not overcome then it can result in creation of gap within data and can further lead to creation of
wrong message and insight which can further result in wrong or false decision making.
Getting important insights with the help of Big data analytics: it is another main challenge
associated with big data analytics because in order to gain correct information it is extremely
important for companies to access correct insight from big data analytics and not doing so can
affect accuracy of data or information
The techniques that are currently available to analyse big data
There are many different kinds of techniques that are available for analysis of big data.
Some of the most commonly used and available techniques available for analysis of big data are
as follows:
 A/B testing: this technique is used for comparing control group with large variety of test
groups, in order to identify type of treatment or type of changes that can directly help in
gaining objective variable. This technique directly helps in testing large or huge numbers for
achievement of meaningful difference.
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 Data fusion and data integration: it is a kind of technique which is used for analysing and
integrating data from large number of sources as well as solutions for gaining more insights
in an effective manner so that accurate information can be extracted and developed from a
single variety of data source.
 Data mining: it is one of the most common type of technique which is used for analysis of
big data in which a patter in extracted from large amount of data set in which different kinds
of methods are combined with machine learning. It is used by organizations for analysing
needs and preferences of customers.
 Machine learning: this technique is used for analysis of big data in field of artificial
intelligence. It uses varied type of computer algorithm for analysing data and identifying
appropriate and accurate assumptions based upon data. it provides variety of ways if
analysing information and provide predictions that are impossible for human analysis.
 Natural language processing: it is another one of the most important type of technique which
is used for analysis of big data in which different types of algorithms are used for analysis of
human language.
 Statistics: this technique is mostly used for analysing, collecting, interpreting, organizing big
data. this technique is most commonly used within surveys or within experiments.
There are many other kinds of techniques that are used for analysis of big data in an
appropriate and accurate manner. Some other kinds of techniques that are used for analysis of big
data are: spatial analysis, association rule learning, predictive modelling, network analysis and
many other kinds of techniques. Each of these techniques are used in different ways for analysis
of big data and are used for different variety of purposes. As technology is progressing, need and
demand of different type of data is increasing. Different types of techniques are used by different
kinds of organizations for varied purposes.
How Big Data technology could support business, an explanation with
examples
There are many different ways that can directly help in explaining ways in which Big
Data technology could support business. Effective use of big data technology can directly help
and support business in many different ways. Some of the ways in which Big Data technology
could support business have been explained below with examples:
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 Making better business decisions: Big Data technology directly helps in making smatter
decisions. It can directly help in bringing improvement within decision making. Not only
this, it can further help in identifying issues or gap within current business operations or
process so that effective and appropriate decisions can be taken. For example: if sales of a
product decrease suddenly due to miscalculated higher price, then with the help of Big Data
technology they can easily identify this issue and take important decisions for correcting this
miscalculated higher price in an appropriate manner.
 Understanding your customers: Big Data technology can directly help a business
organization in understanding their customers in an appropriate manner. For example, can be
used by organizations for identifying type of products preferred by their customers, price
range which is most commonly opted by customers so that they can bring required changes
within their sales strategy in an appropriate manner.
 Delivering smarter services or products: IT can directly be used for designing of smarter
services and products that suits their customers needs and requirements. For example: bank
can use this Big Data technology to understand need of their customers about loan services,
financial services etc.
 Improving business operations: Big Data technology can be used for identification of flaws
within business operations or issues that are required to be improved in an appropriate
manner. For example: organizations can use this technology for identifying issues faced by
employees and for this they can introduce chatbot facilities for their employees where most
common answers of customers of customers can be answered in an appropriate manner.
 Generating an income: Big Data technology can directly help in generating revenue and can
work as an additional income stream. Such as banks can use this technology for handling
credit card transactions and can make online transactions much easier so that customers start
using their services more as compared to their customers.
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Poster
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REFERENCES
Books and Journals
Kamilaris, A., Kartakoullis, A. and Prenafeta-Boldú, F.X., 2017. A review on the practice of big
data analysis in agriculture. Computers and Electronics in Agriculture, 143, pp.23-37.
Oussous, A., and et. al., 2018. Big Data technologies: A survey. Journal of King Saud
University-Computer and Information Sciences, 30(4), pp.431-448.
Wang, J., and et. al., 2020. Big data service architecture: a survey. Journal of Internet
Technology, 21(2), pp.393-405.
Hasan, M.M., Popp, J. and Oláh, J., 2020. Current landscape and influence of big data on
finance. Journal of Big Data, 7(1), pp.1-17.
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