Information Systems: Big Data Analysis, Business Support & Examples

This essay provides a comprehensive overview of big data analysis, starting with its historical context and evolving into a detailed examination of its definition and key characteristics, including volume, value, variety, velocity, and veracity. It explores the challenges associated with big data analytics, such as the lack of skilled professionals and the complexities of predictive analytics. Furthermore, the essay discusses various techniques used in big data analysis, including knowledge discovery tools, stream analytics, and in-memory data fabrics. It highlights how big data technology supports businesses by enabling better decision-making, improving customer understanding, and facilitating the development of smarter products and services. The essay concludes by referencing several academic sources, emphasizing the growing importance of big data in modern business operations and information systems.

Information Systems and Big Data Analysis
History on big Data
Data is the characters, symbols, quantities on which the
computers operate. These can be stored and transferred from
one computer or device from another. Big data is data which
is very large in volume and is growing very rapidly (Farboodi,
M., et.al., 2019). The size of these data is very large that it is
very difficult to manage by the traditional tools of data
management and even they cannot store and process it
effectively.
The challenges of big data analytics
There are various challenges which are faced using the big
data are-
Lack of knowledge- Modern technologies and managing tools
for large data companies require highly skilled employees. For
these companies hire data scientists, data analysts and data
engineers to work for these big data. The biggest challenge
faced by the companies there is lack of knowledge for working
with these big data this is because there are many tools
manufactured to handle the data but there is lack of
knowledge in professionals to handle these data.
What is big Data
Big data is larger as well as more complex data from
the new sources of data. These are very difficult to
manage by data processing software as they are
much voluminous. These large volumes of data can
be utilized to address the problems of the
organization which were not able to handle
previously. Big data analytics helps businesses in
various aspects such as helps businesses in better
understanding of consumers, identification of
operational issues, managing the supply chains and
detecting the fraudulent activities happens in the
businesses.
Techniques that are currently
available to analysis big data
Predictive Analytics- One of the most
frequently used tools in businesses
predictive analytics.
Knowledge discovery tools- This tools
helps the businesses to organize the
big data which are stored at various
sources.
Stream Analytics- The data stored of the
organizations can be stored on the various
sources.
In-memory Data fabric- This technological
tool helps in the distribution of data to
different sources like Dynamic RAM, Solid
state storage drives.
Characteristics of Big data
Volume- This feature of the big data includes the amount or size of big data that the businesses
manages and analysis.
Value- It is the most important V of the big data from the business point of view. Value is the term
which describes the usefulness of data which has been gathered
Variety- There are variety of types of data such as unstructured data, semi-structured data and
raw data
Velocity- It includes the speed at which businesses store, manage and receive the data.
Veracity- It states the accuracy of the data and its information.
How Big Data technology could support
business & Examples
Making better business decisions- It enables the
business to take smarter decisions which related
to data. Every company must have access to data
for improvement of decision-making.
Understanding the customers- It is very
important for the organizations to understand
their customers for their satisfaction. Big data
enables the organization to understand the
customers and through which they can serve
them.
Providing smarter products- By evaluating the
data the organizations can provide better
services or products to the customers
References
Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data analytics
in operations management. Production and Operations
Management. 27(10). pp.1868-1883.
De Mauro, A., et.al. 2018. Human resources for Big Data
professions: A systematic classification of job roles and required
skill sets. Information Processing & Management.54(5). pp.807-
817.
Farboodi, M., et.al., 2019, May. Big data and firm dynamics. In
AEA papers and proceedings (Vol. 109, pp. 38-42).
Marinakis, V., et.al. 2020. From big data to smart energy
services: An application for intelligent energy management.
Future Generation Computer Systems.110. pp.572-586.
Rabhi, L., et.al., 2019. Big data approach and its applications in
various fields. Procedia Computer Science.155. pp.599-605.
Ranjan, J., 2019. The 10 Vs of Big Data framework in the Context
of 5 Industry Verticals. Productivity.59(4).
Rossi, R. and Hirama, K., 2022. Characterizing big data
management. arXiv preprint arXiv:2201.05929.