Information Systems and Big Data: Challenges, Techniques, and Business

This report provides an analysis of big data and its characteristics, highlighting its role within information systems. It discusses the challenges associated with big data analytics, including exponential growth, security concerns, and fake data generation, and explores various techniques such as A/B testing, data mining, and machine learning used to address these challenges. Furthermore, the report examines how big data technology supports businesses through artificial intelligence, customer relationship management, and risk management, citing examples like Walmart's use of data mining for personalized product recommendations. The conclusion emphasizes the importance of information systems and big data for improving operations, customer services, and marketing campaigns, enabling better decision-making and a deeper understanding of market trends and customer behavior. Desklib provides students access to similar solved assignments and past papers for comprehensive study support.

Information System
and Big Data Analysis

Table of Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
Big data and its characteristics...............................................................................................1
Challenges in big data and techniques that are available in big data analytical.....................2
Big data technology in support for business...........................................................................3
CONCLUSION................................................................................................................................5
REFERENCES ...............................................................................................................................6

INTRODUCTION
Data analysis is a procedure to examine sets of data to identify the trends and to draw a
conclusions regarding the information it may contain. Information system is an set of elements
that collects, stores and process the data in order to provide knowledge, information and digital
products. In this report will cover the analysis related to big data and characteristics of it. Further
there will be a discussion regarding the challenges of big data analytical and with this there will
be techniques discussed available analysis of big data. Lastly there will be examine of big data
technology that support business.
MAIN BODY
Big data and its characteristics
Big data analysis is an components of several information in huge quantity which cannot
be stored, processed or being analysed with traditional methods. Big data refers to a collection of
huge sets of data with these are several framework, techniques and tools involved. There are
various benefits of using big data analytical such as.
 It helps in making quick and better decisions- as organisation can access a huge
volume of data information and can analyse with huge variety of data that is being
sourced which will provide a new sights and helps in taking actions against it.
Companies can start this on small scale and handle data from history to real time.
 Cost reduction and operational efficiency- there is flexible data that is being processed
and stored using big data which will provide help to organisation to save their cost for
storing and analysing large amount of data. It will provide a base to identify the patterns
and insights which will help in identify efficiency of business.
 Provides an improved data-driven got to market- It helps in analysing the data from
different devices, videos, transactional application, social media, logs, web and sensors
that will empowers the organisation for data-driven. It will also provides customers
needs and potential risks which will help in making new products and services
accordingly.
The characteristics of Big data are mentioned below.
1. Variety- This refers to structured, unstructured and semi-structured data which is
collected through multiple sources. Variety has a types of data which differ in format,
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organised and process. As the traditional types of data are well structured and fit properly
in relational database. With the emergence of big data, the information data comes with
an unstructured and semi-structured types, which need an extra pre-processing for better
meaning and a support for metadata.
2. Velocity- This mostly refers to the speed of the data at which the data is being created in
real time. At a wide aspects velocity consist the rate of change, activity bursts and links
between incoming data sets with differ speeds. Velocity is the speed of data action and
receipt. As the highest velocity data is directly being written into memory rather than
written on disk. This majorly required on evaluation and on real time.
3. Volume- This refers to the size of data that is stored and generated in big data system.
The size of the information is in petabytes and exabytes range. For processing these
massive amounts of data it requires advanced technology which will be more powerful
than a typical storage units. These large amount of data are stored are generally stored in
warehouse of data.
Challenges in big data and techniques that are available in big data analytical
The challenges that might occur in big data are mentioned below.
 Exponential growth- the data centres and database stores massive amount of data that is
still increasingly growing. With the exponential growth of data, it will cause difficulty for
organisation to properly stored the data.
 Big data tools- there are various tools in big data and choosing the wrong one can cause
wastage of money, time and efforts.
 Securing- as organisation are mostly busy for understanding and analysing the data. They
generally leave the data security for last stage and being protected data become the breed
for hackers.
 Data security and protection- As the organisation is growing and they are adding new
tools and software for storing the data there is huge probability of lapse in security.
 Fake data generation- If the data is gathering from multiple sources it is possible to
gather the fake data which is invalid and might affect the analysis.
 Unsecured data sources- Collecting data from different channels which are not secured
affect the system and will be more vulnerable for external potential of malware.
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 Non-compliance to privacy laws- If there is no proper strategy that will ensure
