Big Data's Role: Information Systems, Analysis, and Business Support

This report provides an overview of big data analysis within information systems, highlighting its characteristics, challenges, and techniques. It discusses the importance of big data in supporting business organizations by understanding customers, delivering smarter services, and generating income. The report covers key aspects such as data volume, velocity, variety, value, and veracity, along with techniques like machine learning and A/B testing. It emphasizes how big data technologies enable businesses to make informed decisions and improve overall performance. Desklib offers a wealth of similar solved assignments and resources for students.

History on big Data
Big data analysis refers to a process of inspecting large and
complex data which has been used in order to disclose the
data. For instance, correlation preferences, hidden pattern
and market trends. This tool is useful in extracting the
relevant information from large data which is helpful for the
company for making effective decision which leads to the
reduction of risk. Information system has a wide concept in
the economy because all the business organizations are
related to the IT sector which include wide range of solution.
The information system use internet which is associated with
network of devices for the purpose of ensuring the
connectivity along with the exchange of data. The
communication network is created with the use of
information technology and by creating and administrating
databases the information is safeguard. Information system
plays an important role in the organization as it perform
several operations. Such as, conducting business
transactions, managing the organization, interaction with
customers and many more. This report deals with the concept
of big data which include characteristics, challenges with the
technique of big data. Furthermore, it will cover the
description of how big data technology support the business
organization.
Information Systems and Big Data Analysis
Name of the Student
What is big Data
Big data is defined as the data which is fast, long and complex and it is really
difficult to process with that b the use of traditional methods. Big data analysis
include the actions of accessing and storing the huge amount of information for
analytics has been around for a long time. Big data analytics are made for the
purpose of helping the organization for taking data driven decisions which
leads to the improvement of businesses results. By the use of Big data,
organizations can improve their work performance by providing better services
to their customers which leads to the increment in the overall revenue and
bottom line. The size of data is large and it is impossible to process with that
data by the use of traditional method. Big data analytics makes the data easier
for the companies so that they can use it for the purpose of finding new
opportunities which also result in the smarter management of the company.
Hence, it is related to the generation of higher profit by performing the
business operations in more effective and efficient manner which also result in
happy customers and positive feedback. Big data involve several functions
such as data mining, data storage, data visualization, data analysis and many
more.
Characteristics of Big data
Volume: Big data deals with the several of technology processes which are related to the large
amount of data which has been collected from several of resources such as social media, machines,
networks and many more.
Velocity: The velocity of big data is related to the speed of flow of data from various sources such
as application logs, social media sites, sensors, business processes, networks, and many more.
Variety: Data is found in any type of format such as numeric data, audios, videos, financial
transaction, emails and many more. In simple words, this feature of big data defines the nature of
extracting the data (Wang and et.al, 2020). Above mentioned are the modern formats of data
whereas in the ancient times, spreadsheets and databases were used.
Value: It refers to the benefits abstracted from the data. It is necessary that the data should be
relevant and valuable for processing. Data is consider as valuable after its successful analysis.
Veracity: This feature is related with the reliability and trustworthiness of data as there are many
ways to translate the data. In simple words, it can be said that the accuracy of data can be
determined.
The challenges of big data analytics
The challenge of Big data refers to the searching of best way
of managing the large amount of data, which also involve
storing and analyzing the information in multiple data stores.
Some of major challenges of big data analytics are explained
below as:
Lack of knowledge professionals: In order to use the big
data techniques, the organizations are required to hire
professional people having proper skills of using large data
tools. Some of professionals of big data who work with the
several tools of data are known as data engineer, data analyst
and data scientist.
Lack of proper understanding of Massive data: Due to the
insufficient understanding, organizations are fail in their big
data initiatives. Mostly the employees are not aware about
the data, its storage, importance, source, processing and
many more.
Data Growth Issues: One of the major challenge of big data
analytics is storing of large sets of knowledge. The quantity
of knowledge being stored in data centers and databases of
organizations increases quickly. It is really a huge challenge
to handle these massive data.
Confusion while big data tool selection: It is a confusing
step to select the most simplest tool for giants data analysis
and storage. There are several questions while selecting any
tool and it has been found that sometimes the companies are
not able to find out the answers.
References
Pramanik and et.al, 2017. Big data analytics for security and criminal investigations. Wiley interdisciplinary reviews: data mining and knowledge discovery.
7(4). p.e1208.
Sun and et.al, 2020. Big data analytics for venture capital application: towards innovation performance improvement. International Journal of Information
Management. 50. pp.557-565.
Wang and et.al, 2020. Big data analytics on enterprise credit risk evaluation of e-Business platform. Information Systems and e-Business Management.
18(3). pp.311-350.
How Big Data technology could support business & Examples
The technologies of big data provide new opportunities for business growth from the internal
view for the purpose of increasing interaction with users or customers. Big data provide the
tools to the business organizations which are helpful in making smart decisions based on the
data or assumptions. The big data offers new perspective for companies to discover
information which can be utilized in appropriate way. Big data can help the business
organizations in the below mentioned ways:
Understanding the Customers: By the use of big data, the companies performing business
operations can get to know more about their customers. The marketers get to know more
insider to their customers that what they want, what channels they use to buy the products,
what they will use and many more. For instance, Disney is leveraging Big data technology
for the purpose of understanding the behavior of visitor at its theme park (Pramanik and et.al,
2017).
Delivering smarter services or products: When an organization get to know about the
needs and preferences of their customers, they just focus on the production of smarter
products or services. Disney is doing this with its MagicBrand initiative. For instance, Royal
Bank of Scotland is using big data for the purpose of delivering a better service to their
customers. RBS is beginning to harness the potential of this knowledge to better meet
customer's need.
Generating an income: Big data not only improve the decisions making processes but it also
leads to the understanding the behavior of customers. It helps the organization in boosting the
revenue or creating the extra additional income stream. For example, American Express is
handling more than 25 percent of credit card transactions in the US. Amex is leveraging the
data generated by these transactions for the purpose of bringing the businesses and customers
close together.
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Techniques that are currently available to
analysis big data
It has been found that the organizations use a variety of
techniques for data management in order to acquire more
insight into speed, scope and depth. Some of techniques
of analyzing big data are given below:
Machine learning: It refers to the technique of big data
which make the data more understandable by displaying
more trends and patterns. This technique work as
advantage in accelerating the processes by the use of
algorithms in order to making decisions (Sun and et.al,
2020). It is related with the recognition of trends &
patterns, upcoming information and then data is
converted in visualize form. It also provide prediction
which is a limitation in human analyst.
A/B Testing: Under this technique, the comparison
among the two variants of a version have been done for
the purpose of identifying the the better performers under
a controlled environment. It perform the function of
comparison, hence the successful completion of A/B
testing technique result in the higher earn of profit.
Statistics: While analyzing the big data, it perform the
function of collecting, organizing and inferring the data
by the use of several research methods such as base
surveys and experiments. Statistical technique make it
easy to analyses the big data by inferring understanding
about implicit data sets or the fact that it is trying to
explain.