Information Systems and Big Data: Analysis, Challenges & Business Use

This report provides an overview of big data within the context of information systems, highlighting its characteristics, challenges, and available technologies for analysis. It begins by defining big data and its key attributes, including volume, variety, veracity, value, and velocity. The report then delves into the challenges associated with big data analysis, such as a lack of skilled professionals, inadequate understanding of big data platforms, data growth issues, and difficulties in selecting appropriate tools. Furthermore, it explores various technologies used for big data analysis, including the Hadoop ecosystem, artificial intelligence, NoSQL databases, and R programming. Finally, the report discusses how big data technology can benefit businesses through customer acquisition and retention, targeted marketing campaigns, risk identification, innovative product development, and improved supplier networks. The report concludes by emphasizing the importance of big data for organizational growth and future development.

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INFORMATION
SYSTEM AND BIG
DATA

Contents
INTRODUCTION...........................................................................................................................3
TASK-..............................................................................................................................................3
Characteristics of Big data-.........................................................................................................3
Big Data Analysis Challenges and Technologies Currently Available for Analysing Big Data.4
Technology available for big data analysable.............................................................................6
How big data technology can help your business with suitable examples..................................6
REFERENCES................................................................................................................................8

INTRODUCTION
Big data has received a lot of attention in recent years from the field of information systems.
Some recent statement, article, and special content introductions on this subject can be found in
major information system publications. These document present different position on promising
big data research content and highlight some challenges. Represents big data.(Adnan, et.al., 2020)
This article summarises this address and contributes further. Offer the first step towards
information system integrated big data research agenda by focusing on the action between the
characteristics of big data information value chain including human process technology and three
major information system research traditions information system business activity, design, and
economics. Big data is considered a wide spread disruption in the value chain Impact. Important
as Critically discuss informations system research behaviour, designing science, and economics
possibility and challenges. This report includes the characteristics, and challenges of big data
analytics and the techniques that are currently available to analysis big data.
TASK-
The amount, letters, or symbols performed by a computer that can be stored and transmitted in
the form of electrical signals and recorded on magnetic, optical, or mechanical recording media
known as data
Big data is a large collection of data, but it grows exponentially with the time. This is data of
such a large size of it and complexness that traditional data management tools cannot store or
process it efficiently. Big data is also data, but it is huge in size.(Gao, 2021)
Big data is a larger and more analysable dataset, especially from new data sources. These
datasets are so big that they cannot be managed by traditional data processing software. This
huge amount of data can be used to address previous unsoluble business problems.
Characteristics of Big data-
Volume-
Big data itself is attached with huge sizes. Big data is a huge amount of data create daily from
many platform, including business activity, machines, social media platforms, networks, human
interactions, etc.

Variety-
Big data can be organized, unorganized, semi-structured and collected from a variety of sources.
Data was previously collected only from databases and sheets, but now provided in arrangement
formats such as PDF, email, audio, SM posts, photos and videos.
Organized data-
In Structured plan along with all the required columns. It is in a tabular form. Structured Data is
stored in the relational database management system.
Semi organized data-
In semi-structured, the schema is not well defined. XML, CSV, TSV, and email. Online
Transaction Processing systems are planned to process semi-structured data.
Unorganized data-
All unorganized files, log files, audio files, and image files are included in the unorganized data.
Some organizations have a lot of data available with them, but because the data is raw data, they
did not know how to conclude the value of the data.
Veracity-
Accuracy means the reliability of the data. There are many options for filter out or transforming
the data. Veracity is a process that allows you to process and maintain your data efficiently. Big
data is also essential for business development.(Shang, Lu, and Zhou, 2021)
Value-
Value is an necessary characteristic of big data. It is not the data that we process or store. It is
precious and dependable data that is stored, process and analyse.
Velocity-
Speed plays an important role as compared to others. Velocity creates the speed at which data is
created in actual time. This consider links to incoming datasets, speeds, rate of change. The main
feature of big data is the fast delivery of advanced data. Big Data Speed supports the speed of
data flow from sources such as application logs, business processes, networks, social media sites,
detector and mobile devices.
Big Data Analysis Challenges and Technologies Currently Available for Analysing Big Data
Big data challenges-

