Professional Skills for Information and Communication Report 2022

Running head: PROFESSIONAL IT SKILLS AND COMMUNICATION
Professional Skills for Information and Communication
Name of the Student
Name of the University
Author’s Note:

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Executive Summary
The objective of the report is for understanding about the case study of JD organization. For
the purpose of improving their business efficiency and effectiveness, the Chief Technology
Officer of this organization has decided to implement machine learning application in their
business. Machine learning majorly emphasizes on the computerized programs, which could
easily and promptly access the confidential data and then utilize it for learning purposes. The
major procedures that are included in this ML are absolutely similar to the predictive
modelling as well as data mining. These two technologies need searching through
confidential data for looking for patterns and even adjusting various program actions. The
algorithms of machine learning are eventually categorized as unsupervised and supervised
and both of these categories are responsible for analysing and utilizing for development of
predictions. The respective algorithms is then applied to the new data. This report has
provided detailed analysis on the several merits and demerits of ML and the aspects that
should be considered by JD are also provided in the report. Moreover, 3 applicable
suggestions are provided in the report for JD.

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Table of Contents
1. Introduction............................................................................................................................3
2. Discussion..............................................................................................................................3
2.1 Brief Knowledge of the Case Scenario for JD organization............................................3
2.2 Defining Machine Learning and Differences and Relation between AI and Machine
Learning.................................................................................................................................4
2.3 Application of Machine Learning in 3 Different Industries of Healthcare Industry,
Automotive Industry and Financial Services Industry...........................................................4
2.4 Investigation of Process of Machine Learning getting adopted in JD with its application
in 2 Different Business Functional Areas of Updating Resume in HR and Improving
Business Efficiency and Advantages and Disadvantages of the Application........................5
2.5 Discussion of 2 Issues for each Ethical, Legal and Social Aspects about ML
Application on Online Retail Platforms for JD......................................................................7
3. Conclusion..............................................................................................................................8
4. Three Recommendations for adopting Machine Learning in JD...........................................9
References................................................................................................................................10
Appendix..................................................................................................................................13

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1. Introduction
Machine learning (ML) is a distinctive AI application, which is responsible for
providing the systems with the core ability of automatically learning as well as improvising
from experiences without even being explicitly programmed (Quinlan, 2014). It is a basic
technique of learning initiates with the data observation, such as instruction and direct
experiences for checking for trends in the datum and then making effective decisions and
judgments in future on the basis of those experiences in the business. It is considered as the
one of the most significant and important applications, which can be easily and promptly
applied in any business for reducing complexities related to decision making technique
(Witten, Frank, Hall & Pal, 2016).
Few of the most distinctive programming languages that are considered to be the best
for machine learning mainly include Python, R, Java, Prolog and Lisp. These programming
languages help to reduce the complexities related to artificial intelligence and even for
building computer systems (Abadi et al., 2016). This report would be providing a detailed
description of the case study of an organization, called JD. It is an Australian online retailer
that sells larger range of items in the entire world. They have realized that the application of
machine learning would be extremely effective for them for increasing the overall business
efficiency and gaining competitive edges. This report will be describing about the difference
and relationship between AI and machine learning, application of machine learning in 3
industries with ethical logical and social issues in details.
2. Discussion
2.1 Brief Knowledge of the Case Scenario for JD organization
JD is an online retail within Australia, which provides several items like consumers’
electronics, accessory or apparel as well as books to their clients in the entire world. By
proper adoption of e-commerce, they have observed a massive increment in the sales.
However, still they are unable to capture the highest market shares. Only 200 million
Australian dollars were spent on the retail goods in the last year. It was a sharp decline of
20% in the retail goods expenditure than last year. To solve this situation, the CTO of JD has
decided to include ML application in their business for bringing better business success and
also increasing customer satisfaction. They will also be implementing machine learning in
other business functional areas like automatic resume screening within the department of
human resources and also increasing total business efficiency for gaining competitive edges.

