Information Systems and Big Data Analysis: Challenges and Applications

This report provides a comprehensive overview of big data, defining it as complex and large datasets that require advanced processing techniques. It highlights the key features of big data, including volume, variety, and velocity, and discusses the challenges associated with its analysis, such as lack of understanding and difficulty in integrating data from various sources. The report also explores techniques for analyzing big data, such as A/B testing, data fusion, natural language processing, and statistics. Furthermore, it outlines how big data can benefit businesses through improved customer dialogue, product redevelopment, risk analysis, and data safety. The report concludes that big data is a valuable asset for organizations, enabling them to gain insights, improve performance, and make informed decisions.

Information Systems
and Big Data Analysis

Table of Contents
INTRODUCTION...........................................................................................................................3
TASK...............................................................................................................................................3
What is big data and its features?................................................................................................3
The challenges and techniques of big data..................................................................................4
How big data could help business?.............................................................................................5
CONCLUSION................................................................................................................................6
REFERENCES................................................................................................................................7
Books and Journals:....................................................................................................................7

INTRODUCTION
Advancement in technologies such as high access to bib volumes of data, massive data
has a bright and shining future ahead of it, as it enables companies to attain more insights, make
profits, increase performance and evolve more swiftly (Galetsi, Katsaliaki and Kumar, 2019).
Data as well as analytics, and AI technologies in businesses will be crucial in the quest to
anticipate, prepare for, along with react proactively and promptly to an international recession
and its influences. This report will define the meaning of big data, together with its
characteristics. Big data also involves some key challenges that are important to identified.
Techniques to analyse big data are also the part of this report. At the end, it will outline how the
big data could help business.
TASK
What is big data and its features?
Big data can be defined as the sets of data that too complex or large to be dealt by old
methods data processing application software. In simple words, big data is an aggregation of data
that is high in volume, still progressing exponentially with the time. It is also the data but with he
size (Ghani and et.al., 2019). Big data has following types:
 Structured: This is the data which can be stored, approached, together with processed in
the way of certain format.
 Unstructured: This is the data which is not known in kind or the structure is signified as
unstructured data. It possesses several challenges in terms of its processing for generating
value out of it.
 Semi-structured: It involves both kind of data i.e. structured and unstructured.
Some key examples of Big Data:
 Social media: The statistic exhibits that 500+terabytes of new data get consumed into the
base of data of social media Facebook on regular basis. It is the data that is basically
made in the manner of photo, video uploads, comments, message exchanges and many
more (Ghasemaghaei, 2020).
 New York Stock Exchange: It is another example of Big data that makes about one
terabyte of new trade data per day.
Characteristics of Big Data:

 Volume: Data size possesses a very important role in ascertaining the value out of data.
Moreover, whether a certain data can actually be ascertained as a Big Data or not, is
relied upon its size.
 Variety: Big data relates with the heterogeneous sources and the form of data, both
structured, unstructured as well as semi-structured. In former times, spreadsheets were
the only data source determined by most of the applications, but in current times, data can
be in the form of videos, emails, images, PDFs, monitoring devices and much more.
 Velocity: It relates with the speed of generation of data that means how fast the data is
created and processed to meet the requirements (Li and et.al., 2022). Hence, this feature
determines the actual potential of data.
The challenges and techniques of big data.
Challenges in massive data covers the best way of managing the multiple amount of data
that comprises the process of storing, assessing the crucial set of information on many data
stores. The following are some main challenges of big data analytics:
 Lack of proper understanding of massive data: Businesses miscarry in their big data
initiatives, all credit goes to the lack of understanding. Employees and staff members
might not understand what data is, its retention, processing, implication, along with
sources. Experts of data may understand what is occurring, but others might not have a
limpid understanding. For example, if staff members do not comprehend the value of
data storage, they could not be able to keep the data as back up.
 Integrating Data from a spread of sources: Data in a business comes from multiple
sources such as social media pages, customer logs, ERP applications, e-mails, financial
reports, presentations developed by employees. Integrating all such data to organise
reports may be a intriguing task. Integration of data is complicated for the reasoning,
reporting, along with business intelligence.
 Lack of knowledge professionals: For the purpose to operate advanced technologies
and massive data tools, organizations require skilled and competent data experts. These
professionals will cover data analysts, data scientists as well as data engineers. Hence,
one of the complexities is the lack of big data professionals in the company.
Techniques of Big Data:

