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

This report discusses big data and the features of big data analytics, as well as the problems and tools required for data analysis. It covers the characteristics of big data, the challenges of big data analytics, and the techniques currently available to analyze big data. Additionally, it explains how big data technology can support businesses with examples.

Information Systems and
Big Data Analysis

Contents
INTRODUCTION...........................................................................................................................................3
TASK............................................................................................................................................................3
What big data is and the characteristics of big data................................................................................3
Characteristics of big data.......................................................................................................................3
The challenges of big data analytics........................................................................................................4
The techniques that are currently available to analyse big data.............................................................5
How Big Data technology could support business, an explanation with examples.................................5
CONCLUSION...............................................................................................................................................6
REFERENCES................................................................................................................................................7

INTRODUCTION
In order to compete effectively edge, a company organisation must control its activities.
Throughout this information management, big data analytics plays a vital role in handling
enormous sets of data, allowing business organizations to manage complicated and massive data
sets effectively (Adams and Krulicky, 2021). Big Data analytics and information systems play a
key role in helping companies to employ sophisticated analytics approaches to create strategic
business decisions, analyses consumption behavior, enhance corporate operations, supply
smarter goods and services, and produce more amount of income. The report discusses big data
and the features of big data analytics, as well as the problems and tools required for data
analysis. The following study discusses big data approaches and their features, numerous
obstacles, ways that are now available to evaluate huge data, and how big data technology will
benefit businesses.
TASK
What big data is and the characteristics of big data
Big data approaches are having an influence on the computing technologies sector in
today's competitive environment. Hadoop, Spark, Hive, and Cloud are examples of big data
technologies. This also software applications that assist the firm in handling the large data
involved in the business. This encompasses the layout of many aspects that preserve information
and data preservation that are effective for corporate growth by generating big data
synchronization. There really are two types of big data technology: workable big data and
analysis big data. It is primarily the responsibility of the company's IT department to assess and
analyses big data needed to make company functions. Big data also aids in raising the motivation
to carry out the activities required for the organisation to create big data approaches (Bologa,
Bologa, and Florea, 2019). Big data technologies focus on the fundamental categories that are
required for tracking the critical data that will relate to the client. By big data technologies, the
company may achieve financial performance. It serves an important function by effectively
displaying a bigger term of portions.

Characteristics of big data
The company must determine the following terms of factors that are specifically linked to big
data innovation in the context of region:
• The volumes of information - It is a broader division, and management must create effective
methods for managing data partitions. Big data volume is substantial, and there are a big number
of customers in the worldwide and national professional areas. By maintain significant number
of data that is required for administration to handle company tasks in order to obtain smart and
confident staff.
• Data variety - It is enormous in the area of big data technology and must be kept effectively
(Demirkan, and Delen, 2017). It includes the data application's segmentation. The direction of
field requirements will alter to ensure the kind of information. Administration is responsible for a
variety of tasks, including financial transactions and the development of strategic strategy.
• Data velocity - This will be studied how fast information may be moved from one department
to another department. In this segment, the pace of information is also handled by developing
wide terms of software such as HADOP HPCC in the industry. It is a necessary action for
administration to regulate the velocity of data in order to keep performance over a lengthy span
of time.
• Veracity - It refers to inconsistencies and uncertainty in information, which implies that the
existing information would become untidy at times, and the accuracy and accuracy are hard to
regulate.
• Value - The information itself has no significance or purpose, but it should be turned into
anything useful in order to retrieve data.
The challenges of big data analytics
Big data analytics will be regarded as an efficient business department. It has lasting
implications, and most administration in the current period uses big data technology (Erraissi,
Belangour, and Tragha, 2017). To maintain consumer happiness over a longer length of time,
businesses must focus on big data. In the present system, there are several categories of
difficulties related to big data that are expanding on a regular basis. As it is a fundamental

