[Solved] PPT VII

Assignment Prompt & Instructions

See attached

– ## UnitVIPowerPointPresentation.docx

– ## UnitVIStudyGuide.pdf

**## UnitVIPowerPointPresentation.docx**

**Unit VI PowerPoint Presentation**

**Harnessing the Power of AI for Strategic Decision- Making: Opportunities and Challenges**

The objective of this assignment is to understand and analyze the role of artificial intelligence (AI), business intelligence (BI), and business analytics (BA) in enhancing organizational decision-making processes, while also considering the ethical and social implications.

Using PowerPoint, create a fictional organization facing a strategic decision (e.g., market expansion, product launch, process optimization). Develop a detailed scenario including the hypothetical organization’s background, industry, and specific decision-making challenges.

Propose a plan for how AI, BI, and BA can be integrated to address the organization’s decision-making challenges, by creating visual aids like charts, graphs, or infographics. Create your visuals answering the following:

· Explain the value added to the organization you introduced by implementing advanced information systems solutions.

· Explore the role of artificial intelligence (AI) and machine learning in business and how your organization could benefit from adopting it.

· Analyze how business intelligence and business analytics can support decision-making in your organization.

· Examine ethical and social issues presented by automated machines.

Your presentation should include at least four visuals. Use the speaker notes to expand upon and explain your visuals.

Include a title page and a references page. You are required to use at least two peer-reviewed, academic sources that are no more than 5 years old (one may be your eTextbook). All sources used, must have citations and references properly formatted in APA Style.

**## UnitVIStudyGuide.pdf**

ITC 5301, Management Information Systems 1

Course Learning Outcomes for Unit VI

Upon completion of this unit, students should be able to:

3. Explain how information technology systems influence organizational strategies.
3.4 Explore the role of artificial intelligence (AI) and machine learning in business.
3.5 Analyze how business intelligence and business analytics can support decision-making.

4. Evaluate the prevailing ethical issues of information systems.

4.2 Examine ethical and social issues presented by automated machines.

Required Unit Resources

Chapter 11: Managing Knowledge and Artificial Intelligence

Chapter 12: Enhancing Decision Making

Unit Lesson

What Is Knowledge?

Most of us, at some point in our educational journey, have learned the difference between data, information,
and knowledge. We have discussed data in great detail in this course, so we know that data include the base
transactions of our systems. Those transactions mean little unless we use tools and turn the data into
information. We can discern sales information and inventory information, for example, via organized
categories. What do we do with that information? If we need a simple question answered, we can simply
query the information. For example, we may query a database to see what a specific customer’s annual sales
were for 2014. Also, we may pull a simple report. For example, if you manage all of the customers in New
York City, your report may list the annual sales for all of those customers in New York City. That is
information.

Knowledge is an asset to an organization and can take the form of information that has been put into some
sort of context. It can also take the form of tacit knowledge in the minds of employees. Regardless of the type
of knowledge, organizations need to manage it.

In many organizations, knowledge management is not a high priority, but for those who realize the value,
there are systems that can be used for enterprise-wide management. They help us to manage information
and have capabilities for capturing, storing, retrieving, distributing, and preserving knowledge. There is also
the opportunity for accessing internal information, such as reports, documents, and e-mails. What purpose is
it to have an enterprise knowledge management system? Where is the value for businesses? The answer is
better business processes and decisions!

The Business Value of Decision-Making

We use much of the information and knowledge captured in organizations for the purpose of decision-making.
Sales data are used to make decisions about inventory levels, the purchase of new products, credit levels,
and so on. Not only can you make decisions about an individual customer, but you can also make decisions
about a specific store or make organization-wide decisions. There are decisions made at all levels of the
organization, and some may be small with little impact or large with a business-wide impact. The actual value

UNIT VI STUDY GUIDE
Managing Knowledge, Artificial Intelligence,
and Enhancing Decision-Making

ITC 5301, Management Information Systems 2

UNIT x STUDY GUIDE
Title

to the business is based on many different factors. Regardless, it is important to note that information systems
will likely be involved in almost every decision in some way.

Information Systems in Management Decision-Making

There are different types of decisions used at different levels of management. For example, structured
decisions are fairly routine and not considered to be very risky; lower-level management uses them.
Unstructured decisions require a good amount of insight and judgment. They are more likely to be riskier
decisions and tend to be made by senior management. There are also semi-structured decisions that contain
some of both characteristics and are usually made by middle management. Regardless of the decision type
and role, information systems can support the decision-making process. In fact, in many cases, computer
technology is relied upon pretty heavily for decision-making.

Within the opening case of Chapter 11, “Artificial Intelligence Beats Radiologists in Reading Mammograms” is
a good example that demonstrates how organizations use new tools and technologies to improve their
business performance.

This case study reveals that in 2019, the American Cancer Society reported 268,600 new cases of invasive
breast cancer and predicted 41,760 deaths from breast cancer among women in the United States. Globally,
breast cancer is a leading source of death among women, with 2 million deaths reported. Mammography is
the primary tool used to detect breast cancer. It usually involves two or more x-ray pictures or images of each
breast. Doctors use mammograms to check for breast tumor in women who have no signs or symptoms of the
disease. There are about 33 million screening mammograms performed each year. However, manual tests
miss 20 percent of breast cancers due to false negatives. There is also a seven to 12 percentile of false
positives reported (American Cancer Society, 2019, as cited in Laudon & Laudon, 2022).

Researchers from Google Health, Northwestern University in Chicago, and Britain’s Cancer Research
Imperial Centre and Royal Surrey County Hospital developed a computer algorithm using AI technology to
read the X-ray images. The result is that the new AI-based system was more accurate than the radiologists.
The system was able to reduce false negatives by 9.4 percent and false positives by 5.7 percent. This
research is not yet ready for widespread clinical use. However, it demonstrated the potential advantages of
using AI to assist radiologists in reading mammograms as part of their routine practice (Grady, 2020;
Lichtenstein, 2020, as cited in Laudon & Laudon, 2022).