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2.2 Defining Machine Learning and Differences and Relation between AI and Machine
Learning
Machine learning can be defined as the classification of algorithm, which enables
software application for becoming more precise in proper prediction of results without even
explicit programs (Meng et al., 2016). The most basic premise of this ML is building
algorithm, which can receive input datum and even utilizing statistical analyses to predict the
outputs or up grading the outputs with new data. AI, on the contrary, is the proper simulation
of different processes of human intelligence with the help of machines or computerized
systems. These distinct processes mainly include learning, reasoning and even self correction
(Marsland, 2014). The main applications of this AI involve expert systems, machine
visioning and speech recognitions. ML is related to AI since ML is a subset of AI and helps
to reduce complexities to a high level. However, although they are related, technology of AI
is different from ML. AI is the acquisition of knowledge intelligence as the major ability of
acquiring and applying knowledge, whereas ML is the acquisition of skills and knowledge
(Jordan & Mitchell, 2015).
AI works as a computerized program, which is responsible for doing smart work and
ML is a simplified concept, in which machine undertakes data and then learn from data. AI
helps in decision making, however ML allows the system for learning new systems from the
data (Chen et al., 2014). AI leads in developing system for mimicking human being for
responding to behaviours, however ML is responsible for involving in creation of the self
learning algorithms. Artificial intelligence leads to wisdom and intelligence and machine
learning leads to proper knowledge of that particular human being or user.
2.3 Application of Machine Learning in 3 Different Industries of Healthcare Industry,
Automotive Industry and Financial Services Industry
Machine learning has become extremely popular for several distinctive industries and
are being applied in different businesses. They are providing efficient services to not only to
the online retail or electronic commerce services, but also to other different industry sectors
in the world (Xingjian et al., 2015). Three other industries in which machine learning has
provided major services for their business efficiency and effectiveness are as follows:
i) The Healthcare Industry: The first and the foremost type of industry that has got
major advantages from the application of machine learning is the healthcare industry
(Papernot et al., 2017). Machine learning has been fast acquired by this particular industry

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with the subsequent advent of several wearable devices as well as sensors, which could easily
and promptly utilize data for the core purpose of assessing health of patients within real time.
It is being observed from a survey that in the year of 2017, more than 7 million Americans
had enrolled themselves in the digitalized health platform, in which the most important signs
were continuously being monitored by the sensors, worn over the body of the patient (Chen et
al., 2015). This specific information was sent to the analytics centre of machine learning,
which then flagged anomalies and alerted the treatment professionals. Machine learning is
even helpful for the medical experts in analysing the confidential data for identifying the
trends, which might lead to the most improvised diagnosis.
ii) The Automotive Industry: The second significant and important industry that has
applied machine learning is the automotive industry (Biamonte et al., 2017). This particular
industry is taking steps for differentiating itself after leveraging the capabilities of machine
learning as well as big data analytics for improvement of operations, customer experiences
and marketing while purchasing. Predictive analytics allows the manufacturers in monitoring
or sharing confidential information about potential vehicles and part failures with reduction in
the expenses of vehicle maintenance (Low et al., 2014). Moreover, after identification of
patterns and trends from the larger datasets for vehicle ownership, these dealer networks
could be eventually optimized by locations for more accurate parts inventory as well as
improvised customer satisfaction.
iii) The Financial Services Industry: Another popular and distinctive industry that
has applied machine learning for their businesses is the financial services industry (Lison,
2015). The banks as well as the other banking businesses within the financial industry utilize
he technology of machine learning for two major purposes, which are identification of vital
insights within data and even prevention of fraud. These insights could eventually identify the
opportunities of investments and even helping investors know the time for trade. The
technology of data mining could even identify the clients with higher risk profiles and even
utilize the respective cyber surveillance for pinpointing each and every warning sign of fraud
(Obermeyer & Emanuel, 2016). As a result, the entire industry of financial services has got
several advantages from machine learning and the issues related to financial services were
resolved completely.

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2.4 Investigation of Process of Machine Learning getting adopted in JD with its
application in 2 Different Business Functional Areas of Updating Resume in HR and
Improving Business Efficiency and Advantages and Disadvantages of the Application
The CTO of JD has decided to deploy application of ML in their business for
improving the business efficiency and customer satisfaction to a high level. Three distinctive
requirements should be taken into consideration by them for implementing machine learning
in the business in JD (Ghahramani, 2015). The first as well as the most significant aspect of
machine learning is introduction of specialized ML roles. For the purpose of adopting ML
within the business, JD would have to introduce special roles such as specialists of data
optimization as well as deployment of machine learning models. They will have to involve
data engineer, data scientist and deep learning engineer. Moreover, they would also have to
implement specific success metrics of machine learning.
There should be a reduction in the complexities within their business. Another
significant aspect of machine learning application that should be taken into consideration is
building robust model checklists. JD involve model deployment, monitoring and operations
(Li et al., 2014). They will be able to improve their 2 different business functional areas of
automatic resume screening in the HR department and incrementing business efficiency for
gaining competitive edges. ML would be able to reduce the major complexities related to
issues in updating resumes periodically or existing problems in the business operations.
The major advantages of machine learning that JD would get are as follows:
i) Easy Identification of Patterns and Trends: There is an easy identification of
various patterns and trends in machine learning. It can easily review the larger volumes of
data for discovering specified trends, which will not be apparent to human beings. Since, JD
is an e-commerce site, the browsing habits as well as purchase histories of the customers
would be well understood with this application.
ii) Continuous Improvement: ML provides scope for continuous improvement in the
business and also provides accurate data without much complexity. Hence, decision making
process becomes extremely easier and effective.
iii) Lack of Human Intervention Model: ML does not need any type of human
intervention model in its application and thus it helps in automation of the programs