 A/B testing: This data analysis technique encompasses differing a control group with a
range of test units, for the purpose to recognise what changes or transparent will enhance
a certain objective variable. McKinsey illustrates of analysing what text, copy, layout or
images will increase the conversion rates on a digital site.
 Data fusion and data integration: By segregating a collection of techniques that assess
and integrate from various sources as well as solutions, the vision are more potential and
efficient than if created through a single data source (Rialti and et.al., 2019).
 Natural language processing: Famed as sub-speciality of artificial intelligence,
computer science along with linguistics, this data analysis tool employ algorithms to
judge natural or human language.
 Statistics: It works to organise, collect and interpret the data, within the experiments or
surveys.
How big data could help business?
Big data in businesses, defines the a collection of all the tools and processes related to
exploiting as well as managing massive data sets. With the help of big data, many business
companies can utilise the figures and analytics, to know and serve the most valuable customers.
The following discussion is based upon how big data could support businesses:
 Dialogue with customers: These days, customers understand their priorities and are
smart enough to make any purchase by looking around different choices and options.
They even talk to companies through digital platforms and communicates specific
demands. Therefore, big data enables businesses to profile such consumers in a far-
reaching manner. This lets businesses to engage in an actual time, one on one
communication. It can be better understood with an example such as suppose a customer
entering into a bank and clerk can exploit big data to identify his or her profile in actual
time (Sahoo and et.al., 2019). The clear can identify about the customer's desires and
preferences. This enables him to advertise germane services and goods to the consumer.
 Re-develop products: It is one of the best manners to collect and utilise the feedback or
review. It supports businesses how their clients perceive about their offerings. Thus, the
business is able to make essential modifications and renovate their goods and services in
the best possible manner. When business assess unstructured social media text, it enables
to unfold the general feedback from the clients. The businesses can also distinguish the

feedback in multiple demographic groups, geographic locations and so on. For examples,
the businesses can assemble information about lead times, performance, material affect
costs and so forth. It lets businesses to raise the efficiency and productivity of multiple
productions.
 Perform risk analysis: Success depends on a multiple variables. It is not just about how
one can run their business. Social factors and economic factors plays a very important
role to determine the attainments (Thomas and et.al., 2019). Since big data leads to
prognostic analytics, it allows businesses to evaluates and scan social media feeds as well
as newspaper reports. Thus one can permanently keep up with the velocity on the present
trends and evolution in the sector.
 Data Safety: Big data tools allows one to make the whole data landscape across the
business. With such data, one keep maintain their confidential data safe. It allows to
assess all types of internal threats. It is moated in the suitable way and saved as per the
regulatory requirements. Due to this cause, most of the sectors have been paying
concentration on Big Data to make sure data protection, along with safety. It is even more
crucial in companies that deal with debit and credit information, financial information
and many more.
CONCLUSION
The conclusion is that big data refers as the massive or large data that is used by many
organizations. It is big in size or volume that helps businesses in numerous ways. The main
characteristics are integrated with volume, variety and velocity. It is seen that big data involves
many challenges and complexities around them such as lack of understanding, lack of
professionals or experts of big data and so forth. Techniques involving in big data are data
mining, a/b testing, statistics and many more. Lastly, it is seen that big data is supporting
businesses in number of manners, also they help in growing business.

REFERENCES
Books and Journals:
Galetsi, P., Katsaliaki, K. and Kumar, S., 2019. Values, challenges and future directions of big
data analytics in healthcare: A systematic review. Social science & medicine, 241,
p.112533.
Ghani, N.A. and et.al., 2019. Social media big data analytics: A survey. Computers in Human
Behavior, 101, pp.417-428.
Ghasemaghaei, M., 2020. The role of positive and negative valence factors on the impact of
bigness of data on big data analytics usage. International Journal of Information
Management, 50, pp.395-404.
Li, L. and et.al., 2022. Evaluating the impact of big data analytics usage on the decision-making
quality of organizations. Technological Forecasting and Social Change, 175, p.121355.
Rialti, R. and et.al., 2019. Big data analytics capabilities and performance: Evidence from a
moderated multi-mediation model. Technological Forecasting and Social Change, 149,
p.119781.
Sahoo, A.K. and et.al., 2019. Intelligence-based health recommendation system using big data
analytics. In Big data analytics for intelligent healthcare management (pp. 227-246).
Academic Press.
Thomas, J.J. and et.al., 2019. Deep learning techniques and optimization strategies in big data
analytics. IGI Global.