Analyzing Big Data: Information Systems, Business Support & Examples

This report explores the critical role of big data analytics and information systems in enabling companies to make strategic business decisions, analyze consumer behavior, enhance operations, and develop smarter products. It identifies key big data technologies like Hadoop, Spark, and Hive, emphasizing the core categories necessary for tracking client-related data and achieving financial performance. The report also addresses the challenges associated with big data, including data security, complexity, and quality management. Furthermore, it outlines available techniques for analyzing big data, such as machine learning and statistics, and references McKinsey's analysis on strategies used by large collaborations to handle diverse data.

History on big Data
Big Data analytics and information systems play a key
role in helping companies to employ sophisticated
analytics approaches to create strategic business
decisions, analyses consumption behavior, enhance
corporate operations, supply smarter goods and
services, and produce more amount of income.
Big data technologies focus on the fundamental
categories that are required for tracking the critical
data that will relate to the client. By big data
technologies, the company may achieve financial
performance.
Information Systems and Big Data Analysis
What is big Data
Big data approaches are having an influence on the computing
technologies sector in today's competitive environment. Hadoop,
Spark, Hive, and Cloud are examples of big data technologies. This
also software applications that assist the firm in handling the large
data involved in the business. This encompasses the layout of
many aspects that preserve information and data preservation
that are effective for corporate growth by generating big data
synchronization.
• Volume
• Variety
• Velocity
• Veracity
• Value
Characteristics of Big data
• The volumes of information - It is a broader division, and management
must create effective methods for managing data partitions..
Data variety - It is enormous in the area of big data technology and must
be kept effectively (Demirkan, and Delen, 2017). It includes the data
application's segmentation.
• Data velocity - This will be studied how fast information may be moved
from one department to another department.
• Veracity - It refers to inconsistencies and uncertainty in information,
which implies that the existing information would become untidy at
times, and the accuracy and accuracy are hard to regulate..
• Value - The information itself has no significance or purpose, but it
should be turned into anything useful in order to retrieve data.
The challenges of big data
analytics
Big data analytics will be regarded as an efficient business
department. It has lasting implications, and most
administration in the current period uses big data technology
(Erraissi, Belangour, and Tragha, 2017). To maintain consumer
happiness over a longer length of time, businesses must focus
on big data. The following are the basic issues in big data:
• Data security
• Data complexity
• Managing quality data
References
Markl, V., 2019. Breaking the chains: On declarative data analysis and
data independence in the big data era. Proceedings of the VLDB
Endowment, 7(13), pp.1730-1733.
Van der Aalst, W. and Damiani, E., 2017. Processes meet big data:
Connecting data science with process science. IEEE Transactions on
Services Computing, 8(6), pp.810-819.
Wixom, B., and et.al., 2019. The current state of business intelligence in
academia: The arrival big data. Communications of the Association for
information Systems, 34(1), p.1.
How Big Data technology could
support business & Examples
• Customer engagement
• Data privacy
• Data management
Techniques that are
currently available to
analysis big data
According to Mckinsey's analysis, there are some strategies used
by large collaborations to address the issue of large and diverse
data. The many approaches used in big data are explained in the
table below:
Machine learning
Statistics