Information Systems and Big Data Analysis: Characteristics, Challenges, and Techniques

This report discusses the characteristics, challenges, and techniques of big data analysis in information systems. It covers the four Vs of big data, challenges in big data analytics, and techniques such as data mining, machine learning, and statistics. It also explores how big data technology could support businesses with examples such as innovation based on data, flexible supply chain management, better customer insight, data management, and privacy.

Big data

Table of Contents
Introduction......................................................................................................................................3
What big data is and the characteristics of big data?...................................................................3
The challenges of big data analytics; and the techniques that are currently available to analysis
big data.........................................................................................................................................4
The techniques that are currently available to analyse big data..................................................6
How Big Data technology could support business, please use examples wherever necessary.. .6
Conclusion.......................................................................................................................................7
POSTER......................................................................................................................................8
REFERENCES................................................................................................................................9

Introduction
Big data technology is termed as the broader term of the data that relates to the diverse
consumers in the management. As it is important and vital division for the management to
manage their big data technology in terms of developing the orientation of structure. The
business of the management has to create an efficient functional software for maintaining the
functions and operations that are relates with the big data techniques (Cirillo, and Valencia,
2019). The following report involves the big data techniques and their aspects or characteristics,
several sort of the issues, techniques that are currently available to measure to examine big data.
And at last it involves the manner through which the big data technology that will help the
management as effectively and efficiently.
What big data is and the characteristics of big data?
Big data refers to large, complex, organized, and unstructured data collections that are generated
and sent in real time from a variety of sources. The three Vs of big data are made up of these
characteristics:
• The enormous amounts of data being stored are referred to as volume.
• Velocity refers to how quickly data streams must be processed and analysed.
• Data is acquired from a variety of sources and forms, including numbers, text, video,
photos, audio, and text.
When consumers use their mobile devices to open apps, search Google, or simply travel from
place to place, data is continually generated. Businesses and organisations must manage, store,
visualise, and analyse large amounts of useful data. Because conventional data tools aren't
designed to handle this degree of complexity and volume, a slew of big data software and
development solutions have sprung up to meet the need (Erraissi, Belangour, A. and Tragha,
2017). Data is crucial for gaining significant insights into the target audience and client
preferences. When properly analysed, these data sets can disclose a lot about behaviour,
characteristics, and life events. Organizations can use these results to improve their corporate
strategy and marketing activities in order to appeal to their target clients. Despite the fact that the
concept of big data and its utility has long existed, technology has only recently created an
opportunity to analyse large amounts of data rapidly and effectively. Every interaction with
technology, whether active or passive, produces new information that can be used to characterise

us. In the coming years, structured and non - structured research will be conducted and analysed
in order to find unexpected insights and maybe help tell the future. Big data will transform how
even the smallest organisations do business as data collection and interpretation become more
accessible (Hasan, Popp, and Oláh, 2020). New, outlay, and inventive technologies are
constantly emerging and growing, offering big data solutions very straightforward to implement
for any firm.
Characteristics of Big data
Specifications of Large data is a set of criteria that describe several approaches to big data
analysis and characteristics of big data are mentioned below –
The four v's of big data are volume, velocity, variety, and value. Big data refers to the massive
amounts of data generated every day from many sources such as human interactions, networks,
machines, corporate processes, social media platforms, and so on. Data warehouses store a lot of
information. After that, the characteristics of large data come to an end.
Velocity - In the same way that the sheer variety and volume of data organisations collect and
keep has evolved, so has the speed at which it is produced and managed. One of the most
common misconceptions regarding big data velocity is that it is one of the most essential
properties of big data. Big data comes in a variety of forms, including semi-structured,
unstructured, and structured data gathered from a variety of sources. Previously, data had to be
acquired from databases and spreadsheets; however, it comes in a number of formats, including
social media posts, audios, videos, photographs, PDFs, and emails, among others. One of the
most important characteristics of huge data is its diversity (Liu, 2018). Veracity of big data
refers to the reliability, correctness, and quality of the information gathered. Veracity isn't a
distinguishing feature of big data, all things considered. However, because of the high pace,
diversity, and volume, high reliability is critical if a company develops precise inferences from
data.
The challenges of big data analytics; and the techniques that are currently available to analysis
big data
Big data analytics that relates an efficient term of the sections in the management. It have
an effective and efficient terms, most of the business in the present period of time in utilising big
data techniques as effectively and efficiently. They management need to concentrates on big data

technique to capture and manage the consumer satisfaction for the period of an efficient section
of time. In the present framework, there are several sort sections that apprehensive to big data
that are maximising the consistent basis. As it is basic requirement for data analysis in respect to
maintain the consumer information in an effective and efficient manner. The basic fundamental
challenges in big data that are as follows –
 Data security - In the business of management the big data technology for the data
security is term to be very effective and efficient that plays a vital role (Lv, and Qiao,
2020). As it is essential in controlling and maintaining the data of their visitors are
consumers from the virus at the wider and particular term of the section first of the
personalized collection of the data from several sort of the division will get present the
orientation of manager in providing data security to the customer by which they will get
not have trust on loyalty issue in the future. In present manner of the division there are
several terms of the global techniques that operates various functions like malware and
spyware it will have a direct impact on the broader sources of data with high effective
data security.
 Data complexity – The term data complexity e-will relates to the managing and
maintaining of the big data techniques as it is essential for the investigator to overcome
the issues and problems in an effective manner. The source of data that should be get
analyse and examined will be presented to secure from the viruses or any fundamental
problems. The information consultants for experts in the business of management made
various records to mention the data into it for a longer period of time by which it can be
get saved and used at any time (Markl, 2019). To maintain the b complexity in the big
data technique it will be easy for the business of management to use those records in
future.
 Maintaining quality Data – By managing the qualitative set of the data in the division of
high field of big data analytic it will be difficult for the business of management to save it
from the viruses or from the issues or problems. As it will get supported by clearing and
managing the false error and mistakes in the field of the business from there data. As it
will be beneficial for them to sort all the problems and issues and save the source of the
data and maintain its quality in an effective and efficient manner.