Decision-making Technologies

Decision support system (DSS). A DSS is an information system that is utilized to assist in the decision-
making process. A DSS consists of several different components, but the most important thing to understand
is that the DSS accepts information from both internal and external sources to make quick decisions under
rapidly changing conditions. The DSS technology provides easy graphical user interfaces and models to aid
in decision-making processes (Laudon & Laudon, 2022).

Business intelligence (BI). BI is a broad term used to describe an infrastructure designed to store, integrate,
process, analyze, and report data. All types of data that are included in this definition include what is referred
to as big data. Big data is simply a term to describe data so large that it does not fit effectively in standard
databases. BI information can be stored in transactional databases, especially as database management
systems improve and become more efficient. More likely, though, the data will be moved to a data warehouse
built appropriately for the organization and its needs. A part of the BI infrastructure includes tools for analytics
and handy user interfaces such as dashboards, reports, and scorecards.

BI offers rich information quickly to the decision makers, and the analytic tools help them to make sense of
that information. You might even say that the analytic tools help us to turn information into knowledge.

One of the more valuable uses for analytics involves forecasting or predictive analytics. Many times,
managers need to be able to use models to predict the outcome of future events. Predictive analytics use
statistical tools and data mining, for example, to predict future trends. One or more predictors may be
changed to see what the outcome might be. A good example might be marketing expenditures. If your
marketing budget is $100,000 per year and you have four product lines, what might be the best way to
allocate those funds? If you have historical data, you can use it along with your predictive variable to predict

ITC 5301, Management Information Systems 3

UNIT x STUDY GUIDE
Title

what the outcome will be. For example, if you spend $50,000 of the marketing allowance on product line A,
what will be your predicted net profits?

The opening case study in Chapter 12, “Big Data and the Internet of Things Drive Precision Agriculture,”
demonstrates how data analytics can be used to help organizations make better decisions more effectively. At
Purdue University’s College of Agriculture, students are learning about data-driven farming. Students and
researchers can study and learn how to improve plant growth and food production processes using an
agriculture-oriented network with advanced Internet of Things (IoT) sensors and devices. This technology has
the potential to improve every process from farm to table.

The IoT network oversees a 1,408-acre farm, analyzing and storing terabytes of data gathered daily from
sensors, cameras, and manual data inputs. With the use of Wi-Fi hotspots, the data from the farm is sent to a
supercomputer. Data is also collected by automated drones and rovers that examine the farm plots and
capture real-time data that are sent to the supercomputer. This project is an example of precision agriculture,
where data collected are analyzed with digital tools to assist with decision-making such as fertilizer
distribution, planting depth, watering cycles, and optimal harvesting times. This project demonstrates that
farmers can produce higher crop yields with less energy and waste.

Information systems are also used for other types of decision support. For example, senior management may
use executive support systems (ESS) to fully understand the performance of the organization. The balanced
scorecard methodology allows for the organization’s strategy and objectives to be operationalized into four
dimensions. This just means it breaks them down into four areas to be measured more effectively. The four
dimensions are financial, customers, business process, and learning and growth.

Another management method is business performance management (BPM), which allows management to
translate an organization’s objectives into operational targets. This means that management will have
understandable and achievable targets to measure against. Finally, there are group decision-support systems
(GDSS), which are interactive systems that allow decision makers to work together to solve problems. It is a
collaborative system that focuses on decision-making tools (Laudon & Laudon, 2022).

Ethical Issues of Information Systems

In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman in
Tempe, Arizona. Since the crash, Arizona has suspended autonomous vehicle testing in the state, and Uber
is not renewing its permit to test self-driving cars in California. The company has also stopped testing
autonomous cars in Pittsburgh and Toronto, and it is unclear when it will be revived.

Even before the accident, Uber’s self-driving cars were having trouble driving through construction zones and
next to tall vehicles like big truck rigs. Uber’s drivers had to intervene far more frequently than drivers in other
autonomous car projects. The Uber accident raised questions about whether autonomous vehicles were even
ready to be tested on public roads and how regulators should deal with this.

While proponents of self-driving cars like Tesla’s Elon Musk envision a self-driving world where almost all
traffic accidents would be eliminated and the elderly and disabled could travel freely, most Americans think
otherwise. A Pew Research Center survey found that most people did not want to ride in self-driving cars and
were unsure if they would make roads more dangerous or safer. In fact, 87% wanted a person always behind
the wheel and ready to take over if something went wrong.

Some pundits predict that in the next few decades, driverless technology will add $7 trillion to the global
economy and save hundreds of thousands of lives. At the same time, it could devastate the auto industry
along with gas stations, taxi drivers, and truckers. People might stop buying cars because services like Uber
using self-driving cars would be cheaper.

This could cause mass unemployment of taxi drivers and large reductions in auto sales. It would also cut
down the need for many parking garages and parking spaces, freeing up valuable real estate for other
purposes. More people might decide to live further from their workplaces because autonomous vehicles
linked to traffic systems would make traffic flow more smoothly and free riders to work, nap, or watch videos
while commuting.

ITC 5301, Management Information Systems 4

UNIT x STUDY GUIDE
Title

Some people will prosper. Most will probably benefit, but many will be left behind. Driverless technology is
estimated to change one in every nine U.S. jobs, although it will also create new jobs. Another consideration
is that the tremendous investment in autonomous vehicles, which is estimated to be around $32 billion
annually, might be better spent on improving public transportation systems like trains and subways. Does
America need more cars in sprawling urban areas where highways are already jammed?

Summary

As you can see from the list of available technologies and decision support tools, information systems are a
key component in their development and use. Technology is ever-changing, and the benefits are evident in
the tools that are available to assist managers.

Reference

Laudon, K. C., & Laudon, J. L. (2022). Management information systems: Managing the digital firm (17th ed.).

Pearson.

Suggested Unit Resources

In order to access the following resources, click the links below.