requirement for data collection in order to handle consumer data. The following are the basic
issues in big data:
• Data security - Data security is regarded as a fundamental concern in big data analytics
administration. It is critical in directing and maintaining it over a wide range of divisions (Liu,
2018). The designated person will collect data from various parts and present basic duties in
providing data protection to their clients. In the current worldwide technology scene, there are
several activities like malwarebytes that have a powerful effect on the larger data.
• Data complexity - This refers to the upkeep of the big information complexity that is required
for the investigator to solve the concerns and difficulties. The data can be examined and fixed in
oriented divisions, making it difficult for some people to handle the source of components.
Because of its fundamental portions of working with It experts in the company to handle the big
data complexities, this will be related to the basic principle concerns.
• Managing Quality Data - Handling the huge portion of big data will make it difficult for the
company to be stored correctly. It entails removing identical defects, mistakes, and blunders,
among several other things.
The techniques that are currently available to analyse big data
• Data mining - Data mining is a powerful tool that businesses employ to manage massive data
analysis. Data mining has an impact on numerous broader terms in the datasheet. It is concerned
with the appropriate ways using numbers and computer vision in the study of management.
• Machine learning - This takes into account numerous terms of the communication that will
have an influence on AI, automation understanding, which is strongly attributable to the structure
of computer engineering, and computational arrangement (Markl, 2019). Robotics learning
additionally provides a variety of terminology depending on diverse transactions.
• Statistics - The technology relates to the process, management, and interpretation of several
types of information that are essentially distinct and expanding through time. Big data
technology describes the path and statistics that are important. This aids in the management of
efficient sections that may display the organization, goods, and marketing capability.

How Big Data technology could support business, an explanation with examples
In today's competitive climate, big data technology assists businesses in developing through
offering management reports about their faithful customers. Several organisations can get an
edge over its competitors by utilising big data technologies. The following businesses will
benefit from big data technologies in terms of emerging market:
• Data management - Big data techniques aid in the preservation of corporate information, which
aids in their growth. Big data technology employs a broader definition of tools and categories
that may be useful for companies to market a larger volume of data (Van der Aalst, and Damiani,
2017). The data managed by big data technologies will be important to businesses for an
extended length of time. It also aids in efficiently keeping customers in the firm. For example,
Sainsbury's is a global UK-based retail company that uses big data technology to manage larger
portions of knowledge in several courses.
• Data privacy is vital for both owners and employee in the market. Through utilizing the
biochemical pathway in big data approach, it plays a prominent role in organizational growth by
preserving the privacy of client information. This will boost client morale by preserving the
confidentiality of a broader set of data. For example, Tesco, a multinational retail corporation,
used big data technologies to manage its personal information department.
• Customer engagement - Divisions in big data technologies also include consumer interaction.
As it will aid in data of synchronization. It is a fundamental word for the functions of the
Information department in the organisation to handle information based on the number of
customers (Wixom, and et.al, 2019). User involvement is essential to the success of any
organisation. From the other hand, as it develops consumer elements in the business, this strategy
will aid in improving the value of the company by enhancing customer happiness. For instance,
ASDA is a multinational retail corporation that will effectively achieve consumer involvement
and happiness via the use of big data technology.
CONCLUSION
According to the above-mentioned report, big data is a special type of information that is useful
to both researchers and businesses. Big data technologies will assist researchers in handling a
larger part of the data utilizing effective approaches.

Poster

REFERENCES
Books and Journal
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analysis. International Journal of Parallel, Emergent and Distributed Systems, 34(6),
pp.632-652.
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.
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.
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.
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.
Bologa, A.R., Bologa, R. and Florea, A., 2019. Big data and specific analysis methods for
insurance fraud detection. Database Systems Journal, 4(4), pp.30-39.’
Demirkan, H. and Delen, D., 2017. Leveraging the capabilities of service-oriented decision
support systems: Putting analytics and big data in cloud. Decision Support
Systems, 55(1), pp.412-421.
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.