Exploring Information Systems and Big Data Analytics for Business

This report provides a detailed analysis of big data within information systems, exploring its characteristics, including variety, velocity, and volume, and differentiating between structured, semi-structured, and unstructured data. It addresses the challenges of big data analytics, such as lack of understanding, tool selection confusion, and data security, while also examining techniques like data fusion, machine learning, and A/B testing. Furthermore, the report discusses how big data technology supports business through data management, employee engagement, and data privacy. The conclusion emphasizes the importance of big data analysis for businesses to protect their data, maintain productivity, and adapt to changing environments, particularly in the IT and finance industries, highlighting the need for effective data processing and analysis to gain valuable insights and benefits.

Information Systems and
Big Data Analysis

Contents
Contents...........................................................................................................................................2
INTRODUCTION...........................................................................................................................1
What big data is and the characteristics of big data.........................................................................1
Characteristics of Big Data:.........................................................................................................2
The challenges of big data analytics................................................................................................2
How Big Data technology could support business, an explanation with examples....................4
CONCLUSION................................................................................................................................4
REFERENCES................................................................................................................................5

INTRODUCTION
Big data means collection of number of information that is connected to large business and
growing it with time. It has seen that keeping large data for long period is complex task for
industries where it became important to use software systems that could be used to managing
tool and keeping data stored (Ghasemaghaei, and Calic, 2019). The current project report is
based on big data and its analysis that supports to develop understandings and maintain good
activities. The report involves techniques that used in collecting data and ways in which data has
been used.
What big data is and the characteristics of big data
Big data is the collection of information in huge volume which is growing continuously
with time. In the business environment it is important to have good analysis of data and
information that is used to develop the business productivity and profitability. Big data is defined
as field of collecting data from different ways with the help of software that can help to manage
each functions and activities. In other words, big data refers to massive data which is connected
with organization and helps to develop the understandings and managing data in synchronized
form that supports to develop business activities (Ghasemaghaei, 2020).
Types of big data
In business environment, there are different types of data which is used to collect, is
defined below:
Structured – This can be explained as big data in structure form that can be processed,
retrieved, and stored in a fixed format. This can help to manage each data and information in
effective form that support to develop business productivity. For example, to keep records of
employees at the workplace it should be structured properly and increase performance (Grover,.
and Kar, 2017).
Unstructured – This means data which is not in structure format and lacks any specific
information that can create challenge for organization to maintain information. This makes very
complex and time consuming process to analyse unstructured data. For example, E-mail
Semi-structured – This is third type of data which is in high volume and states data
containing both formats that mentioned above, is known as unstructured data. In other words, it
contains vital information that segregate individual element within data.
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For each organization it is important to have control over data and information that is
huge in number and supports to develop business productivity (Gupta, and Rani, 2019).
Characteristics of Big Data:
Big data is the involvement of various information that connected with data analysis and
managing the performance. This became challenging for an industry to collect data and keep it
right format as it is time consuming process. The characteristics of Big data is defined below:
Variety – This refers to structured, semi structured, and unstructured data that is collected
from multiple sources. It has seen that data could only be collected from spreadsheets and
spreadsheet in past period that have been consumed more time. In now days, data comes in the
form of emails, photos, videos, audios, PDFs and more that are used in best quality and develop
understanding to manage time. If information is in good variety then it would be good
opportunity to grow arrange all business activities (Manogaran, and Lopez, 2017).
Velocity – The velocity of big data is connected with speed where data is being created in
real time. In broader prospects, velocity comprises rate of change, linking of incoming data sets
at varying speeds and managing data properly. This directly connected with data in relation to
managing all information effectively. The velocity of data showing how quickly that data is
arriving and stored, that associates retrieval rates that can help to manage business activities
(Shah, Steyerberg, and Kent, 2018).
Volume of data – This is different traits of big data that indicates larger volume of data, is
generated on daily basis from different sources like business process, social media platform,
business processes, networks, and human interactions. For instance, the warehouse is used by
researcher to collect data from various resources that could help to maintain higher performance.
Volume is one of characteristic of big data as it indicate data in huge volume which can be
generated from different source on daily basis like business process, social media platform,
human interaction, machine work and many more. These large numbers of data can be stored in
warehouses.
The challenges of big data analytics