To reinforce the concepts from this unit, you are encouraged to review the following:

• Chapter 11 PowerPoint Presentation
(PDF for Chapter 11 PowerPoint presentation)

• Chapter 12 PowerPoint Presentation
(PDF for Chapter 12 PowerPoint presentation)

The following video cases mentioned in your eTextbook augment the information presented in the assigned
chapter readings. You are encouraged to review the Chapter 11 and 12 video cases linked below.

A transcript can be accessed for each video within the YouTube player by clicking on the three dots below the
video title on the right. Closed-captioning can be turned on by clicking the “cc” icon at the bottom of each
video.

Alfresco. (2012, June 7). Alfresco tour video [Video]. YouTube.

https://www.youtube.com/watch?v=p266dTL6oJQ

Alfresco. (2012, April 25). Alfresco case study: The New York Philharmonic [Video]. YouTube.

https://www.youtube.com/watch?v=UWI5RZRC56A

IBM. (2011, July 18). IBM Watson: The science behind an answer [Video]. YouTube.

https://www.youtube.com/watch?v=st-_yEvQeK0

IBM Watson. (2014, October 7). IBM Watson: How it works [Video]. YouTube.

https://www.youtube.com/watch?v=U6rvaWaiZNg

Learning Activities (Nongraded)

Nongraded Learning Activities are provided to aid students in their course of study. You do not have to submit
them. If you have questions, contact your instructor for further guidance and information.

Review the following case study, FreshDirect Uses Business Intelligence to Manage its Online Grocery, which
discusses how FreshDirect adopted a comprehensive enterprise data base system from SAP utilizing a
number of business intelligence applications to track orders and customers. There is a link within the case
study to an accompanying video. (A transcript can be accessed for the video within the YouTube player by

[https://online.columbiasouthern.edu/bbcswebdav/xid-150632003_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150632003_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150605739_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150605739_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150632005_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150632005_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150605741_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150605741_1)

[https://www.youtube.com/watch?v=p266dTL6oJQ](https://www.youtube.com/watch?v=p266dTL6oJQ)

[https://www.youtube.com/watch?v=UWI5RZRC56A](https://www.youtube.com/watch?v=UWI5RZRC56A)

[https://www.youtube.com/watch?v=st-_yEvQeK0](https://www.youtube.com/watch?v=st-_yEvQeK0)

[https://www.youtube.com/watch?v=U6rvaWaiZNg](https://www.youtube.com/watch?v=U6rvaWaiZNg)

ITC 5301, Management Information Systems 5

UNIT x STUDY GUIDE
Title

clicking on the three dots below the video title on the right. Closed-captioning can be turned on by clicking the
“cc” icon at the bottom of the video.)

After watching the video and reviewing the case study, answer the video case questions at the end.

– Course Learning Outcomes for Unit VI
– Required Unit Resources
– Unit Lesson

– What Is Knowledge?
– The Business Value of Decision-Making
– Information Systems in Management Decision-Making
– Decision-making Technologies
– Ethical Issues of Information Systems
– Summary
– Reference

– Suggested Unit Resources
– Learning Activities (Nongraded)

**## UnitVIStudyGuide.pdf**

ITC 5301, Management Information Systems 1

Course Learning Outcomes for Unit VI

Upon completion of this unit, students should be able to:

3. Explain how information technology systems influence organizational strategies.
3.4 Explore the role of artificial intelligence (AI) and machine learning in business.
3.5 Analyze how business intelligence and business analytics can support decision-making.

4. Evaluate the prevailing ethical issues of information systems.

4.2 Examine ethical and social issues presented by automated machines.

Required Unit Resources

Chapter 11: Managing Knowledge and Artificial Intelligence

Chapter 12: Enhancing Decision Making

Unit Lesson

What Is Knowledge?

Most of us, at some point in our educational journey, have learned the difference between data, information,
and knowledge. We have discussed data in great detail in this course, so we know that data include the base
transactions of our systems. Those transactions mean little unless we use tools and turn the data into
information. We can discern sales information and inventory information, for example, via organized
categories. What do we do with that information? If we need a simple question answered, we can simply
query the information. For example, we may query a database to see what a specific customer’s annual sales
were for 2014. Also, we may pull a simple report. For example, if you manage all of the customers in New
York City, your report may list the annual sales for all of those customers in New York City. That is
information.

Knowledge is an asset to an organization and can take the form of information that has been put into some
sort of context. It can also take the form of tacit knowledge in the minds of employees. Regardless of the type
of knowledge, organizations need to manage it.

In many organizations, knowledge management is not a high priority, but for those who realize the value,
there are systems that can be used for enterprise-wide management. They help us to manage information
and have capabilities for capturing, storing, retrieving, distributing, and preserving knowledge. There is also
the opportunity for accessing internal information, such as reports, documents, and e-mails. What purpose is
it to have an enterprise knowledge management system? Where is the value for businesses? The answer is
better business processes and decisions!

The Business Value of Decision-Making

We use much of the information and knowledge captured in organizations for the purpose of decision-making.
Sales data are used to make decisions about inventory levels, the purchase of new products, credit levels,
and so on. Not only can you make decisions about an individual customer, but you can also make decisions
about a specific store or make organization-wide decisions. There are decisions made at all levels of the
organization, and some may be small with little impact or large with a business-wide impact. The actual value

UNIT VI STUDY GUIDE
Managing Knowledge, Artificial Intelligence,
and Enhancing Decision-Making

ITC 5301, Management Information Systems 2

UNIT x STUDY GUIDE
Title

to the business is based on many different factors. Regardless, it is important to note that information systems
will likely be involved in almost every decision in some way.

Information Systems in Management Decision-Making

There are different types of decisions used at different levels of management. For example, structured
decisions are fairly routine and not considered to be very risky; lower-level management uses them.
Unstructured decisions require a good amount of insight and judgment. They are more likely to be riskier
decisions and tend to be made by senior management. There are also semi-structured decisions that contain
some of both characteristics and are usually made by middle management. Regardless of the decision type
and role, information systems can support the decision-making process. In fact, in many cases, computer
technology is relied upon pretty heavily for decision-making.