Lack of knowledge professionals
For these latest technologies and big data tools, companies want qualified data professionals.
These professionals include data scientists, data analysts, and data engineers who use tools to
understand large datasets. One of the big data situation facing every business is the deficiency of
large data professionals. This is frequently due to the speedy evolution of data processing tools,
but in most cases professionals have not need to take certain actionable steps to fill this gap.
Lack of proper understanding of large data
Companies are failing big data platforms due to lack of understanding. Employees do not know
what data is, how it is stored, prepared, important, and where it came from. Data experts may
know what's going on, but others may not have a clear picture. If employees do not understand
the importance of protective knowledge, they may not be able to keep a backup of sensitive
content data. They were ineffective to properly use the database for storage. (Yue, et.al., 2018)
Data growth issue-
The biggest challenge with large amounts of data is to properly store this huge body of
knowledge. The amount of knowledge stored in firm data centre and databases is growing
rapidly. These datasets grow functional over time, making them difficult to procedure. Most of
the information is unstructured and comes from documents, videos, audio, text files and other
sources. This indicates that they cannot be found in the database.
Confusion while Big Data Tool selection-
Companies are rarely confused when choosing the simplest tools for analysing and storing huge
amounts of data. Sometimes they can't find the answer. They find themselves making bad
decisions and choosing the wrong technology. As a result, money, time, effort and man-hours are
wasted that cause the loss.
Integrated data from a spread sources-
Data within your administration comes from a variety of sources, consider social media pages,
ERP applications, customer logs, financial reports, emails, presentations, and employee-
generated reports. Combination of all this data into an organized document can be a discouraging
task. This is an area frequently overlooked by businesses. Data integrating is important for
analytics, reporting, and business intelligence service.

Technology available for big data analysable
Big data is a particular sign of the large amount of data that grows functional and tremendously
over time. Big data technology can be characterized as a software tool for analysing, processing,
and extracting data from very complex and large datasets that traditional management tools
cannot handle.(Zhang, et.al., 2019)
DATA TECHNOLOGIES
Hadoop Ecosystem-
Hadoop Framework is designed to store and manipulate data in a distributed computing
environment using a simple programming model and store and analyse data that resides on a
variety of fast and low cost machines.
Artificial Intelligence-
A wide scope of computing technologies involved in the development of intelligent device that
can perform a mixture of tasks that typically require human intelligence.
NoSQL Database-
Various big data technologies in the database formed for the design of modern applications. It
shows a non-SQL or non-relational database that provides a way to collect and recover data.
R Programming-
One of the open source big data technologies and programming languages. This free software is
widely used in incorporate development environments such as statistical calculations,
visualizations, and Visual Studio Assist Communication.
How big data technology can help your business with suitable examples
Big data can help your business in five ways
Customer Acquisition And Retention-
Organizations need to have their own approach to selling their products. By investing big data,
companies can spot exactly what their customers are looking for. Build a solid customer base
from the beginning. The new big data process is observing consumer structure. Then use these
patterns for induction brand loyalty by grouping more data and identifying more trends and
opportunities to satisfy your customers.
Focused And Targeted Campaigns-
Big data can be used by companies to bring forward customized products to their target markets.
Forget about spending money on unsuccessful advertisement campaigns. Big data helps

businesses differentiate and analyse customer trends. This analysis typically includes observation
online purchases and monitoring POS transactions. These visual percept enable businesses to
create successful, focused and targeted campaigns that meet and exceed customer expectations
and increase brand loyalty.
Identification Of Potential Risks-
Today's businesses prosper in high-risk environments, which require risk management processes,
and big data is serving to develop new risk management solutions. Big data can help in improve
the effectiveness of risk management models and develop smarter plan of action.
Innovative Products-
Big data continues to help companies update their existing products while creating new ones. By
collecting large amounts of data, companies can distinguish what react to their customer base. If
a company wants to remain competitive in today's market, it can no longer trust its instincts.
Complex Supplier Networks-
Companies offer a network of suppliers with greater quality and insight, also known as the B2B
community. Supplier can avoid the boundary they normally face by applying big data analytics.
applying big data, suppliers purchase the higher levels of contextual intelligence they need to
succeed.(Zhou,et.al., 2018)
CONCLUSION
This report helps us in knowing about the data and big data these are created to the each other.
We also discussed about the various characteristics of the big data analysis which consist variety,
volume, velocity, and value and the various techniques or technologies used for the big data for
the organisation purpose to store them and utilise them when it is need and keep safe huge
amount of data. In last we discussed about the various big data technology which help the
business environment in various form and technology which help business in growth and future
development.

REFERENCES
Books and Journals
Adnan,et.al., 2020. Role and challenges of unstructured big data in healthcare. Data Management,
Analytics and Innovation, pp.301-323.
Gao, H., 2021. Big data development of tourism resources based on 5G network and internet of things
system. Microprocessors and Microsystems, 80, p.103567.
Shang, H., Lu, D. and Zhou, Q., 2021. Early warning of enterprise finance risk of big data mining in
internet of things based on fuzzy association rules. Neural Computing and Applications, 33(9),
pp.3901-3909.
Yue, et.al., 2018. Research and application of a big data-driven intelligent reservoir management
system. Journal of Coastal Research, (82 (10082)), pp.270-279.
Zhang, et.al., 2019. Orchestrating big data analytics capability for sustainability: A study of air pollution
management in China. Information & Management, p.103231.
Zhou, et.al., 2018. BEGIN: Big data enabled energy-efficient vehicular edge computing. IEEE
Communications Magazine, 56(12), pp.82-89.