Information Systems and Big Data Analysis - Characteristics, Challenges, Techniques and Business Support

This report covers the meaning and the characteristics of Big Data, challenges and the techniques that are available to analyse these data and the technology used with examples. It also explains how Big Data technologies are supporting businesses in understanding customers, developing new products, keeping data safe and making use of quality data.

Information Systems
and Big Data Analysis

Table of Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY ..................................................................................................................................1
What is Big Data and what are the characteristics of Big Data?.................................................1
What are the challenges of the big data analytics?......................................................................2
What are the techniques available for big data analysis?............................................................3
How are Big Data technologies supporting businesses?.............................................................4
References:.......................................................................................................................................5

INTRODUCTION
Information Systems are the integration of elements that are used in storing and processing data
in order to provide products and information. Big Data, as the name stands, is referred to
material in form of data that is huge in size and are obscure in understanding. Big Data has been
in consideration since ages but it was in 1990's that it became popular due to a guy named 'John
R. Mashey' (Eken, 2020). In this report, the meaning and the characteristics of the Big Data will
be covered. The challenges and the techniques that are available to analyse these data and the
technology used will be observed with examples. At the end there will be a poster demonstrating
this article it to understand it in better way.
MAIN BODY
What is Big Data and what are the characteristics of Big Data?
To begin with, Big Data is not a new process. People have been trying to simplify large data for
their analysis for centuries. However, in last few years the data have been scaling in zettabytes.
To illustrate it better, the total data in the world was around 44 zettabytes in 2020. Even though
the technologies are very advanced today, they are not enough to properly convert these data into
information. Hence, it can be said that the Big Data is the collection of diverse data, present in
huge magnitude and arriving at great velocities (Jamshidi and et.al., 2018). These data can help
in storing information for future purposes and can be helpful in sectors like Healthcare, sports, IT
and research etc. Even though this is so advantageous, it does have some drawbacks. Violating
privacy of people and the expenses to store it are some the drawbacks of Big Data analysis.
Characteristics of Big Data
 Volume
The first of the 5 V's is known as Volume. It is considered as the magnitude of data that exists. If
the data is very large it can be considered as Big Data. But there are conditions which is to be
met to be considered a Big Data. The fast increasing Volume of Big Data is due to many factors.
These factors are Cloud computing, Internet of things and High no. of internet users.
 Velocity
It is considered as the speed at which the data is moved. It is required for the companies to
analyse and process data quickly so as to get the best possible information from the data. For
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example the rate at which the searches were made in the google was around 32.8 millions per
day. As in 2018, this was increased up to 5.6 billion searches per day.
 Variety
The third of the 5V's of Big Data, Variety is considered as the diversity in the types of data.
These can be structured, unstructured or semi-structured in nature(Jiang, Huo and Song, 2018).
These structures also determine how easy it is to analyse them. An unstructured data will be
much more complex than the structured ones.
 Veracity
It is considered to be the preciseness of the data. The better the quality of the data, more is the
veracity of it. It needs to be accurate as wrong data can lead to taking wrong insights from the
data. The veracity and the value together define the quality of the data. Because the data is
collected from tons of sources, it is required for the company to convert it into accurate data
before it could be used for business functions.
 Value
The last V of the 5V's is called as the value. How big the data maybe, the value it provides define
the amount of required information that can be taken from the data. These should be unique to
the companies or the value of the big data will decrease(Khalid, and Zeebaree, 2021).
What are the challenges of the big data analytics?
 Too large to store
The big data usually lives up to its name, and are usually so big in magnitude that it becomes
very tough to store. The technologies though, have improved over time, but the growth of big
data has happened in a higher pace than the technology. Also the other reason because of which
the data is tough to store could be the complexity of it .
 Fetching the meaningful data
Getting good and meaningful information from the big data is important and a very basic
requirement for the companies. They spend millions in retrieving the valuable information. This
meaningful data is then processed for useful information needed by the companies. These data
are confidential and very valuable.
 Keeping up with the high velocity