Within the opening case of Chapter 11, “Artificial Intelligence Beats Radiologists in Reading Mammograms” is
a good example that demonstrates how organizations use new tools and technologies to improve their
business performance.

This case study reveals that in 2019, the American Cancer Society reported 268,600 new cases of invasive
breast cancer and predicted 41,760 deaths from breast cancer among women in the United States. Globally,
breast cancer is a leading source of death among women, with 2 million deaths reported. Mammography is
the primary tool used to detect breast cancer. It usually involves two or more x-ray pictures or images of each
breast. Doctors use mammograms to check for breast tumor in women who have no signs or symptoms of the
disease. There are about 33 million screening mammograms performed each year. However, manual tests
miss 20 percent of breast cancers due to false negatives. There is also a seven to 12 percentile of false
positives reported (American Cancer Society, 2019, as cited in Laudon & Laudon, 2022).

Researchers from Google Health, Northwestern University in Chicago, and Britain’s Cancer Research
Imperial Centre and Royal Surrey County Hospital developed a computer algorithm using AI technology to
read the X-ray images. The result is that the new AI-based system was more accurate than the radiologists.
The system was able to reduce false negatives by 9.4 percent and false positives by 5.7 percent. This
research is not yet ready for widespread clinical use. However, it demonstrated the potential advantages of
using AI to assist radiologists in reading mammograms as part of their routine practice (Grady, 2020;
Lichtenstein, 2020, as cited in Laudon & Laudon, 2022).

Decision-making Technologies

Decision support system (DSS). A DSS is an information system that is utilized to assist in the decision-
making process. A DSS consists of several different components, but the most important thing to understand
is that the DSS accepts information from both internal and external sources to make quick decisions under
rapidly changing conditions. The DSS technology provides easy graphical user interfaces and models to aid
in decision-making processes (Laudon & Laudon, 2022).

Business intelligence (BI). BI is a broad term used to describe an infrastructure designed to store, integrate,
process, analyze, and report data. All types of data that are included in this definition include what is referred
to as big data. Big data is simply a term to describe data so large that it does not fit effectively in standard
databases. BI information can be stored in transactional databases, especially as database management
systems improve and become more efficient. More likely, though, the data will be moved to a data warehouse
built appropriately for the organization and its needs. A part of the BI infrastructure includes tools for analytics
and handy user interfaces such as dashboards, reports, and scorecards.

BI offers rich information quickly to the decision makers, and the analytic tools help them to make sense of
that information. You might even say that the analytic tools help us to turn information into knowledge.

One of the more valuable uses for analytics involves forecasting or predictive analytics. Many times,
managers need to be able to use models to predict the outcome of future events. Predictive analytics use
statistical tools and data mining, for example, to predict future trends. One or more predictors may be
changed to see what the outcome might be. A good example might be marketing expenditures. If your
marketing budget is $100,000 per year and you have four product lines, what might be the best way to
allocate those funds? If you have historical data, you can use it along with your predictive variable to predict

ITC 5301, Management Information Systems 3

UNIT x STUDY GUIDE
Title

what the outcome will be. For example, if you spend $50,000 of the marketing allowance on product line A,
what will be your predicted net profits?

The opening case study in Chapter 12, “Big Data and the Internet of Things Drive Precision Agriculture,”
demonstrates how data analytics can be used to help organizations make better decisions more effectively. At
Purdue University’s College of Agriculture, students are learning about data-driven farming. Students and
researchers can study and learn how to improve plant growth and food production processes using an
agriculture-oriented network with advanced Internet of Things (IoT) sensors and devices. This technology has
the potential to improve every process from farm to table.

The IoT network oversees a 1,408-acre farm, analyzing and storing terabytes of data gathered daily from
sensors, cameras, and manual data inputs. With the use of Wi-Fi hotspots, the data from the farm is sent to a
supercomputer. Data is also collected by automated drones and rovers that examine the farm plots and
capture real-time data that are sent to the supercomputer. This project is an example of precision agriculture,
where data collected are analyzed with digital tools to assist with decision-making such as fertilizer
distribution, planting depth, watering cycles, and optimal harvesting times. This project demonstrates that
farmers can produce higher crop yields with less energy and waste.

Information systems are also used for other types of decision support. For example, senior management may
use executive support systems (ESS) to fully understand the performance of the organization. The balanced
scorecard methodology allows for the organization’s strategy and objectives to be operationalized into four
dimensions. This just means it breaks them down into four areas to be measured more effectively. The four
dimensions are financial, customers, business process, and learning and growth.

Another management method is business performance management (BPM), which allows management to
translate an organization’s objectives into operational targets. This means that management will have
understandable and achievable targets to measure against. Finally, there are group decision-support systems
(GDSS), which are interactive systems that allow decision makers to work together to solve problems. It is a
collaborative system that focuses on decision-making tools (Laudon & Laudon, 2022).

Ethical Issues of Information Systems

In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman in
Tempe, Arizona. Since the crash, Arizona has suspended autonomous vehicle testing in the state, and Uber
is not renewing its permit to test self-driving cars in California. The company has also stopped testing
autonomous cars in Pittsburgh and Toronto, and it is unclear when it will be revived.

Even before the accident, Uber’s self-driving cars were having trouble driving through construction zones and
next to tall vehicles like big truck rigs. Uber’s drivers had to intervene far more frequently than drivers in other
autonomous car projects. The Uber accident raised questions about whether autonomous vehicles were even
ready to be tested on public roads and how regulators should deal with this.

While proponents of self-driving cars like Tesla’s Elon Musk envision a self-driving world where almost all
traffic accidents would be eliminated and the elderly and disabled could travel freely, most Americans think
otherwise. A Pew Research Center survey found that most people did not want to ride in self-driving cars and
were unsure if they would make roads more dangerous or safer. In fact, 87% wanted a person always behind
the wheel and ready to take over if something went wrong.