Big data analysis is a process of analysing the data in relation to an industry and working
place that could supports to develop business activities. If data is not properly collected or
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managed, then it will be challenging for industry to regulate its business. There are different
challenged that might be occurs while running the business is defined below:
Lack of understanding of Big Data – Having big data and various information can
create challenge for individual to understand it and work accordingly. Due to lack of
understanding, sometimes organization fails to initiates or read their data. For example, Tesco is
larger organization where records of all products, services and financial transaction, and
employees is required to keep that could be massive. In case employees are not enough
knowledgeable then it became challenging to keep backup of sensitive data. Thus, this challenge
is faced by companies while running their business and managing data (Surbakti, Wang,
Indulska, and Sadiq, 2020).
To solve this issue, there is need to have professional and having knowledge of software
which used to gather informational and grow it exponentially.
Confusion of Big data tool selection – The organization get confused in selecting the
simplest tool for giant data analysis and storage that affected the business productivity. Number
of large companies are running their business where it became challenging to select best tool as it
affects business performance.
To solve this issue, there is need to hire experienced professionals who have good
knowledge about tools and can use properly. The consultant is also requiring to provide
recommendation of using simple tools that could support company scenario.
Securing data – The aim of business organization and individual is that their data will be
secure and safe as it supports to running the business activities. Most of the business are engaged
in storing, understanding and checking the data set that might be create challenge in data
security. The collected data can be loss due to mistake, theft and leak by another person in
unauthorised form that create problems for an industry.
To solve this issue, management should recruit more cybersecurity professionals to guard
their data. For securing data there is need to use data encryption, data segregation and control
implementation that could help to develop business activities.
The techniques that are currently available to analyse big data
Data Fusion – This shows data understanding is potentially accurate that combined set of
fusion techniques to analyses and integrating data from sources and manage it properly.
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Machine learning – This technique is related to analysing data by using artificial
intelligence that can help to manage business activities. The professional use this technique by
emerging in computer science and algorithms work that supports to work accordingly.
A/B testing – This technique is used to comparing information from variety of test
groups and allocate information in to groups that can help to bring improvements and supports to
attain objectives.
How Big Data technology could support business, an explanation with examples
To manage business and information there are different factors that used to develop
business:
Management of Data – Data management is the important task which is done with the
help of software system. The individual should use MIS, office, excel, and cloud to keep records
of all transactions and employees that can help to grow a business continuously.
Employee engagement – Employee engagement is the important task which used by
organization for the purpose of growing their business by managing all activities and functions.
To develop a business each organization should engage all employees in their working that helps
to build trust and develop performance (Taleb, Serhani, and Dssouli, 2018).
Privacy of data – The big data technology can be used by organization for data privacy
and maintaining all information collectively. By keeping data privately an organization will grow
their business continuously and maintain loyalty at the workplace.
CONCLUSION
From the above report, it can be concluded that Big data is one of the important
technology that used by companies to protect their data as well as information in right format.
Information and big data analysis is the requirement of each business industry as it helps to keep
records and maintain higher productivity. This is important for each organization to understand
how their sales and productivity will increase and how it contain large data in short form. As
environment is changing where business organizations are also increasing their activities by
using new technology and activities that can help to develop the organizational productivity. In
IT and finance industry it became challenging for organizations to manage their big data and
keep proper records that can help to develop the business activities and performance. Big data
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refers to complex and large data sets that needs to be processed and analysed to discover
valuable information that could benefit organization and businesses.
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REFERENCES
Books and journal
Ghasemaghaei, M. and Calic, G., 2019. Can big data improve firm decision quality? The role of
data quality and data diagnosticity. Decision Support Systems. 120. pp.38-49.
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.
Grover, P. and Kar, A. K., 2017. Big data analytics: A review on theoretical contributions and
tools used in literature. Global Journal of Flexible Systems Management, 18(3), pp.203-
229.
Gupta, D. and Rani, R., 2019. A study of big data evolution and research challenges. Journal of
Information Science. 45(3). pp.322-340.
Manogaran, G. and Lopez, D., 2017. A survey of big data architectures and machine learning
algorithms in healthcare. International Journal of Biomedical Engineering and
Technology, 25(2-4), pp.182-211.
Shah, N. D., Steyerberg, E. W. and Kent, D. M., 2018. Big data and predictive analytics:
recalibrating expectations. Jama, 320(1), pp.27-28.
Surbakti, F. P. S., Wang, W., Indulska, M. and Sadiq, S., 2020. Factors influencing effective use
of big data: A research framework. Information & Management, 57(1), p.103146.
Taleb, I., Serhani, M. A. and Dssouli, R., 2018, July. Big data quality: A survey. In 2018 IEEE
International Congress on Big Data (BigData Congress) (pp. 166-173). IEEE.
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