Information Systems: Big Data Analysis Techniques and Challenges

This report provides an overview of big data analysis, including its history, challenges, techniques, and applications in supporting business decisions. It discusses the evolution of big data, starting from its early traces, and highlights the importance of tools and techniques for collecting and arranging data for user benefit. The report identifies key challenges such as managing data volume, ensuring real-time data relevance, visual representation, and maintaining data quality. It also defines big data as comprehensive information on a large scale, encompassing even the smallest details relevant to a specific purpose, and explores techniques like machine learning, A/B testing, data fusion, data integration, and data mining used for analysis. Furthermore, the report outlines the characteristics of big data, including volume, velocity, and variety, and provides examples of how big data technology supports business by aiding in product development, service delivery, and decision-making. It concludes by emphasizing the role of big data in helping firms determine their targeted market and gain insights into their market position.

History on big Data
Big data analysis refers to those tools and techniques
that are used for observing and collecting the big data.
These tools help in gathering the important information
from the data. Along with this, it further helps in
arranging the important data that can be used for the
beneficial of user. The big data was first time trace in
1664. With the passage of the time, the tools and
techniques of the big data has become more important as
it helps in dividing the whole data into smaller parts so,
it can be easily understood by the user
INFORMATION SYSTEMS AND BIG DATA ANALYSIS
What is big Data
The big data has been considered as the complete information about a
particular thing on a large scale. The data contains even the smallest
detail about the thing onto which the data has been prepared. This data
helps in knowing the different aspects that are relates to the same
purpose. For example: a business performs the collects the big data from
the market in order to know the current position of the business, demand
by the customers, competition in the market and how the share can be
captured. All of these aspects are related to the main purpose and that is
the success of the business.
Characteristics of Big data
Volume- It refers to the quantity of data. It shows that how
much data can be stored.
Velocity- This refers to the speed of the data.
Variety- This involves all the structured, unstructured and
semi-structured data that has to be arrange with the help of
humans or machines.
The challenges of big data analytics
The amount of data- This is very important to know that, the
amount of data that has been collected by the user should be
useful.
Real-time data- In business sector the data that gas been
arranged should on the basis of the current scenario.
Visual representation- In order to understand the data in
effective manner. The data should be presented with the help of
charts or diagrams.
Poor quality data- The quality of the data decides the
accuracy of the data.
How Big Data technology could support business &
Examples
The big data technology has been considered as the most
useful for especially the business sector. This big data
technology helps in deciding the product, services and
decision making of the business. With the help of big data
the firm are able to decide their targeted market. Observing
and arranging the big data results in providing the guidance
to the business about their position in the market.
Techniques that are currently available to analysis big data
Machine learning- This technique of analyzing the Data is the part of
artificial intelligence.
A/B testing- Under this technique, the data has been controlled in
different forms such as dividing them into text, pictures and layouts
Data fusion and data integration- Under this technique the data has
been arrange from the different sources and then the data has been
developed under the single source.
Data mining- This refers to setting the data with in the mining extract
patterns.
. REFERANCES
Pramanik, P.K.D., Mukhopadhyay, M. and Pal, S., 2021. Big
Data Classification: Applications and Challenges. Artificial
Intelligence and IoT: Smart Convergence for Eco-friendly
Topography. 85. p.53.