Big Data Analytics: Techniques, Challenges, and Business Support

This report from Desklib covers the definition and characteristics of big data, the challenges of big data analytics, techniques for analyzing big data, and how big data technology can support businesses. The report includes examples and references to support the discussion. The subject is BSc (Hons) Business Management, and the course code is BMP4005 Information Systems and Big Data Analysis.

BSc (Hons) Business Management
BMP4005
Information Systems and Big Data
Analysis
Poster and Accompanying Paper
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Contents
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
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Introduction
Big data refers to collection of unstructured, semi structured, structured gathered
by the company that can be used in machine learning work, predictive modeling and
advanced analytic model. It provide effective competitive advantage as it make
organizational performance faster and help to make decision correctly.(Saggi and Jain,
2018) The report will cover discussion about big data and its difficulty of big data whiling
using. In addition to this, the tools available for analyzing the big data along with big data
aid to organizational business.
What big data is and the characteristics of big data
The big data is a information that contains huge verity, more velocity and more
volume. The big data is bigger in numerous that can process in software so that
organization easily address the problems and tackle the threat. .(Ngiam and Khor,
2019)The big data is helpful for operational work,establish personalized marketing
campaigns and determines the other actions. The five characteristics of big data is
mention below.
Volume: The big data is a large in volume that generate from different sources
such as human interaction, network, platforms, social media and business process. For
example, the Instagram can create approx. billions message, 4.5 billions times like
buttons and millions new post are transferred every day.
Veracity: The veracity in big data that means data is more reliable and genuine so
that it can be translate and filtered the data. The veracity refers to process in which
expert and manger able to mange and tackle the data smoothly as it important for
organizational growth.(Naeem and et.al., 2022) For example, The Instagram post with
hashtags.
Variety: The big data is defined in different such as quasi-structure, semi-
structured, structured and unstructured that are accumulated from different roots such
as sheets and database in the past but the data comes different framework such as
video, photos, SM post, PDF, audio and email etc.
Value: The value is an important elements of big data as it is a valuable data that
company analyses, process and store.
Velocity:The velocity in big data in which create the pace in data In real time. It
include the linking of incoming activity bursts, pace of change and data set speeds. Big
data velocity cope with the pace in the transferring from different sources like mobile
devices,senors, social media, network, business and application logs.
The challenges of big data analytics
In competitive world, The multinational companies are using big data analytics
that enhance the decision quality. However, the companies face difficulty in handling big
data that will mention below.
Lack of data: The company have not enough sources to generate new insights as
poor data organization or lack of integration. (Luengo and et.al. 2020)The small company
face challenges to find the different sources as it may have not expert to handle the bid
data.
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Old approaches: The organization have transferred the complex data to new
systems as it would be arduous to acquire fresh answers. The mostly companies have
not advanced tool to handle this threat and fix the problems.
Poor quality: Organizational decision rely on big data as it may a chance that
data have contains errors, incomplete and defects so that organization fail to make
quality decision.
System defect: The company face challenges to mange the big data due to error
that intervene the organizational development, verification and testing process.
Presently, the multinational companies are using high quality testing system that
eliminate numbers of issues.
Messy data: The big data are more complex and typical to understand as it
involve messy and unorganized form that make difficulty to extract the essential data.
This problems can be solved by UX/UI expert that will assess the manager to handle
that big data
Securing data: The company main focus on understanding, analyzing and storing
the data instead of securing the big data from hacker. (Ghasemaghaei and Calic,
2019)The company are hiring cuber-security experts to protect the data.
Expensive: the big data require huge investment to mange the big data so that it
may difficult for small organization. The risk manager critically estimate the return on
investment and make strong organizational goals.
Shortage of experts: Some company face difficulty in analysis the big data due
to lack of professional experts. Employees or mangers have not complete knowledge of
operate the big data.
The techniques that are currently available to analyse big
data
Data integration and data fusion: In this techniques, The company integrate
and analyse data from different solution and sources. The insights are more accurate
and efficient than if formed through a single source
A/B testing: This technique includes examining a control group with a different
test groups. In contemplation of recognize what change or treatments will enhance a
bestowed goals. The big data again set in this model because It can examine huge
value.
Statistics: This techniques is used for interpreting data, organizing and
collecting within experiments and survey. The application that manage, analyse and
process this data are completely expansive and different field which similarity
develop and evolves over time.