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It is big challenge to keep up with the pace by which the data is coming. Similarly the demand
for the data is large too. And the big data technologies have not been able to keep up with this
much velocity(Kolisetty and Rajput, 2020).
 Safekeeping the data
As the value of the data increases the demand and greed to avail it increases too. The information
taken out by the companies are targetted by the hackers and if hacked could lead to
unprecedented situation which would be tougher and expensive to handle.
What are the techniques available for big data analysis?
 Cluster analysis
The action by which the data elements are clustered in a way that the data is simplified enough to
get the meaningful information is known as Cluster analysis. For example- By clustering
customers into different genders and then marketing them in that basis can be considered as
cluster analysis(Liu, 2020).
 Cohort analysis
In cohort analysis, the existing data is used to retrieve data meaningful information. It is a form
of analysis that takes data and groups it into related groups rather than seeing it as a single group.
One of the best examples of the cohort analysis is Google analytics.
 Regression analysis
In this analysis method, there is a correlation between a dependent variable and the outcome of a
independent variable. For example the relationship between the money spent in the marketing
and the sales incurred due to it could be an example of regression analysis.
 Factor analysis
Factor analysis is the way of simplifying the data in many variables into a data of few variables.
For example- for studying a very broad market, the factor analysis can be done to factor into
simpler data to study.
 Data mining
In this technique of Big Data analysis, the large sets of data are sorted to identify patterns that
can help in simplifying business issues. These techniques help the companies in forecasting
future trends and make more information based decision making.
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How are Big Data technologies supporting businesses?
Using Big data technologies has become a very crucial requirement for the companies to succeed
and move over their competitors. Whichever sector may be the business in, it will require the
heavy usage of Big Data to work. The following are the ways by which Big Data can support
businesses:
 Understanding the customers
Gaining proper insights from the Big data helps the companies in knowing the customer wants
and satisfaction. Also using the Big Data the target market could be identified which could
reduce the marketing cost at a broad level(Lv and Singh, 2021).
 Developing new or refreshing the old products
The Big Data could provide the meaningful and the required data that is needed for knowing the
requirements of the new products. Successfully bringing in new products can help the company
in staying relevant in the market for a longer time. Also, with the right data the old products
could be redone for a better result. A good example of this could be the automobile sector which
keeps on updating their products to keep up with the customer satisfaction.
 Keeping the data safe
The tools of Big Data have the capability of safekeeping the company's confidential reports and
data. This advantage is making the companies to focus on the Big Data tools for security reasons.
Since companies deal with high finances and classified information, Big Data comes as a big
support to the business. An example of this could be that the tools of Big Data allows people
to map the entire data landscape across the company which helps them in tackling the internal
threats(Reis, Bornschlegl and Hemmje, 2021).
 Making the use of quality data
Big Data, as the name suggest, is quite large and are coming at high velocities and thus there is
no shortage of quality content present. It just have to be processed and cleansed. Then the quality
data produced could be used by entrprises to use for marketing, sales, operation and expand etc.
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References:
Books and Journals
Eken, S., 2020. An exploratory teaching program in big data analysis for undergraduate
students. Journal of Ambient Intelligence and Humanized Computing, 11(10), pp.4285-
4304.
Jamshidi, A., and et.al., 2018. A decision support approach for condition-based maintenance of
rails based on big data analysis. Transportation Research Part C: Emerging
Technologies, 95, pp.185-206.
Jiang, D., Huo, L. and Song, H., 2018. Rethinking behaviors and activities of base stations in
mobile cellular networks based on big data analysis. IEEE Transactions on Network
Science and Engineering, 7(1), pp.80-90.
Khalid, Z.M. and Zeebaree, S.R., 2021. Big data analysis for data visualization: A
review. International Journal of Science and Business, 5(2), pp.64-75.
Kolisetty, V.V. and Rajput, D.S., 2020. A review on the significance of machine learning for
data analysis in big data. Jordanian Journal of Computers and Information Technology
(JJCIT), 6(01), pp.155-171.
Liu, X., 2020. Analyzing the impact of user-generated content on B2B Firms' stock performance:
Big data analysis with machine learning methods. Industrial marketing management, 86,
pp.30-39.
Lv, Z. and Singh, A.K., 2021. Big data analysis of internet of things system. ACM Transactions
on Internet Technology, 21(2), pp.1-15.
Reis, T., Bornschlegl, M.X. and Hemmje, M.L., 2021. Toward a Reference Model for Artificial
Intelligence Supporting Big Data Analysis. In Advances in Data Science and Information
Engineering (pp. 561-572).
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