Big Data: History, Challenges, Techniques, Characteristics, and Business Support

This presentation covers the history of Big Data, its challenges, techniques for analysis, and its characteristics. It also discusses how Big Data technology could support businesses with examples. The subject is Information Systems and Big Data Analysis, and the course code is not mentioned. The presentation is relevant for students studying Big Data, Data Science, and related fields in any college or university.

History on big Data
Big data is a form of technology which is used in storing,
analyzing and managing the massive data. It is a tool
which is used to identify the patterns of the data. This
form of data is different because it is difficult to handle
and manage the large data
Information Systems and Big Data Analysis
Name of the Student
What is big Data
In every field of area such as medicine, agriculture, gambling
and environmental protection, the application of big data is
vital. Data is a raw form of facts and figures which are required
to be organized in the form of tables. The main aim of the data
collection from various sources is to analyze the pieces of data
to study its aspects separately.
Characteristics of Big data
There are various features of big data which can be
explained as given below:
a.) Volume- The big data is large in volume and carries
variety of data such as homogeneous or heterogeneous
data.
b.) Variety – The data is a raw facts and figures. It can be
either structured, semi structured and unstructured.
c.)Velocity – This term defines the rate at which data is
processed and retrieved from the huge collection of data.
d.)Veracity – It indicates the validity and accuracy of the
big data which helps to take several decisions.
e.) Value – The value of big data must carry some value
and have authentic source as well.
The challenges of big data
analytics
There are various challenges of big data which can be
explained as given below:
• A/B testing: A/B testing involves relation between a
control group with a various form of test group, in
order to identify what modification will enhance a
giver objective. It creates a hypothesis of variables
which include dependent or independent variable
(van Leeuwen, 2019). Big data suitable in this model
also, it can trial vast numbers, however it is only
helpful when the collected data is meaningful.
• 2. Data fusion and integration: It
refers to the combination of various techniques
which analyses data from aggregate sources and
solution.
Kumar, P.A.V., 2018. The use of big data analytics in
information systems research. Available at SSRN 3185883.
Pani, S.K and et.al., 2021. Applications of Machine Learning
in Big-Data Analytics and Cloud Computing (pp. i-xxxii).
River Publishers.
Rachman, Z.A., 2019, July. Big data analytics in airlines:
Efficiency evaluation using DEA. In 2019 7th International
Conference on Information and Communication Technology
How Big Data technology could
support business & Examples
• Enhance the product quality: Big data help in
analyzing the demand of the consumers which help to
improve the quality of existing product. It also
suggests the company to launch new product to meet
consumer wants. Big data provide support in that
areas where company is lacking.
• Data safety and security: Big data ensure that the data
is stored at a place where it is secured and protected
from the various risks, frauds and hacking.
Techniques that are currently
available to analysis big data
1.)a/b testing- It is a technique which helps to compare the
two different web pages and takes decisions regarding the
best performing web page.
2.)data fusion and integration- The analysis of data is done
when data is inspected closely and integration includes the
synthesis of the several components of the data.
3.)Statistics- It is branch of knowledge which collect,
organize and interpret the data to take various decisions.
4.)Natural language processing – It is a language which uses
simple English words and easily understandable by the
humans.
5.)data mining – it is the process of extracting data from
several sources to know the patterns of the consumer.