Information System

Running head: INFORMATION SYSTEM
Information System
Name of the Student
Name of the University
Author’s Note:

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INFORMATION SYSTEM
Table of Contents
1. Introduction............................................................................................................................2
2. Portfolio Tasks.......................................................................................................................2
2.1 Discussion on Machine Learning and Impact of it on Business with Proper Challenges
and Opportunities to manage Information Systems...............................................................2
2.2 Strategic Position of Amazon with Two Strategic Planning Tools and use of
Information Systems for supporting Business Strategies.......................................................5
2.3 Explanation of Advantages and Disadvantages for Migrating Information Systems to
the Cloud by considering Differences in Organizational Structure and Types of
Information Systems..............................................................................................................9
2.4 Example of an Ethical Dilemma relating to Information System with Difficulties in
reaching a Resolution and Ethical Perspectives...................................................................13
2.5 Comparison of Two Information System Development Methods of SDLC and Agile
and Description of its Effective Uses with Examples..........................................................16
3. Conclusion............................................................................................................................22
References................................................................................................................................23

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1. Introduction
The information systems are being referred to as parts of information and
communications technology, which several companies use interact for subsequently
supporting their business procedures. These systems are organization based systems,
designed for assembly, treating, storing and even distributing confidential information
(Laudon & Laudon, 2016). These information systems are mainly formed by four elements of
tasks, people, structures and technologies. Information is well interpreted or processed
without much issue or complexity by information system. The decision making process is
well supported with such information system and thus it becomes easy to complete any type
of operation on information. The following research report will be outlining 5 different
aspects of information system with relevant details.
2. Portfolio Tasks
2.1 Discussion on Machine Learning and Impact of it on Business with Proper
Challenges and Opportunities to manage Information Systems
Machine learning could be referred to as a popular application of artificial
intelligence, which can provide each and every system, with an ability of automatically
learning or improvising from experiences without even being explicitly programmed
(Quinlan, 2014). This particular application mainly emphasizes on the overall expansion of
the computerized programs that have the ability of easily and promptly accessing the
confidential information and then utilizing it for learning purposes. The entire technique of
this learning initiates with subsequent observations or data such as instructions or direct
experiences to look for different patterns in data as well as also making effective decision in
future on the basis of examples that are being provided. The major objective of machine
learning is enabling the computer system to learn automatically without any kind of human

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intervention or intervention as well as adjusting actions (Witten, Frank, Hall & Pal, 2016).
The most popular and significant methods of machine learning, categorized as unsupervised
or supervised, mainly involve supervised ML algorithms, un supervised machine learning
algorithms, semi supervised ML algorithms and finally reinforcement machine learning
algorithms (Papernot et al., 2017).
Machine learning has a distinctive impact on business. Any organization could
eventually incorporate the application of machine learning into the major procedures for
various strategic reasons (Abadi et al., 2016). This particular application could easily and
promptly deliver advantages like the core capability of discovering correlations and patterns,
improvising customer segmentation as well as targeting and finally incrementing the total
revenue, market position and growth of the business. Machine learning enables the
organizations in reduction of the total time, which is needed for overall collection and entry
of the data, since it is being performed in the most automatic method. Furthermore,
digitalization of process is helpful for preventing misprints, data error or even any other error,
which are due to human factors (Meng et al., 2016). The application of machine learning
could easily and promptly reduce the issue by not only streamlining data analysis or
processing of the large amounts of information than human beings, but also by the high
quality of that analysis.
The smart algorithms of machine learning completely rely on the statistical data that
can allow them in detection of the dependencies much more accurately after establishing
correct diagnosis. This type of mechanism is not prone to the respective factors of one kind,
when they have higher accessibility to a wide data pool that can contribute to effective
decision making process (Jordan & Mitchell, 2015). There are few of the major as well as the
most significant challenges and opportunities that are being faced by a business after

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implementing machine learning in the operations. The various challenges of machine learning
application in a business are provided below:
i) Talent Gap: The first challenge that is being faced by a business after implementing
machine learning is talent gap (Abadi et al., 2016). It becomes extremely difficult for a
business to find people, with technical abilities for understanding and implementing the
machine learning. An example of the challenge would be that this specific application could
easily and promptly contribute to the backlog of machine learning within an organization and
it could deliver capability, however it is required to ensure that the people involved and well
experienced and they comprise of the core ability of finding solutions to all issues faced in a
machine learning application programmed business (Marsland, 2014).
ii) Highly Expensive: The second distinctive and important challenge that is being
faced by a business while involving machine learning is that the computational needs are
extremely expensive (Papernot et al., 2017). For achieving all sorts of larger scale data
processing, a major issue of supply and demand is often suffered in the business and it is
being observed that even the larger organizations do not necessarily comprise of GPUs
accessible to all employees, which require them and the teams that are trying to do machine
learning require longer time period for training the models.
iii) Requires Constant Data: This particular applications requires constant data up
gradation for completing the processes effectively. Machine learning needs labelled data for
providing the answer over a variety of inputs, so that it could easily predict the future updates
without much complexity (Quinlan, 2014). However, providing continuous data could be
extremely expensive for the business and without proper data, it becomes impossible for the
business to execute its operations. An example of such challenge is that machine learning

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needs more data and it becomes extremely difficult to provide proper solutions without this
data.
In spite of having such distinctive challenges, there are few opportunities as well that
are being provided by machine learning, which are as follows:
i) High Security: Machine learning is responsible for providing high data security in
any business and hence the respective organization does not face any type of complexity,
after reduction of cyber security attacks (Abadi et al., 2016). One of the most significant
examples of this opportunity is that hackers could not get any opportunity to hack data and
potential risks to data are minimized to a high level. It could even make better predictions
with time and help to automate the tasks efficiently.
ii) Provides Advantages in Marketing and Sales: Machine learning helps businesses
in finding the most valuable customers and also in identification or gaining new prospects.
The tools of this application perfectly compasses the business in reaching to the customers
(Jordan & Mitchell, 2015). An example of this type of opportunity would be that the data
driven insights help in enabling sales people in obtaining better sales and incrementing
performances.
iii) Better Customer Services: Customers get the opportunity to connect to the
business directly and without involving any complexity in the process. Machine learning
provides chatbot that can use workflows with several features for building a resourceful bot.
These chatbots can understand intents of customers and hence issues related to customer
satisfaction is being resolved effectively (Marsland, 2014). The most significant example of
his opportunity is that it provides conversational AI machine, which makes the bot
conversation looks more like human beings.