Information Systems and Big Data Analysis

This report covers the concepts of big data along with the problems and characteristics in big data analytics. It comprises different techniques used for the analysis of big data. The report also explains how big data technology supports businesses in reducing costs, increasing revenue and sales, improving pricing decisions, and providing a competitive advantage. Subject: Information Systems, Course Code: NA, Course Name: NA, College/University: NA.

Information Systems
and Big Data Analysis

Table of Contents
INTRODUCTION...........................................................................................................................1
MAIN BODY...................................................................................................................................1
Big data and its characteristics...............................................................................................1
Challenge related to big data analytics...................................................................................2
Techniques available to analyze the big data.........................................................................3
Big Data Technology supporting Business............................................................................5
Poster......................................................................................................................................5
REFERENCES................................................................................................................................7

INTRODUCTION
The Big data is basically a combination of large data which could be collected from various
methods such as purchasing product, social media platforms and apps. The big data could be
divided into two parts such as unstructured data and structured data. In reference to structured
data, it is managed and used by the organization in the data base and spreadsheet. On the other
hand, when it comes to unstructured data, it is an unorganized data which is not present in
formatted manner. When it comes to data which is collected from social media that helps in
better understanding about the needs of the customers. In context to report, it covers the big data
concepts along with the problems and characteristics in big data analytics. In addition to that, it
basically comprises of the different techniques which are used for the analysis.
MAIN BODY
Big data and its characteristics
In reference to big data, it basically defined as the quantity of data which cannot be
collected through a data system which traditional. Moreover, big data is used by multinational
companies as it could support companies in helping preferences of the consumers.
Characteristics of big data are generally divided into the following five segments:
Volume: In context to volume of data, it basically refers to the data sets and their size that is
collected by a company (Cohen and Macek, 2021).
Variety: In reference variety, it basically comprises of all the unstructured and structured data
which has been generated with the help of machines and humans.
Value: In context to value in big data, it basically refers to the usefulness of gathering of the data
for the company.
1

Veracity: In case of veracity, it basically refers to data reliability as it involves different ways
to translate and filter data. In reference to such data, it can result in harming organizations. The
process supports in carrying out crucial decisions for the purpose of development.
Velocity: In relation to velocities, it basically helps in improving data speed from various
sources including social media platform, mobile devices, sensors & network etc as all these are
gathered from real time. (Wiech and et.al., 2022).
Challenges of big data analytics
Insufficient data : In organizations, it do not have sufficient data in order to create
apprehension which could result in causing lack of organizing and data combination and
making employees confused when it comes to selection from various options relating to sources.
Inability to understand data: In reference to the organization, it may face issues and problems
when it could have clear understanding of storing, analysing and processing of data.
Securing data: In case of organizations, the company faces challenges and various problems for
the purpose to protect data from discarded threats including data damage, hacking or ransom
ware etc. So that the organization assets huge amount managing the data and its infrastructure
(Surnin and et.al., 2019).
Usage of bad quality in sourcing: In context to organizations, it becomes very difficult in
maintaining the data source and its good quality. In reference to employees, it requires the bang-
up knowledge in relation to big data analytics.
Messy data format: In context to workers, they faces hard knocks when it comes to maintenance
of systematic data because the data size are too large quantity to handle. In addition to that it
requires high skill professional for objective to establish a systematic way.
2

Expensive Maintenance: In context to the companies, it requires immense sum of money
investment as if they have not modify technology. In case of technologies, it supports in analysis
of data and also storage of data in an easy way.
Technical issue : In context to employees, it can postponement in analysing or storing data as it
might lead to bad server issue or also it requires good server network in order to process the data
(She and et.al., 2019).
Excess Pressure: In case of big data, it increases the force on top level manager as it results in
increasing the complexity of work. In context to organization, it involves high skill risk manager
to carry out storing & processing activity in relation to the big data analytics.
Lack of support: In context to big data analytics, it requires assistance from top level to bottom
level as the risk managers generally supports in dealing with data processing, storing and
analysing the data.
Techniques available to analyze the big data
 Classification tree leaning: It is defined as the method used to identify various categories
generally lie in new observation. It basically supports in assignment of documents to
different categories & developing the profiles of student who take class through internet.
 Machine Learning: In machine learning, it basically involves tools and software in form
of coding that learns from records. In addition to that, it supports the computers to read
without having a explicit plan. It is basically taken into consideration for the objective to
distinguish between unsolicited emails and messages.
 Association rule learning: In reference to this method, it is basically used by
supermarket in order to reveal contact in different commodities. Such techniques are
basically based upon on the website visits (Lin and Wei, 2020).
3