Data mining: The company commonly used this technique in big data as it
extract figure from big data sets by amalgamating methods from machine and
statistics,
Machine learning: In growing world, company highly use artificial intelligence
with the help of machine learning for analysis the data. It perform with computer
formula to provide assumption depend on data. (Elia, G.,and et.al., 2020)It
determines the forecasting which would be unfeasible for manlike analysts.
Natural language processing (NLP): This process is used for algorithms to
examine human communication.
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How Big Data technology could support business, an
explanation with examples
Reduce cost: The company can acquire the information that essential to pinpoint
the unskillful in their company's operation so that they can resolve the issue and reduce
the unnecessary cost. For example, The data indicate that consumer have low interest
in buying gift wrapper.(Choi, Wallace, and Wang , 2018) This may cause the company
remove to offering a gift wrapping so that it reduce the operational cost and increase
the profitability of the company.
Increase revenue:The company can gain essential insight into feeling and
consumers shopping taste of their consumers. With the help of big data, the company
design the product and service in contemplation of rendering to consumers what
exactly they like. For example, Apple has designed smart watch and smart hand band
according to the buyer's desire so that increase the sales of particular product.
Enhance pricing decision: Big data determine that the pricing strategy in which
organization compare the price to their competitors.(Zhang and et.al., 2018). For
example, Apple raise and down their product's prices along with analyze their product
from its competitors such as dell, HP and Samsung etc. with the help of big data.
Competitive advantage: The big data help to organization to aim at selective
local customer, focusing on local market and render insight on consumers behaviors.
For example, Apple always are relying on big data as it provide consumer's like and
dislike so that respective company enjoying the competitive advantage.
Improve efficiency in decision making:The manger make decision easily with
the help of big data as it analysis social media data such as interest, behavior and
promotion. For example,Apple mangers examine the social media user profile in
Linkedin, Instagram, twitter and Facebook so that manager easily make decision with
the help of big data analysis.
Fraud detection: The company easily can detect the fraud with the help of big
data as data analysts use artificial learning and machine learning to detect transaction
and anomalies patterns. These pattern help to indicate something is mismatch that
give clues about it may chance of frauds. It generally used in band and financial
industry for example, The Apple are using big data for examining the company's
genuine product so that company's product are reliable for consumers.
HISTORY
The first suggestion of big data was seen in 1663 when Johan Grault
managed with magnitudes amount of information. He became the first person to
utilization of statistical data analysis. (Javaid and et.al.,2021)He has worked on
accounting principles to develop and improve but not achieved extra ordinary. In 2005,
companies and individual realized that much data generated by user through You tube,
Facebook and Orkut etc, so that Hadoop was formed in that same year. The Hadoop
was an essential tool to analyses and store the big data sets and it made easy to store
the data at nominal price. Cloud computing has enlarged big data expectation and it
offer flexible scalability where developer can easily accelerate ad hoc to examine the
data as well as graph data bases has ability to screen huge amount of data that make
comprehensive and fast analytical
Poster
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Paste your digital poster here
References
Baig, M.I., Shuib, L. and Yadegaridehkordi, E., 2019. Big data adoption: State of the art
and research challenges. Information Processing & Management, 56(6),
p.102095.
Javaid, M.,and et.al., 2021. Significant applications of big data in Industry 4.0. Journal of
Industrial Integration and Management, 6(04), pp.429-447.
Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data analytics in operations
management. Production and Operations Management, 27(10), pp.1868-1883.
Elia, G.,and et.al., 2020. A multi-dimension framework for value creation through big
data. Industrial Marketing Management, 90, pp.617-632.
Ghasemaghaei, M. and Calic, G., 2019. Can big data improve firm decision quality? The
role of data quality and data diagnosticity. Decision Support Systems, 120,
pp.38-49.
Luengo, J.and et.al. 2020. Big data preprocessing. Cham: Springer.
Naeem, M.and et.al., 2022. Trends and future perspective challenges in big data.
In Advances in Intelligent Data Analysis and Applications (pp. 309-325).
Springer, Singapore.
Ngiam, K.Y. and Khor, W., 2019. Big data and machine learning algorithms for health-
care delivery. The Lancet Oncology, 20(5), pp.e262-e273.
Saggi, M.K. and Jain, S., 2018. A survey towards an integration of big data analytics to
big insights for value-creation. Information Processing & Management, 54(5),
pp.758-790.
Zhang, N and et.al., 2018. Synergy of big data and 5G wireless networks: opportunities,
approaches, and challenges. IEEE Wireless Communications, 25(1), pp.12-18.
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