Some pundits predict that in the next few decades, driverless technology will add $7 trillion to the global
economy and save hundreds of thousands of lives. At the same time, it could devastate the auto industry
along with gas stations, taxi drivers, and truckers. People might stop buying cars because services like Uber
using self-driving cars would be cheaper.

This could cause mass unemployment of taxi drivers and large reductions in auto sales. It would also cut
down the need for many parking garages and parking spaces, freeing up valuable real estate for other
purposes. More people might decide to live further from their workplaces because autonomous vehicles
linked to traffic systems would make traffic flow more smoothly and free riders to work, nap, or watch videos
while commuting.

ITC 5301, Management Information Systems 4

UNIT x STUDY GUIDE
Title

Some people will prosper. Most will probably benefit, but many will be left behind. Driverless technology is
estimated to change one in every nine U.S. jobs, although it will also create new jobs. Another consideration
is that the tremendous investment in autonomous vehicles, which is estimated to be around $32 billion
annually, might be better spent on improving public transportation systems like trains and subways. Does
America need more cars in sprawling urban areas where highways are already jammed?

Summary

As you can see from the list of available technologies and decision support tools, information systems are a
key component in their development and use. Technology is ever-changing, and the benefits are evident in
the tools that are available to assist managers.

Reference

Laudon, K. C., & Laudon, J. L. (2022). Management information systems: Managing the digital firm (17th ed.).

Pearson.

Suggested Unit Resources

In order to access the following resources, click the links below.

To reinforce the concepts from this unit, you are encouraged to review the following:

• Chapter 11 PowerPoint Presentation
(PDF for Chapter 11 PowerPoint presentation)

• Chapter 12 PowerPoint Presentation
(PDF for Chapter 12 PowerPoint presentation)

The following video cases mentioned in your eTextbook augment the information presented in the assigned
chapter readings. You are encouraged to review the Chapter 11 and 12 video cases linked below.

A transcript can be accessed for each video within the YouTube player by clicking on the three dots below the
video title on the right. Closed-captioning can be turned on by clicking the “cc” icon at the bottom of each
video.

Alfresco. (2012, June 7). Alfresco tour video [Video]. YouTube.

https://www.youtube.com/watch?v=p266dTL6oJQ

Alfresco. (2012, April 25). Alfresco case study: The New York Philharmonic [Video]. YouTube.

https://www.youtube.com/watch?v=UWI5RZRC56A

IBM. (2011, July 18). IBM Watson: The science behind an answer [Video]. YouTube.

https://www.youtube.com/watch?v=st-_yEvQeK0

IBM Watson. (2014, October 7). IBM Watson: How it works [Video]. YouTube.

https://www.youtube.com/watch?v=U6rvaWaiZNg

Learning Activities (Nongraded)

Nongraded Learning Activities are provided to aid students in their course of study. You do not have to submit
them. If you have questions, contact your instructor for further guidance and information.

Review the following case study, FreshDirect Uses Business Intelligence to Manage its Online Grocery, which
discusses how FreshDirect adopted a comprehensive enterprise data base system from SAP utilizing a
number of business intelligence applications to track orders and customers. There is a link within the case
study to an accompanying video. (A transcript can be accessed for the video within the YouTube player by

[https://online.columbiasouthern.edu/bbcswebdav/xid-150632003_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150632003_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150605739_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150605739_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150632005_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150632005_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150605741_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150605741_1)

[https://www.youtube.com/watch?v=p266dTL6oJQ](https://www.youtube.com/watch?v=p266dTL6oJQ)

[https://www.youtube.com/watch?v=UWI5RZRC56A](https://www.youtube.com/watch?v=UWI5RZRC56A)

[https://www.youtube.com/watch?v=st-_yEvQeK0](https://www.youtube.com/watch?v=st-_yEvQeK0)

[https://www.youtube.com/watch?v=U6rvaWaiZNg](https://www.youtube.com/watch?v=U6rvaWaiZNg)

ITC 5301, Management Information Systems 5

UNIT x STUDY GUIDE
Title

clicking on the three dots below the video title on the right. Closed-captioning can be turned on by clicking the
“cc” icon at the bottom of the video.)

After watching the video and reviewing the case study, answer the video case questions at the end.

– Course Learning Outcomes for Unit VI
– Required Unit Resources
– Unit Lesson

– What Is Knowledge?
– The Business Value of Decision-Making
– Information Systems in Management Decision-Making
– Decision-making Technologies
– Ethical Issues of Information Systems
– Summary
– Reference

– Suggested Unit Resources
– Learning Activities (Nongraded)

**## UnitVIPowerPointPresentation.docx**

**Unit VI PowerPoint Presentation**

**Harnessing the Power of AI for Strategic Decision- Making: Opportunities and Challenges**

The objective of this assignment is to understand and analyze the role of artificial intelligence (AI), business intelligence (BI), and business analytics (BA) in enhancing organizational decision-making processes, while also considering the ethical and social implications.

Using PowerPoint, create a fictional organization facing a strategic decision (e.g., market expansion, product launch, process optimization). Develop a detailed scenario including the hypothetical organization’s background, industry, and specific decision-making challenges.

Propose a plan for how AI, BI, and BA can be integrated to address the organization’s decision-making challenges, by creating visual aids like charts, graphs, or infographics. Create your visuals answering the following:

· Explain the value added to the organization you introduced by implementing advanced information systems solutions.

· Explore the role of artificial intelligence (AI) and machine learning in business and how your organization could benefit from adopting it.

· Analyze how business intelligence and business analytics can support decision-making in your organization.

· Examine ethical and social issues presented by automated machines.

Your presentation should include at least four visuals. Use the speaker notes to expand upon and explain your visuals.

Include a title page and a references page. You are required to use at least two peer-reviewed, academic sources that are no more than 5 years old (one may be your eTextbook). All sources used, must have citations and references properly formatted in APA Style.

**## UnitVIStudyGuide.pdf**

ITC 5301, Management Information Systems 1

Course Learning Outcomes for Unit VI

Upon completion of this unit, students should be able to:

3. Explain how information technology systems influence organizational strategies.
3.4 Explore the role of artificial intelligence (AI) and machine learning in business.
3.5 Analyze how business intelligence and business analytics can support decision-making.