The techniques that are currently available to analyse big data
 Data mining - Data mining is termed as an efficient term of the technique that is utilised
by the business of management to maintain their dealings with big data analytics. The
data mining will have an impact on white am of fields of the section that will have large
influence on the sources of data or information (Van der Aalst, and Damiani, 2017). It
will deals with the suitable and approachable terms that involves figures and machine
sections in the field of the management as effectively and efficiently.
 Machine learning – It will evaluate its several courses of section that maintain message
as it will have an impact on the artificial intelligence, robotics learning that will directly
linked with the framework and approaches of the computer science with suitable
schedules of the arrangements in the field of the area with effectiveness. Robotics
learning will also offers several fields and section that will rely on the mentoring
procedure of dealing.
 Statistics - The section of the big data technology will involves several courses of
procedure that maintain the Big data analytics in the manner of source of the information
that are generally depends on the diverse and expandable fields of the section. It develop
with particular and definite period of time that maintain the accuracy in an effective and
efficient manner. Technology in the big data covers the structure and fats and figure that
are important and plays an essential role full stop it also supports in maintaining an
effective terms that should be exist in the business of management and goods for market
orientation with effectiveness.
How Big Data technology could support business, please use examples wherever necessary.
Innovation based on data - It is not only an issue of inspiration when it comes to innovation.
Identifying subject areas that are promising for new attempts and experimentation takes a lot of
time and effort. Big data tools can help with R & D, which can lead to the creation of new
products and services (Wixom, and et.al., 2019). Data that has been cleansed, processed, and
governed for distribution can sometimes become a product in and of itself. For example, the
London Stock Exchange currently makes more money providing data and research than it does
trading equities.
Supply chain management that is flexible - Whether it's pandemic-related toilet paper
shortages, Brexit-related trade disruptions, or a ship trapped in the Suez Canal, modern supply

lines are unexpectedly vulnerable. Surprising, because we usually don't notice our supply
networks until they've been severely disrupted. Big data, which includes predictive analytics and
is often done in near real time, aids in keeping our worldwide network of demand, production,
and distribution running smoothly. Amazon is able to work in effective manner and maintain
supply chain management properly by using big data technologies.
Better customer insight - In an increasingly digitized marketplace, clickstream analysis of e-
commerce activity can offer insight on how customers travel through a company's many
webpages and menus to find products and services (Xu, and Duan, 2019). Companies can see
which goods users placed to their carts but later removed or abandoned without purchasing,
providing valuable insight into what customers might want to buy even if they don't make a
transaction.
Data management - Big data technology helps maintain business information that further
supports its development. Big data technology uses broader terminology software and
departments that can be effective for companies to manage large amounts of data. The data
managed by big data technology is available to the company for as long as it is valid. It helps
keep customers as effective as possible in their business. For example, Sainsbury is a UK-based
multinational retailer that uses big data technology to manage a wide range of segments of data
in a variety of ways.
Privacy - This is important for both market management and customers. By applying this
procedure to big data technology, it plays an important role in organizational development by
protecting customer data protection (Zhu, and et.al., 2018). Increase customer morale by
protecting the privacy of large amounts of information. For example, Tesco, a multinational
retailer that uses big data technology to maintain the sharing of personal information.
Conclusion
From the above mentioned report it has been concluded that, big data is termed as an efficient
and effective term of the approach that deals with the certain and particular term of the sections.
As it is helpful for the key investigators to maintain their facts and figure in the business of
management that will be highly effective and efficient in the business. The big data technique
that will support the investigator in maintaining the effective and efficient course of the data that
are utilising efficient methods.

POSTER

REFERENCES
Books and Journals
Cirillo, D. and Valencia, A., 2019. Big data analytics for personalized medicine. Current opinion
in biotechnology, 58, pp.161-167.
Erraissi, A., Belangour, A. and Tragha, A., 2017. Digging into Hadoop-based big data
architectures. International Journal of Computer Science Issues (IJCSI), 14(6), pp.52-
59.
Hasan, M.M., Popp, J. and Oláh, J., 2020. Current landscape and influence of big data on
finance. Journal of Big Data, 7(1), pp.1-17.
Liu, Y., 2018, January. Big data technology and its analysis of application in urban intelligent
transportation system. In 2018 International Conference on Intelligent Transportation,
Big Data & Smart City (ICITBS) (pp. 17-19). IEEE.
Lv, Z. and Qiao, L., 2020. Analysis of healthcare big data. Future Generation Computer
Systems, 109, pp.103-110.
Markl, V., 2019. Breaking the chains: On declarative data analysis and data independence in the
big data era. Proceedings of the VLDB Endowment, 7(13), pp.1730-1733.
Van der Aalst, W. and Damiani, E., 2017. Processes meet big data: Connecting data science with
process science. IEEE Transactions on Services Computing, 8(6), pp.810-819.
Wixom, B., and et.al., 2019. The current state of business intelligence in academia: The arrival
big data. Communications of the Association for information Systems, 34(1), p.1.
Xu, L.D. and Duan, L., 2019. Bigs data for cyber physical systems in industry 4.0: a
survey. Enterprise Information Systems, 13(2), pp.148-169.
Zhu, L., and et.al., 2018. Big data analytics in intelligent transportation systems: A survey. IEEE
Transactions on Intelligent Transportation Systems, 20(1), pp.383-398.