Tesfamaryam_M_IST530_Assignment1_Week1
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Information Systems
Date
Dec 6, 2023
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Uploaded by millionzak
1
Closing Case One
Million Tesfamaryam
California International University
IST 530 – Management Information Systems
Dr. Nancy Severe-Barnett
Sunday, November 12
th
, 2023
2
Types of Decision-Making Systems
Making effective decisions is a crucial aspect of communication and leadership across all
levels of business operations. The ability to make informed choices and take swift action is vital
for the success of any organization. Paige Baltzan, an expert in the field of information systems,
highlights the significance of decision-making systems in supporting businesses to make
informed choices. These systems can be grouped into three primary categories: operational
support systems, managerial support systems, and strategic support systems (Baltzan, 2023,
p.62).
Operational support systems are essential in facilitating day-to-day activities, including
inventory management and order processing. These systems are designed to make routine
decisions within an organization and are typically rule-based, aiming to streamline processes and
improve efficiency. However, machine learning can revolutionize these systems by automating
the decision-making process through the analysis of historical data. For example, in a
manufacturing setting, machine learning algorithms can predict equipment failures based on
sensor data, allowing for preemptive maintenance. Under operational support systems, there is
also transactional information that encompasses all the data contained within a single business
process or unit of work and its primary purpose is to support the performance of daily
operational or structured decisions. Managers use transactional information to make structured
decisions at the operational level, such as when analyzing daily sales reports to determine how
much inventory to carry (Baltzan, 2023, p.63).
Managerial Support Systems are a crucial tool for decision-making at the middle level of
an organization, helping managers analyze sales trends and allocate resources effectively. This
type of system provides analytical information that encompasses all organizational data, aimed at
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supporting the performance of managerial analysis or semi-structured decisions. Analytical
information includes transactional data, as well as market and industry information that can be
used to make informed decisions. Decision Support Systems are another type of managerial
support system that employs models to evaluate and compare different courses of action,
enabling managers to choose the best possible option. These systems provide a comprehensive
view of the organization's data, allowing managers to make data-driven decisions that can lead to
success.
Strategic support systems are essential for long-term planning, predicting market trends,
and identifying business opportunities. These systems are used by top-level management to
streamline decision-making processes and achieve better outcomes. They help in making long-
term, high-impact decisions that shape an organization's goals and direction. Machine learning
can support strategic decision-making by analyzing vast data to identify trends, market
opportunities, and potential risks. In addition, there is an executive information system (EIS) that
supports senior-level executives in making unstructured, long-term, nonroutine decisions that
require judgment, evaluation, and insight (Baltzan, 2023, p.64).
How machine learning can transform decision-making
The use of machine learning technology has a significant impact on decision-making
processes as it automates tasks, provides data-driven insights, enables predictive analytics,
facilitates personalization, and improves risk management. Decision-making algorithms can
achieve greater accuracy and efficiency through machine learning. By training models on large
datasets, these systems can better adapt to changing patterns and improve decision accuracy over
time. Machine learning is highly valuable in assisting manual decision-making as it provides
valuable insights from large and complex datasets. By analyzing historical data, identifying
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patterns, and predicting future trends, decision-makers can be empowered with data-driven
information for informed choices. With predictive analytics facilitated by machine learning,
organizations can anticipate trends, risks, and opportunities by analyzing historical data and
predicting future outcomes. This empowers decision-makers to make the best informed choices.
Finally, machine learning can identify potential issues early to assess and mitigate risks, making
it an essential tool in risk management.
How machine learning can transform a traditional business process
Machine learning is a powerful tool that has the potential to revolutionize traditional
processes like the checkout process in a grocery store. By leveraging machine learning
algorithms, businesses can introduce efficiency, speed, and personalization into their operations.
One of the most significant benefits of machine learning in the checkout process is its ability to
automate item recognition through computer vision systems. This means that customers can
simply place their items in their cart, and the system can identify and tally them automatically
without the need for manual scanning. In addition to automating the checkout process, machine
learning can also be used to analyze customer feedback and provide valuable insights into areas
of improvement. By continuously collecting and analyzing customer feedback, businesses can
identify pain points in the checkout process and make necessary improvements to enhance the
overall customer experience. The integration of machine learning into the traditional grocery
store checkout process can enable businesses to create a more seamless, personalized, and
efficient experience for their customers. By optimizing their operations through machine
learning, businesses can reduce wait times, minimize errors, and improve the accuracy of the
checkout process. Ultimately, this can result in increased customer satisfaction and loyalty, which
can translate into higher sales and revenue for the business.
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Relationship between bias and machine learning for Alexa
The relationship between bias and machine learning is a crucial aspect that needs to be
addressed when it comes to the development and deployment of devices like Alexa that rely on
natural language processing and artificial intelligence. Bias, in this context, refers to the
existence of systematic and unfair discrimination in the algorithms, data, or processes involved
in creating machine learning models. The algorithms used in NLP can contribute to bias,
especially if they inherently favor certain linguistic patterns or characteristics over others. This
can result in biased outcomes in how Alexa understands and responds to user inputs. Besides the
algorithms, user interactions and feedback can also introduce bias. If users from a specific
demographic are more inclined to provide feedback or correct the device's responses, it may
create a feedback loop that reinforces biases over time. This can lead to biased outcomes and
affect the device's performance and accuracy. Addressing bias in machine learning for devices
like Alexa is crucial to ensure fairness, inclusivity, and ethical use. This involves creating
algorithms that are unbiased and inclusive of all linguistic patterns and characteristics, as well as
implementing measures to counteract potential bias introduced by user interactions and feedback.
Do machine learning systems like Alexa invade user privacy
Machine learning systems like Amazon's Alexa are becoming increasingly popular for
their ability to offer personalized and context-aware services. However, the use of these systems
raises valid concerns about user privacy. The collection and processing of user data is an integral
part of machine learning, and this data is often stored in the cloud for analysis and improvement
of the system. While this helps to improve the accuracy of the system, it also means that sensitive
user data is vulnerable to potential unauthorized access or data breaches.
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One of the main concerns is the collection of voice recordings. Alexa and other virtual
assistants continuously listen for a wake word like "Alexa" to activate and respond to user
commands. While the device is designed to process and store only the audio that follows the
wake word, the fact that it is always listening raises concerns about unintentional activations and
the potential capture of private conversations. This is particularly concerning given the sensitive
nature of some of the conversations that may take place in homes or other private locations.
Overall, it is clear that machine learning systems like Alexa have the potential to greatly
enhance our lives, but we must also be aware of the potential privacy risks associated with their
use. It is important for users to understand how their data is being collected, stored, and used,
and for companies to take steps to protect user privacy and ensure that data is only used with
explicit user consent.
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References
Baltzan, Paige. (2023).
Business Driven Information Systems (8
th
Edition)
. New York, NY:
McGraw Hill LLC.
Dickson, Ben. (2016). “Machine learning has a privacy problem.”
https://bdtechtalks.com/2016/08/26/machine-learning-has-a-privacy-problem/
Jiang, Hui. (2022).
Machine Learning Fundamentals
. Cambridge, United Kingdom: Cambridge
University Press.
Secore, Cam. (2018). “Artificial Intelligence: Helpful or an Invasion of Privacy?”
https://smallbizclub.com/technology/security/artificial-intelligence-helpful-invasion-
privacy/