4. Evaluate the prevailing ethical issues of information systems.

4.2 Examine ethical and social issues presented by automated machines.

Required Unit Resources

Chapter 11: Managing Knowledge and Artificial Intelligence

Chapter 12: Enhancing Decision Making

Unit Lesson

What Is Knowledge?

Most of us, at some point in our educational journey, have learned the difference between data, information,
and knowledge. We have discussed data in great detail in this course, so we know that data include the base
transactions of our systems. Those transactions mean little unless we use tools and turn the data into
information. We can discern sales information and inventory information, for example, via organized
categories. What do we do with that information? If we need a simple question answered, we can simply
query the information. For example, we may query a database to see what a specific customer’s annual sales
were for 2014. Also, we may pull a simple report. For example, if you manage all of the customers in New
York City, your report may list the annual sales for all of those customers in New York City. That is
information.

Knowledge is an asset to an organization and can take the form of information that has been put into some
sort of context. It can also take the form of tacit knowledge in the minds of employees. Regardless of the type
of knowledge, organizations need to manage it.

In many organizations, knowledge management is not a high priority, but for those who realize the value,
there are systems that can be used for enterprise-wide management. They help us to manage information
and have capabilities for capturing, storing, retrieving, distributing, and preserving knowledge. There is also
the opportunity for accessing internal information, such as reports, documents, and e-mails. What purpose is
it to have an enterprise knowledge management system? Where is the value for businesses? The answer is
better business processes and decisions!

The Business Value of Decision-Making

We use much of the information and knowledge captured in organizations for the purpose of decision-making.
Sales data are used to make decisions about inventory levels, the purchase of new products, credit levels,
and so on. Not only can you make decisions about an individual customer, but you can also make decisions
about a specific store or make organization-wide decisions. There are decisions made at all levels of the
organization, and some may be small with little impact or large with a business-wide impact. The actual value

UNIT VI STUDY GUIDE
Managing Knowledge, Artificial Intelligence,
and Enhancing Decision-Making

ITC 5301, Management Information Systems 2

UNIT x STUDY GUIDE
Title

to the business is based on many different factors. Regardless, it is important to note that information systems
will likely be involved in almost every decision in some way.

Information Systems in Management Decision-Making

There are different types of decisions used at different levels of management. For example, structured
decisions are fairly routine and not considered to be very risky; lower-level management uses them.
Unstructured decisions require a good amount of insight and judgment. They are more likely to be riskier
decisions and tend to be made by senior management. There are also semi-structured decisions that contain
some of both characteristics and are usually made by middle management. Regardless of the decision type
and role, information systems can support the decision-making process. In fact, in many cases, computer
technology is relied upon pretty heavily for decision-making.

Within the opening case of Chapter 11, “Artificial Intelligence Beats Radiologists in Reading Mammograms” is
a good example that demonstrates how organizations use new tools and technologies to improve their
business performance.

This case study reveals that in 2019, the American Cancer Society reported 268,600 new cases of invasive
breast cancer and predicted 41,760 deaths from breast cancer among women in the United States. Globally,
breast cancer is a leading source of death among women, with 2 million deaths reported. Mammography is
the primary tool used to detect breast cancer. It usually involves two or more x-ray pictures or images of each
breast. Doctors use mammograms to check for breast tumor in women who have no signs or symptoms of the
disease. There are about 33 million screening mammograms performed each year. However, manual tests
miss 20 percent of breast cancers due to false negatives. There is also a seven to 12 percentile of false
positives reported (American Cancer Society, 2019, as cited in Laudon & Laudon, 2022).

Researchers from Google Health, Northwestern University in Chicago, and Britain’s Cancer Research
Imperial Centre and Royal Surrey County Hospital developed a computer algorithm using AI technology to
read the X-ray images. The result is that the new AI-based system was more accurate than the radiologists.
The system was able to reduce false negatives by 9.4 percent and false positives by 5.7 percent. This
research is not yet ready for widespread clinical use. However, it demonstrated the potential advantages of
using AI to assist radiologists in reading mammograms as part of their routine practice (Grady, 2020;
Lichtenstein, 2020, as cited in Laudon & Laudon, 2022).

Decision-making Technologies

Decision support system (DSS). A DSS is an information system that is utilized to assist in the decision-
making process. A DSS consists of several different components, but the most important thing to understand
is that the DSS accepts information from both internal and external sources to make quick decisions under
rapidly changing conditions. The DSS technology provides easy graphical user interfaces and models to aid
in decision-making processes (Laudon & Laudon, 2022).

Business intelligence (BI). BI is a broad term used to describe an infrastructure designed to store, integrate,
process, analyze, and report data. All types of data that are included in this definition include what is referred
to as big data. Big data is simply a term to describe data so large that it does not fit effectively in standard
databases. BI information can be stored in transactional databases, especially as database management
systems improve and become more efficient. More likely, though, the data will be moved to a data warehouse
built appropriately for the organization and its needs. A part of the BI infrastructure includes tools for analytics
and handy user interfaces such as dashboards, reports, and scorecards.

BI offers rich information quickly to the decision makers, and the analytic tools help them to make sense of
that information. You might even say that the analytic tools help us to turn information into knowledge.

One of the more valuable uses for analytics involves forecasting or predictive analytics. Many times,
managers need to be able to use models to predict the outcome of future events. Predictive analytics use
statistical tools and data mining, for example, to predict future trends. One or more predictors may be
changed to see what the outcome might be. A good example might be marketing expenditures. If your
marketing budget is $100,000 per year and you have four product lines, what might be the best way to
allocate those funds? If you have historical data, you can use it along with your predictive variable to predict

ITC 5301, Management Information Systems 3

UNIT x STUDY GUIDE
Title

what the outcome will be. For example, if you spend $50,000 of the marketing allowance on product line A,
what will be your predicted net profits?