compliance of data protection laws and if there is no tracking and standardising all
channels through which the data is collecting organisation will not be able to ensure that
the provided data is appropriate by the user.
There are various types of techniques that are available in Big data analytical which are
mentioned below.
 A/B testing- This data analytical techniques includes a comparing of control group with
various test groups. This will provide a base in concern wit the necessary changes that
will improve the variation that are occurring in the objectives.
 Data mining- It is a tool that is being used in big data analytical. It helps in extracting the
patterns from sets of large data through combining a statics and machine learning in the
database management.
 Machine learning- In the concept of artificial intelligence the machine learning is
essential part big data analytical. With the rising in computer science, machine learning
help is providing assumption based on the given data, works with the computer
algorithms. It also provides an prediction that is impossible for human to analysis.
 Data fusion and data integration- Through combining the different sets of techniques to
analysis and integrate from the multiple sources and solution, it helps in providing an
insight for the possibility to more efficiency and potential accurately rather than
developed by the single sources of data.
Big data technology in support for business
Enterprise have large volumes of data that is stored and analysed for further usage. There
are various usage of big data technology for business, which are mentioned below.
 Artificial intelligence and machine learning- It will provides sets of large data to
intelligence machine which allows a base to learn from a gathered information.
 Customers relationship management- There are more data that enterprise can
successfully access to analyse. As they can be more prepared to interact with customers
which will provide an ideal solutions.
 Risk management- When organisation have a large amount of data with multiple
business process. This allows to learn from the past mistakes and risks to plan the
mitigate future.
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There are various sectors in which big data analytical is useful, which are mentioned below.
 E-commerce
 Marketing
 Education
 Healthcare
 Media and entertainment
 Banking
 Telecommunication
 Government
There are several advantage of Big data such as.
 The big data analytical tool helps in predicting the outcomes accurately. Which allows
business and organisation to create a better decisions, with increasing the efficiency and
reducing the risks.
 Through gather data from social media platforms by big data analytical tools,
organisation around the world can stream their digital marketing strategies in order to
enhance their overall consumers experience.
 Big data analytical help enterprise to generate more sales leads that will boost the
revenue. As they can use big data tool in order to understand their products and services
in market with customers response.
 It provides a insight that helps in keeping the business a step ahead from their
competitors. Big data insights allows companies to learn the customers behaviour in order
to understand the trends and provides creating a personalised experience to the customers.
Few examples of usage of big data in business are mentioned below.
Walmart
It leverages big data and data mining to make personalised products for their customers.
With the help of big data technology Walmart identify patterns which shows frequent bought
products by the customers and most popular product of the company. With these insights
organisation creates an attractive and customized advice for individual customer. Through
implementing data mining technique effectively, the retail of the company has increased over the
conversion rates and has improved their customers sustainability. Walmart is using NoSQL and
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Hadoop technology which allows customers to access in real time data in order to collect from
different sources.
There are various others business who re using bog data analytical tin their operation
such as, American Express, General Electric, Uber, Netflix, IRS and Procter and Gamble.
CONCLUSION
From the above report it is concluded that information system and big data is important
for business and organisation in order to store and analysis thee data from different sources. As it
helps in improving the operation, customers services and marketing campaigns for the company.
After discussing the big data and their characteristics it allows company to operate more
effectively and helps in determining the cause of the problems. Big data helps organisation to
improve their decision making process across various sectors to predict the market trends,
customers behaviour and to interpret the natural world with the help of information system and
big data analytical.
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REFERENCES
Books and Journals
Choi, D. and et. al., 2021. Design and implementation of an academic expert system through big
data analysis. The Journal of Supercomputing, 77(7), pp.7854-7878.
Fedin, F.O. and et. al., 2019, September. Assessment of intelligent decision support systems
effectiveness in technological processes of big data processing. In 2019 International
Russian Automation Conference (RusAutoCon) (pp. 1-6). IEEE.
Li, K. and et. al., 2020, April. Security Management System Construction of Information System
Based on Big Data Analysis. In 2020 International Conference on Urban Engineering
and Management Science (ICUEMS) (pp. 546-549). IEEE.
Liang, T.P. and Liu, Y.H., 2018. Research landscape of business intelligence and big data
analytics: A bibliometrics study. Expert Systems with Applications, 111, pp.2-10.
Orozova, D. and Atanassov, K., 2019. Model of Big Data Map/Reduce Processing. Comptes
Rendus de l'Academie Bulgare des Sciences, 72(11), pp.1537-1545.
Pan, L., 2019. A Big Data-Based Data Mining Tool for Physical Education and Technical and
Tactical Analysis. International Journal of Emerging Technologies in Learning, 14(22).
Vialetto, G. and Noro, M., 2020. An innovative approach to design cogeneration systems based
on big data analysis and use of clustering methods. Energy Conversion and
Management, 214, p.112901.
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