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(Kurakin, Goodfellow & Bengio, 2016). Threats and risks are easily identified within the
process.
The major demerits of ML that JD would get are as follows:
i) Data Acquisition: ML needs huge amount of data sets for making the process
effective and these data should be absolutely unbiased in nature (See Appendix). However,
there often exists issues related to data acquisition and hence it is needed to generate new
data.
ii) Time and Resources: There is a major requirement of time and resources for
implementing ML in a business (Carrasquilla & Melko, 2017). Considerable amounts of
relevancy and accuracy are required for this purpose and it could lead to failure in the
processes of business complexity due to excess expenses.
2.5 Discussion of 2 Issues for each Ethical, Legal and Social Aspects about ML
Application on Online Retail Platforms for JD
i) Ethical Issues: The major ethical issues of ML on the platforms of online retail are
as follows:
a) Usage of Data: The algorithms of machine learning although, provide numerous
advantages to the customers, they never take consent or knowledge of users for using data
and hence it is a significant ethical concern to not warn users for data usage.
b) Possible Biases: The second important and significant ethical issue is possible
biases (Xiao, Rasul & Vollgraf, 2017). The utilization of ML often leads to biases and hence
effective results are not obtained in the business. The programs are changed eventually as per
consideration of these biases and customers do not get products according to their demands.
ii) Legal Issues: The major legal issues of machine learning on the platforms of
online retailer are as follows:
a) Inventory Issues: This is the first and the most significant legal issue that is being
faced in the online retailer from machine learning. If there exists any type of complexity of
issue related to inventory tracking, it is being observed that maximum of the time it is for the
application of machine learning (Obermeyer & Emanuel, 2016). This particular technology
cannot resolve inventory issues and hence discrepancy in inventory management takes place.

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b) Lack of Data Privacy: The second important and noteworthy legal issue that is
extremely common in this application of machine learning is lack of data. As ML mainly
deals with large sets of data, it is evident that data security should be kept on top priority,
however data often gets lost with this application.
ii) Social Issues: The major social issues of machine learning on the platforms of
online retailer are as follows:
a) Sustainability Issues: This is the first and the most significant social issue that is
being faced in the online retailer from machine learning (Xingjian et al., 2015). Such
distinctive sustainability issues could lower the business opportunities and threats to the
products and services would be increased.
b) Product Liability: The second important and noteworthy social issue that is
extremely common in this application of machine learning would be product liability. There
could be a high chance that due to excessive involvement of machine learning, the quality of
products could be degraded and since JD is an online retailer, the customers would not get
any idea regarding the product quality, until they receive it and hence if there exists any kind
of problem or complexity, the product liability would be lost and customers would be highly
dissatisfied (Lison, 2015).
3. Conclusion
Hence, conclusion could be drawn that ML is one of the most significant and
important applications of AI that various computer systems utilize for performing a specified
task without even using any type of external instructions and completely relying on the
inference and patterns. It is being observed as the subset of AI and the algorithms help in
building the mathematical models on the basis of sample datum, called training datum and
hence making estimates or decisions for performing the respective task. The major algorithms
of ML are being utilized in a wider variety of applications like computer vision and email
filtering, in which it becomes extremely difficult and infeasible to develop the most
conventional algorithm to perform the task efficiently.
Machine learning technology is closely associated to the computational statistics that
emphasizes for making proper estimations with the help of computer systems. In the
application of data mining for several business problems, this machine learning is termed as
predictive analytics. The tasks of this machine learning are broadly classified into categories,

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such as supervised learning, semi supervised learning and unsupervised learning. The major
algorithms help in successful identification of spam emails and are being represented by
Boolean values. The above provided report has clearly outlined the detailed case study
analysis of the organization, called JD. The CTO of the organization has taking the decision
of including machine learning in their business and also getting more effectiveness and
efficiency in their business operations. Three suitable recommendations are being provided to
JD for adopting ML application in their business.
4. Three Recommendations for adopting Machine Learning in JD
JD should adopt machine learning in their business for making their business
operations and products much more effective. Three recommendations for them to adopt
machine learning are provided below:
i) Introducing Special ML Roles: JD should introduce special roles of machine
learning like data optimization specialists and deploying ML models. JD should include new
job titles like data engineer, deep learning engineer and data scientist.
ii) Implementation of Specified ML Success Metrics: as the lesser experienced
companies are dependent on product managers for successfully determining the major criteria
for success of such project. However, entrusting data science could lead in setting team
priorities. As a result, they would be able to reduce the complexities in the business.
iii) Building Robust Model Checklists: Building different robust model checklists is
the third recommendation for JD. They can adapt the procedures, utilized in the software
development for successfully building the data products. JD would be able to include model
deployment, operations and monitoring.

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Appendix
Advantages and Disadvantages of Machine Learning