The opening case study in Chapter 12, “Big Data and the Internet of Things Drive Precision Agriculture,”
demonstrates how data analytics can be used to help organizations make better decisions more effectively. At
Purdue University’s College of Agriculture, students are learning about data-driven farming. Students and
researchers can study and learn how to improve plant growth and food production processes using an
agriculture-oriented network with advanced Internet of Things (IoT) sensors and devices. This technology has
the potential to improve every process from farm to table.

The IoT network oversees a 1,408-acre farm, analyzing and storing terabytes of data gathered daily from
sensors, cameras, and manual data inputs. With the use of Wi-Fi hotspots, the data from the farm is sent to a
supercomputer. Data is also collected by automated drones and rovers that examine the farm plots and
capture real-time data that are sent to the supercomputer. This project is an example of precision agriculture,
where data collected are analyzed with digital tools to assist with decision-making such as fertilizer
distribution, planting depth, watering cycles, and optimal harvesting times. This project demonstrates that
farmers can produce higher crop yields with less energy and waste.

Information systems are also used for other types of decision support. For example, senior management may
use executive support systems (ESS) to fully understand the performance of the organization. The balanced
scorecard methodology allows for the organization’s strategy and objectives to be operationalized into four
dimensions. This just means it breaks them down into four areas to be measured more effectively. The four
dimensions are financial, customers, business process, and learning and growth.

Another management method is business performance management (BPM), which allows management to
translate an organization’s objectives into operational targets. This means that management will have
understandable and achievable targets to measure against. Finally, there are group decision-support systems
(GDSS), which are interactive systems that allow decision makers to work together to solve problems. It is a
collaborative system that focuses on decision-making tools (Laudon & Laudon, 2022).

Ethical Issues of Information Systems

In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman in
Tempe, Arizona. Since the crash, Arizona has suspended autonomous vehicle testing in the state, and Uber
is not renewing its permit to test self-driving cars in California. The company has also stopped testing
autonomous cars in Pittsburgh and Toronto, and it is unclear when it will be revived.

Even before the accident, Uber’s self-driving cars were having trouble driving through construction zones and
next to tall vehicles like big truck rigs. Uber’s drivers had to intervene far more frequently than drivers in other
autonomous car projects. The Uber accident raised questions about whether autonomous vehicles were even
ready to be tested on public roads and how regulators should deal with this.

While proponents of self-driving cars like Tesla’s Elon Musk envision a self-driving world where almost all
traffic accidents would be eliminated and the elderly and disabled could travel freely, most Americans think
otherwise. A Pew Research Center survey found that most people did not want to ride in self-driving cars and
were unsure if they would make roads more dangerous or safer. In fact, 87% wanted a person always behind
the wheel and ready to take over if something went wrong.

Some pundits predict that in the next few decades, driverless technology will add $7 trillion to the global
economy and save hundreds of thousands of lives. At the same time, it could devastate the auto industry
along with gas stations, taxi drivers, and truckers. People might stop buying cars because services like Uber
using self-driving cars would be cheaper.

This could cause mass unemployment of taxi drivers and large reductions in auto sales. It would also cut
down the need for many parking garages and parking spaces, freeing up valuable real estate for other
purposes. More people might decide to live further from their workplaces because autonomous vehicles
linked to traffic systems would make traffic flow more smoothly and free riders to work, nap, or watch videos
while commuting.

ITC 5301, Management Information Systems 4

UNIT x STUDY GUIDE
Title

Some people will prosper. Most will probably benefit, but many will be left behind. Driverless technology is
estimated to change one in every nine U.S. jobs, although it will also create new jobs. Another consideration
is that the tremendous investment in autonomous vehicles, which is estimated to be around $32 billion
annually, might be better spent on improving public transportation systems like trains and subways. Does
America need more cars in sprawling urban areas where highways are already jammed?

Summary

As you can see from the list of available technologies and decision support tools, information systems are a
key component in their development and use. Technology is ever-changing, and the benefits are evident in
the tools that are available to assist managers.

Reference

Laudon, K. C., & Laudon, J. L. (2022). Management information systems: Managing the digital firm (17th ed.).

Pearson.

Suggested Unit Resources

In order to access the following resources, click the links below.

To reinforce the concepts from this unit, you are encouraged to review the following:

• Chapter 11 PowerPoint Presentation
(PDF for Chapter 11 PowerPoint presentation)

• Chapter 12 PowerPoint Presentation
(PDF for Chapter 12 PowerPoint presentation)

The following video cases mentioned in your eTextbook augment the information presented in the assigned
chapter readings. You are encouraged to review the Chapter 11 and 12 video cases linked below.

A transcript can be accessed for each video within the YouTube player by clicking on the three dots below the
video title on the right. Closed-captioning can be turned on by clicking the “cc” icon at the bottom of each
video.

Alfresco. (2012, June 7). Alfresco tour video [Video]. YouTube.

https://www.youtube.com/watch?v=p266dTL6oJQ

Alfresco. (2012, April 25). Alfresco case study: The New York Philharmonic [Video]. YouTube.

https://www.youtube.com/watch?v=UWI5RZRC56A

IBM. (2011, July 18). IBM Watson: The science behind an answer [Video]. YouTube.

https://www.youtube.com/watch?v=st-_yEvQeK0

IBM Watson. (2014, October 7). IBM Watson: How it works [Video]. YouTube.

https://www.youtube.com/watch?v=U6rvaWaiZNg

Learning Activities (Nongraded)

Nongraded Learning Activities are provided to aid students in their course of study. You do not have to submit
them. If you have questions, contact your instructor for further guidance and information.

