Information Systems and Big Data Analysis: Challenges and Solutions

This report provides a detailed overview of big data, its characteristics, and the challenges faced in big data analytics. It explores various techniques currently available for analyzing big data, including A/B testing, data fusion, data integration, data mining, machine learning, and natural language processing (NLP). The report also discusses how big data technology can assist enterprises by reducing costs, increasing sales and revenue, improving pricing decisions, providing a competitive advantage, and increasing efficiency in decision-making. It emphasizes the importance of data analysts in managing and interpreting the vast amounts of data produced every minute, highlighting the role of big data analytics in modern organizations.

Information
Systems and Big
Data Analysis

Table of Contents
INTRODUCTION...........................................................................................................................3
TASK...............................................................................................................................................3
Define big data and its characteristics.....................................................................................3
Evaluating challenges faced by big data analytics.................................................................4
All the techniques that are currently available to analysis the big data...............................5
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7
Appendix..........................................................................................................................................8

INTRODUCTION
Big data involves data management, which are sets of large and complex traditional processing
applications that are not suitable for capturing, managing, managing and also processing data in a
reasonable time. Big data can be used for predictive analytics and user behavior analytics. This
report covers what big data is and the characteristics of big data. Also, what are the challenges of
big data analysis and also the techniques currently available for big data analysis. It lies in how
big data technology can support business. These are the topics covered in this
report(KumarGuha, 2018).
TASK
Define big data and its characteristics
Big data is a combination of structured, semi-structured and unstructured data that is collected by
organizations that can be targeted for information that can be used in machine learning projects,
predictive modelling and other and more advanced analytics applications (Faccia and et.al 2019).
Big data is collected from many different sources and is more often described by five
characteristics:-
 Volume:- The size and quantity of big data that companies manage and analyze. High
speed is important for companies to carry out any big data process. It consists of the
speed of switching activities and linking incoming data files
 value:- The most fundamental “V” from a business perspective is the value of big data
which usually comes from discovering insights and also pattern recognition which also
leads to more efficient operations which leads to more efficient operations as well as
stronger customer relationship and other clear and qualified business benefits.
 Variety: There is a variety and rage of different data types which include unstructured
data semi-structured data and also raw data. This is one of the big problems facing the big
data industry as it directly affects the performance of the company. It is vital for the
industry to properly manage the diversity of data by organizing it. Diversity is another
type of data that is usually collected from different kinds of sources
 velocity:- The velocity at which companies receive data for storage and management.
Companies measure data volume in gigabytes, zettabytes, and yottabytes

 Veracity:- This is the “truth” and accuracy of data and information that often helps
determine the level of confidence of executives. It is among the most important properties
of big data, as low veracity can greatly harm the accuracy of industry results
(Chinnaswamy et al. 2018).
 Variability: The changing nature of data companies capture, manage and analyze.
Evaluating challenges faced by big data analytics.
In this digitizing world, we produce huge amounts of data every minute. While such vast
amounts of data are generated every minute and their storage and management use and analysis,
even large business firms are trying to find ways to make the data useful, simply storing this vast
amount of data will not be everything. this is useful, so that's why organizations are looking for
options like big data analytics tools in big lake that can help the organization handle big data.
These are some of the challenges big data analytics has to face:-
 The need for synchronization across disparate data sources – As data sets become
larger and more diverse, incorporating them into an analytics platform is a very
challenging task, if these things are overlooked, then it creates gaps and leads to incorrect
reporting and insights.
 Acute shortage of professionals who understand big data analytics – Data analytics is
very important to create or produce large amount of data every minute. with such a large
increase in data, there are many requirements for data scientists. Another major problem
facing organizations is the lack of professionals who can understand big data analytics,
there is also a severe shortage of data scientists compared to the huge amount of data
being produced (Lee,,Huh2019).
 Gaining Meaningful Insights Using Big Data Analytics – It is essential for a business
organization to get insights from big data analytics and it is also very important that only
the relevant department has access to this information. A major challenge facing big data
analytics companies is effectively addressing this large gap.
 Data security and privacy – Once a business finds out how to use big data, it brings a
wide range of possibilities and opportunities. However, this also includes the potential
risk associated with big data regarding data privacy and security. Big data tools used for
analysis and storage use different data sources, which always leads to a high risk of data

