Big Data Analysis: Characteristics, Challenges, Techniques and Business Benefits

This report covers the concept of big data along with its characteristics and the difficulties in big data analytics. It discusses the techniques used for analyzing big data and the benefits it provides to businesses. The report also highlights the challenges and constraints related to big data analytics.

Information Systems
and Big Data Analysis
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Contents
Introduction................................................................................................................................2
Big data and its characteristics...................................................................................................2
Problems/Constraints/Challenges related to big data analytics.................................................3
Techniques available to analyze big data...................................................................................4
Big Data technology supporting businesses...............................................................................5
Poster..........................................................................................................................................6
References..................................................................................................................................7
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Introduction
The big data is combination large information, yet thriving with time and this can be
gathered from different ways such as social media, voluntarily, apps and purchase product etc. It
can be divided into two parts as structured data and unstructured data. Structured data is used and
managed by the company in spreadsheet and database. On the hand, the unstructured data is
unorganized information as it not has formatted data or predetermined model and this data
collected from social media that help to understand the consumer's need, the report will cover the
concept of big data along with characteristics and the difficulty in big data analytics. In addition to
this, discussion about the technique used for analyses the big data as well as the big data benefits
in company's business.
Big data and its characteristics
The big data can be explained as the collection, classification, presentation, analysis and
interpretation of enormous amount of data through digital means that involves collection of
customer data from various devices, online sources, etc. (Cohen and Macek, 2021). The big
data is used by the multinational companies as it can be help for measuring the consumer's
preference. The characteristics of big data divided into five segments
 Volume: The volume of Big data is very enormous and it is collected from various
sources like social media, online platforms, etc. For instance example of Facebook
can be brought into notice which is used by more than one fourth of the world and is
connecting people globally and it is used as a tool to collect data about like’s dislikes
of people.
 Variety: The variety can be explained as the mixture of various data that are
collected from various data sources. Variety includes structured data including
statistics, tabular data, etc. and semi structured data which includes TSV, SCV and
JSON. In additional to this, a raw form of data is unstructured data such as
unstructured files, images and audios etc. along with this, quasi-structured data refers
textual data as its required time and efforts.
 Value: The value is an important for big data as it is trustworthy data and reliable data
that necessary to store, analyze and process.
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 Veracity: The veracity refers to data reliability because it has different ways to
translate and filter data. The low veracity data can easily harm the business (Wiech
and et.al., 2022). This process able to make the crucial for business development.
 Velocity: The velocities improve the speed of data from different sources such as
mobile devices, network, and sensors etc. because these are created and collected on
real time basis.
Problems/Constraints/Challenges related to big data analytics
 Shortage of data: The organizations do not have much data for creating newer
interpretations because of shortage of data combinations to derive accurate results.
These are mainly affected by proper collection, classification, sorting, presentation
and analysis of data collected
 Lack of understanding of big data: The Company may face issues of properly
analyzing and interpreting data and this may reduce the clarity of data processed and
results obtained from it.
 Securing data: The organizations face challenges to secure the data from unwanted
threats such as hacking, data damage, and ransomware etc. so that company
investment huge amount in infrastructure for data management.
 Usage of bad quality in sourcing: The company has faced difficulty in maintain the
good quality of data source (Surnin and et.al., 2019). The employees within the
organization must be skilled enough to use big data and properly obtains benefits
from it.
 Messy data format: The employees face hardship in systemically maintain the data
because the data sizes are too large quantity to handle. It require high skill
professional to maintain the data systemically.
 Expensive maintenance: An organization require big data but for this they need huge
amount of finances and investment to bring and develop digital tools and technology
and create/acquire skills to manage and handle hem and help the existing employees
to adapt to the latest changes.
 Technical issue: The employees delay in storing and analyzing data as it may chance
of bad server issues and big data require good server network for data interpretation.
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 Excess pressure: The big data increase the overall exertion on the higher level
managers due to the increase in the complexity at the workplace. The firm requires
high skill risk manager who are able to incorporate the use of latest technology in the
business.
 Shortage of support: The analytics of big data needs support from the staff members at
all levels so that proper use of the big data can be mad in the organization. The
involvement of employees and staff from all the levels will integrate the collaboration and
management of big data (She and et.al., 2019).
Techniques available to analyze big data
 Association rule learning: This is first method used by supermarket to reveal the
interesting contact in different product. These techniques basically based on visitors
because they look into different online platforms and analyzing it is necessary for
obtaining new information and developing new data.
 Classification tree leaning: This technique is useful to discover those areas that
needs to be discovered and are new to look upon (Lin and Wei, 2020). It is helpful for
examining/evaluating to different segments and developing student's profiles those are
involved in digital classrooms.
 Genetic algorithms: These algorithms are created through evolutionary processes
that is, like methods that include heredity, genetic modification, and solution seeking.
These methods are useful for "transform" useful solutions or resolutions to problems
that require to be completely applied. These are helpful to restore the higher
performance and engineering processes require producing data for all on given
aspects.
 Machine learning: It contains software and tools that are mechanically automatized
for learning by them. It helps to understand the data and facts to the computers by
themselves and provide better experience and outputs on the time or requirement. In
the area of education the machine learning learns from the experiences of users from
vide array of areas that could help to distinguish between the preferences and
unwanted aspects of the user and provide best content for engagement.
 Regressing analysis: In the first phase, this form of analysis involves a few
independent variables to determine how they influence dependent variables. It
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explains that the overall prices of formal adjusters are higher costed and neutral
variables are of lower costs (Jha and et. al., 2016).
 Sentiment analysis: Sentimental analysis helps the researchers to determine what
does the speaker feels and what they are intending to write. Emotional assessments
are used to help improve service in the inn by analyzing guest words, customizing
profits and resources to address customer concerns honestly and determine what
customers think primarily based on critics on digital platforms and online sites.
 Social network analysis: It mainly includes the telecommunications that was later
changed to societal aspects where people look to have interconnectivity among them.
It is used to look into people and areas of their relations. Nodes build people in
society, as bonds represent relationships between people. It basically focuses on
implementing the use of trends to identify what is being used as the form of
interceptors and what does connects two people.
Big Data technology supporting businesses
 Big data reduces overall costs: performance is an important factor in reducing the
cost of small companies. With larger records, smaller organizations can get statistics
that should point out inefficiencies in their operations and, as a result, solve those
problems.
 Rise in revenue and sales: By the use of big data the smaller organizations gains a
broader understanding of customer trends and factors affecting their behavior
(Cockcroft and Russell, 2018). Social media platforms and major records have been
used altogether for identifying better records of customers form social media
preferences, likes and dislikes, such as statistical mining but compulsory on fb,
Twitter, Instagram, LinkedIn, and various social media platforms.
 Suitable pricing decisions: The pricing decisions of an organization largely impact
their success. The use of big data provides many benefits to them such as
understanding the areas in which investments can be made and how the total costs of
the organization can be lowered down.
 Higher Competitiveness: The use of big data boosts the chances of organization to
understand the changing trends more easily, cover larger market areas, identify
chances of growth and improve their customer experiences. The use of big data helps
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to improve overall sales by favoring the organization with enormous facts and figures
to attract more customers.
 Effective and efficient decision-making: The use of digital technology is a great
advantage because the companies can obtain higher competency and chances of
growth that encourages the growth of organization. Big data provides insights that are
highly helpful to interpret the opportunities and threats in the way of the organization.
Poster
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References
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.
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