 Genetic algorithms: In reference to such process, it is basically promoted by the
evolutionary process which comprises of the genetic modifications, heredity and solution
seeking. In reference to this method, it is very helpful for the objective to response to
issues which are need to be fully implemented.
 Regressing analysis: In this phase, the analysis of the regression basically involves few
independent variables in order to determine how they influence the dependent variables.
It also involves the cost of the different formal adjustments where neutral variables are
high.
 Sentiment analysis: The sentimental analysis allows the researchers for the purpose to
determine the writers or speakers feelings through recommendation of a topic. In
reference to emotional assessments, it is basically used for the objective to improve
services with the help of customizing profits and analysing words in order to code
different customers concerns in honest manner and determining what customers think
primarily based on the criticism from different social media (Jha and et.al., 2016).
 Social network analysis: In social screening, it is generally the way which could be used
for the telecommunications sectors. Also it is used for investigating relations among the
individuals in different areas and commercial games.
Big Data Technology supporting Business
Reduces overall costs: It is considered to be a essential factor in relation to reducing small
organizations and their costs. With relation to larger records, the small organizations could get
statistics for the purpose to identify the various inefficiencies in business operations.
Increases revenue and sales: In big data, it generally allows companies to have a better
understanding about beliefs and buying alternatives from their customers. In reference to
different social media platforms and major records, it has teamed up in order to create a problem
with social media mining.
4

Helps in improving pricing decisions: In company, charges of the services and goods could
result in impacting even if they are successful or not. In context to big data, its helpful to
determine prices of the competitors as compared to the competitiveness (Cockcroft and Russell,
2018).
Provides a competitive advantage: In reference to big data, it offers an opportunity for the
company to realise nearby prospects. In addition to that it helps in providing function of
speculation through bringing up the local market closer and also offering the impression of
buying behaviour.
Boosts efficiency in decision-making: In relation to digital tools, it uses digital client
simulations. The equipments and various mathematical techniques could support the company
to mine its social media sites (Rodrigues Jr and et.al., 2016).
Poster
5

6

REFERENCES
Books and Journals
Cohen, S. and Macek, J., 2021. Cyber-Physical Process Monitoring Systems, Real-Time Big
Data Analytics, and Industrial Artificial Intelligence in Sustainable Smart
Manufacturing. Economics, Management & Financial Markets, 16(3).
Wiech, M. and et.al., 2022. Implementation of big data analytics and Manufacturing
Execution Systems: an empirical analysis in German-speaking countries. Production
Planning & Control, 33(2-3), pp.261-276.
Surnin and et.al., 2019. Industrial application of big data services in digital economy.
In CEUR Workshop Proceedings (Vol. 2416, pp. 409-416).
She and et.al., 2019. Importance of small probability events in big data: Information
measures, applications, and challenges. IEEE Access, 7, pp.100363-100382.
Lin, Q. and Wei, W., 2020, June. Design and Research of Intelligent All-area-advancing
Tourism Cloud Platform in the Era of Big Data. In Journal of Physics: Conference
Series (Vol. 1575, No. 1, p. 012145). IOP Publishing.
Jha and et.al., 2016. A review on the study and analysis of big data using data mining
techniques. International Journal of Latest Trends in Engineering and Technology
(IJLTET), 6(3), pp.94-102.
Cockcroft, S. and Russell, M., 2018. Big data opportunities for accounting and finance
practice and research. Australian Accounting Review, 28(3), pp.323-333.
Rodrigues Jr and et.al., 2016. On the convergence of nanotechnology and Big Data analysis
for computer-aided diagnosis. Nanomedicine, 11(8), pp.959-982.
7

8