exposure, making it vulnerable, so the increase in voluminous data increases privacy as
well as security concerns(Kunanets, VasiutaBoiko2019).
All the techniques that are currently available to analysis the big data.
There are 5 big data analytics techniques:-
 A/B testing: involves comparing a control group with different test groups to discern
which treatment or changes will improve a given target variable. Again, big data fits into
this model because it can test huge numbers, but this can only be achieved if the group is
large enough to get a meaningful difference.
 Data fusion and data integration: By combining a set of techniques that analyze and
integrate data from multiple sources and solutions, insights are more efficient and
potentially more accurate than if they were developed from a single data source.
 Data mining: Common tools used in big data analysis data mining extracts patterns from
large data sets by combining statistical and machine learning methods. As part of
database management. This would occur when customer data is mined to determine
which segments are most likely to respond to an offer.
 Machine learning:- well known in artificial intelligence machine learning is also used
for data analysis. It is based on computer science, works with computer algorithms and
makes assumptions based on data. with
 Natural Language Processing (NLP):- known as a subspecialist of computer science,
artificial intelligence and linguist, this data analysis tool uses algorithms to analyze
human natural language.
How Big Data technology could assist an enterprise.
The importance and value of big data should not be overlooked by a business of any size. Big
Data:
Reduce overall costs
Efficiency is the key to reducing costs for small businesses with big data, small businesses can
gain the information they need to identify inefficiencies in their operations and subsequently. For
example, data may reveal that customers are not interested in purchasing Gift Wrap at checkout,
which may cause a small business to remove the offer and stop stocking Gift Wrap (Lv2021).
Increases sales and revenue

Big data allows small businesses to gain significant insight into their customers' opinions and
purchasing preferences. With this information, a business can understand how to tailor its
products and services to give customers exactly what they want, leading to more sales.
Improving pricing decisions
The ways small businesses price their products and services can have a significant impact on
how successful they are. Data tools can help evaluate corporate finances and see how
organizations fare compared to their competitors
It provides a competitive advantage
Big data opens up opportunities for small businesses to target local customer preferences. Data
tools take the guesswork out of approaching and providing insight into your local market
on purchasing behaviour. Once small businesses have a solid understanding of how their
customers think and their likes and dislikes, they can add a personal touch and enjoy a
competitive advantage.
Increase efficiency in decision-making
Social media and big data have come together to create the process and need for social media
mining, which is like data mining but limited to the social media platform. Social media mining
refers to the collection and analysis of data about social media users to make inferences about
purchasing behaviour, interests, response to offers and promotions, and more.
CONCLUSION
The above report contains information about what is big data and also what are all the
characteristics of big data. It also has issues facing big data analytics and techniques currently
available for big data analytics and how big data technology could support business with
examples. In this report there is information about how an organization can have big data
analytics and because there is a lot of data that is produced every minute it is really difficult for
an organization to store and manage the data and that is why there are requirements for data
analysts(Tao2020).

REFERENCES
Books and Journals
Guha, S. and Kumar, S., 2018. Emergence of big data research in operations management,
information systems, and healthcare: Past contributions and future roadmap. Production
and Operations Management, 27(9), pp.1724-1735.
Faccia, Aand et.al 2019, August. Accounting information systems and ERP in the UAE: an
assessment of the current and future challenges to handle big data. In Proceedings of the
2019 3rd International Conference on Cloud and Big Data Computing (pp. 90-94).
Chinnaswamy, A., and et.al 2018. Big data visualisation, geographic information systems and
decision making in healthcare management. Management Decision.
Lee, S. and Huh, J.H., 2019. An effective security measures for nuclear power plant using big
data analysis approach. The Journal of Supercomputing, 75(8), pp.4267-4294.
Kunanets, N., Vasiuta, O. and Boiko, N., 2019, September. Advanced technologies of big data
research in distributed information systems. In 2019 IEEE 14th International
Conference on Computer Sciences and Information Technologies (CSIT) (Vol. 3, pp.
71-76). IEEE.
Lv, X. and Li, M., 2021. Application and research of the intelligent management system based
on internet of things technology in the era of big data. Mobile Information
Systems, 2021.
Tao, D., Yang, P. and Feng, H., 2020. Utilization of text mining as a big data analysis tool for
food science and nutrition. Comprehensive reviews in food science and food
safety, 19(2), pp.875-894.

Appendix