Big Data Techniques for Business Management: Challenges, Analysis, and Opportunities

This report explores the principles and features of big data, including its characteristics and the challenges of big data analytics. It also covers the techniques currently available to analyze big data and how big data technology can support business growth. The report provides examples of how big data can be used to drive innovation, improve supply chain management, and protect privacy.

Business Management
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
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Contents
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Introduction p
What big data is and the characteristics of big data p
The challenges of big data analytics p
The techniques that are currently available to analyse big data
p
How Big Data technology could support business, an explanation
with examples p
Poster p
References p
Introduction
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Big data methods refer to the wider data that links to the various consumers in the
marketplace. As it is very essential and important segment for the organization to maintain
there technology of big data in terms of it’s structure. Various businesses or organizations
should utilise this technology in order to safe and successful growth of the business. The
following report is based on the big data techniques and their principle or features, various
kinds of the challenges or issues, methods that are currently there too examine the big data.
Moreover it will include the way in which the technology of the big data can help the
organization to get long term profitability and success (Grover, and Kar, 2017).
What big data is and the characteristics of big data
Data is gained from numerous of sources numbers, forms text, video, audio, photos.
When a person opens up an application Like Google Maps and casually travel from one place
to another with electronic device like mobile then it can lead to generation of data
consistently. Large quantity of helpful data that an organization and business can store,
manage, visualize and evaluate because current data tools are not that reliable to manage this
sort of complexity and volume. In other word, big data technology referred to as a larger term
of the storage of data that link to the variety of consumers in the management. It is one of the
vital and essential section for the management in order to manage their big large data in case
of evolution of orientation of it (Martin, and Schuurman, 2020). The business of the
organization has to make an effective and efficient operational software for keeping the
operations and functions that are linked with techniques of big data. Moreover, every
connection with the technology gains new data that can be utilised to describe. In order to
cater to the target consumers of the organization, management can utilise these research to
aim of boosting, making efforts and company strategies. The need of the big data and its
usage has been known for a very long time, technology has only made it possible to evaluate
worst quantity of the data consistently and quickly with efficiency. Data is both unstructured
and structured when which will be combined and evaluated in the few years in accordance to
know the deep inside and likely assist to predict the future. As collection of data and
interpretation of it has become more convenient and accessible, wake data will assist small
businesses two large organizations (Ismail, Shehab, and El-Henawy, 2019).
Characteristics of big data :
Features of big data is a combination of criterion that shows various approaches to analysis of
big data and characteristics of big data are described below :
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There are four V’s of big data that are value , variety, volume and velocity (Ghasemaghaei,
2020).
It refers to the big size of data that is generated consistently from the various sources like
networks, machines, social media platforms, Internet, human interaction, and various others.
The four the four V’s and its characteristics are mentioned below:
In the similar manner that the total variety and volume of data companies maintain and collect
has now evolved, so this has the speed at which it is generated and maintained. It is one of the
biggest myths in respect of big data that velocity is one of the most crucial elements of big
data.
It consist of variety of kinds involving unstructured, structured and semi structured data
combined from a multiple of source. Earlier, data had to be gained from different
spreadsheets and databases. However, Velocity of big data refers to the authenticity, quality
and amount of errors of the data gathered..
The challenges of big data analytics
Big data evaluation that links effective and efficient way of the divisions in the
organization. Nowadays almost every business our organization are utilising this technique of
big data effectively and efficiently . The organization requires two focuses on big data
methods in order to capture and maintain the customer satisfaction in order to sustain the long
term growth and success of the business. As it is a basic necessity of big data analysis in
accordance to keep the customer ‘s personal information in an efficient and effective way
(Patel, Shah, and Shah, 2020).
The various basic challenges or issues of the big data technique are described below briefly:
 Security of data – It is very much crucial in maintaining and controlling the large
data of their consumers from any type of virus or hacker at the broader and specific
term of the division. One of the most modified set of data from various sort of the
section can get present the orientation of leader or manager in giving security to the
data of the customer which can help in getting the trust of the consumer and
maintaining their loyalty for the future growth. Various terms of the international
methods that functions several operations like spyware, malware etc. It can have the
direct effect on the wider source of data with high effective security of the data
(Ashabi, Sahibuddin, and Haghighi, 2020)
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 Complexity of data - This term relates to the complexity in the managing and storage
of the big data methods, as it is very crucial for the professional to overcome the
challenges and issues in an effective way. The source of data must be examined and
analyzed in a secure manner in order to prevent the viruses or any kind of issue to
harm the data or information. Experts in the organization develop several records in
order to keep the data into it for a longer period of time that can be utilized anytime by
the firm.
 Quality data - By maintaining the quality of the collection of the data then it can be
tough for the firm to store and save it away from the viruses or any other problems,
which can harm the information. As it will get helped by managing and clearing out
the false data also error in the area of the business that stores the data. Moreover, it
will be very beneficial for the firm to manage all the issues in order to save the source
of the data
The techniques that are currently available to analyse big data
 Machine learning - It refers to evaluating its various ways of sector that keeps a
message at it will affect the robotics learning, artificial intelligence which can directly
related with the method and framework of the computer science with appropriate
schedule of the management.
 Data mining - It refers to the efficient method that is gained by the organization in
order to maintain their dealings with large data analysis. Data mining can affect the
divisions which deal with the suitable and possible terms that include machine and
figures (Sheeran, and Steele, 2017).
 Statistics - It refers to the big data technology that includes various methods of
process to keep the files in the way of source of the data that are usually dependable
on the scalable and diverse area of the division. It also supports in keeping an effective
data that should be there in the business of any industry which produced goods or
services for market share with effectiveness.
How Big Data technology could support business, an explanation with examples
 Innovation based on the data - It takes a lot of time and efforts in order to identify
key areas that are growing for the experimentation and attempts in order to innovate
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something. Big data methods can help with research and development department that
can lead to the innovation of new services and products (Grover, and Kar, 2017).
 Flexible supply chain - Big data includes predetermined evaluation and it is usually
done in present time that helps in maintaining the global link of demand, production,
and distribution in order to operate these things smoothly. There are various
companies which are able to operate in effective and efficient supply chain
management.
 Privacy- It is very essential for both organization and consumers. By utilizing this
method of big data technology it plays a vital role in the development of organization
by protecting the data of their consumers. For instance, Marks and Spencer is a
multinational retailer that utilizes the technology of big data in order to keep the data
of their consumers secure.
References
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.
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.
Patel, D., Shah, D. and Shah, M., 2020. The intertwine of brain and body: a quantitative analysis on
how big data influences the system of sports. Annals of Data Science, 7(1), pp.1-16.
Ismail, A., Shehab, A. and El-Henawy, I.M., 2019. Healthcare analysis in smart big data analytics:
reviews, challenges and recommendations. In Security in Smart Cities: Models,
Applications, and Challenges (pp. 27-45). Springer, Cham.
Martin, M.E. and Schuurman, N., 2020. Social media big data acquisition and analysis for qualitative
GIScience: Challenges and opportunities. Annals of the American Association of
Geographers, 110(5), pp.1335-1352.
Ashabi, A., Sahibuddin, S.B. and Haghighi, M.S., 2020, April. Big data: Current challenges and future
scope. In 2020 IEEE 10th Symposium on Computer Applications & Industrial Electronics
(ISCAIE) (pp. 131-134). IEEE.
Sheeran, M. and Steele, R., 2017, October. A framework for big data technology in health and
healthcare. In 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile
Communication Conference (UEMCON) (pp. 401-407). IEEE.
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.
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