Review the following case study, FreshDirect Uses Business Intelligence to Manage its Online Grocery, which
discusses how FreshDirect adopted a comprehensive enterprise data base system from SAP utilizing a
number of business intelligence applications to track orders and customers. There is a link within the case
study to an accompanying video. (A transcript can be accessed for the video within the YouTube player by

[https://online.columbiasouthern.edu/bbcswebdav/xid-150632003_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150632003_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150605739_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150605739_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150632005_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150632005_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150605741_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150605741_1)

[https://www.youtube.com/watch?v=p266dTL6oJQ](https://www.youtube.com/watch?v=p266dTL6oJQ)

[https://www.youtube.com/watch?v=UWI5RZRC56A](https://www.youtube.com/watch?v=UWI5RZRC56A)

[https://www.youtube.com/watch?v=st-_yEvQeK0](https://www.youtube.com/watch?v=st-_yEvQeK0)

[https://www.youtube.com/watch?v=U6rvaWaiZNg](https://www.youtube.com/watch?v=U6rvaWaiZNg)

ITC 5301, Management Information Systems 5

UNIT x STUDY GUIDE
Title

clicking on the three dots below the video title on the right. Closed-captioning can be turned on by clicking the
“cc” icon at the bottom of the video.)

After watching the video and reviewing the case study, answer the video case questions at the end.

– Course Learning Outcomes for Unit VI
– Required Unit Resources
– Unit Lesson

– What Is Knowledge?
– The Business Value of Decision-Making
– Information Systems in Management Decision-Making
– Decision-making Technologies
– Ethical Issues of Information Systems
– Summary
– Reference

– Suggested Unit Resources
– Learning Activities (Nongraded)

**## UnitVIPowerPointPresentation.docx**

**Unit VI PowerPoint Presentation**

**Harnessing the Power of AI for Strategic Decision- Making: Opportunities and Challenges**

The objective of this assignment is to understand and analyze the role of artificial intelligence (AI), business intelligence (BI), and business analytics (BA) in enhancing organizational decision-making processes, while also considering the ethical and social implications.

Using PowerPoint, create a fictional organization facing a strategic decision (e.g., market expansion, product launch, process optimization). Develop a detailed scenario including the hypothetical organization’s background, industry, and specific decision-making challenges.

Propose a plan for how AI, BI, and BA can be integrated to address the organization’s decision-making challenges, by creating visual aids like charts, graphs, or infographics. Create your visuals answering the following:

· Explain the value added to the organization you introduced by implementing advanced information systems solutions.

· Explore the role of artificial intelligence (AI) and machine learning in business and how your organization could benefit from adopting it.

· Analyze how business intelligence and business analytics can support decision-making in your organization.

· Examine ethical and social issues presented by automated machines.

Your presentation should include at least four visuals. Use the speaker notes to expand upon and explain your visuals.

Include a title page and a references page. You are required to use at least two peer-reviewed, academic sources that are no more than 5 years old (one may be your eTextbook). All sources used, must have citations and references properly formatted in APA Style.

**## UnitVIStudyGuide.pdf**

ITC 5301, Management Information Systems 1

Course Learning Outcomes for Unit VI

Upon completion of this unit, students should be able to:

3. Explain how information technology systems influence organizational strategies.
3.4 Explore the role of artificial intelligence (AI) and machine learning in business.
3.5 Analyze how business intelligence and business analytics can support decision-making.

4. Evaluate the prevailing ethical issues of information systems.

4.2 Examine ethical and social issues presented by automated machines.

Required Unit Resources

Chapter 11: Managing Knowledge and Artificial Intelligence

Chapter 12: Enhancing Decision Making

Unit Lesson

What Is Knowledge?

Most of us, at some point in our educational journey, have learned the difference between data, information,
and knowledge. We have discussed data in great detail in this course, so we know that data include the base
transactions of our systems. Those transactions mean little unless we use tools and turn the data into
information. We can discern sales information and inventory information, for example, via organized
categories. What do we do with that information? If we need a simple question answered, we can simply
query the information. For example, we may query a database to see what a specific customer’s annual sales
were for 2014. Also, we may pull a simple report. For example, if you manage all of the customers in New
York City, your report may list the annual sales for all of those customers in New York City. That is
information.

Knowledge is an asset to an organization and can take the form of information that has been put into some
sort of context. It can also take the form of tacit knowledge in the minds of employees. Regardless of the type
of knowledge, organizations need to manage it.

In many organizations, knowledge management is not a high priority, but for those who realize the value,
there are systems that can be used for enterprise-wide management. They help us to manage information
and have capabilities for capturing, storing, retrieving, distributing, and preserving knowledge. There is also
the opportunity for accessing internal information, such as reports, documents, and e-mails. What purpose is
it to have an enterprise knowledge management system? Where is the value for businesses? The answer is
better business processes and decisions!

The Business Value of Decision-Making

We use much of the information and knowledge captured in organizations for the purpose of decision-making.
Sales data are used to make decisions about inventory levels, the purchase of new products, credit levels,
and so on. Not only can you make decisions about an individual customer, but you can also make decisions
about a specific store or make organization-wide decisions. There are decisions made at all levels of the
organization, and some may be small with little impact or large with a business-wide impact. The actual value

UNIT VI STUDY GUIDE
Managing Knowledge, Artificial Intelligence,
and Enhancing Decision-Making

ITC 5301, Management Information Systems 2

UNIT x STUDY GUIDE
Title

to the business is based on many different factors. Regardless, it is important to note that information systems
will likely be involved in almost every decision in some way.

Information Systems in Management Decision-Making

There are different types of decisions used at different levels of management. For example, structured
decisions are fairly routine and not considered to be very risky; lower-level management uses them.
Unstructured decisions require a good amount of insight and judgment. They are more likely to be riskier
decisions and tend to be made by senior management. There are also semi-structured decisions that contain
some of both characteristics and are usually made by middle management. Regardless of the decision type
and role, information systems can support the decision-making process. In fact, in many cases, computer
technology is relied upon pretty heavily for decision-making.

Within the opening case of Chapter 11, “Artificial Intelligence Beats Radiologists in Reading Mammograms” is
a good example that demonstrates how organizations use new tools and technologies to improve their
business performance.

This case study reveals that in 2019, the American Cancer Society reported 268,600 new cases of invasive
breast cancer and predicted 41,760 deaths from breast cancer among women in the United States. Globally,
breast cancer is a leading source of death among women, with 2 million deaths reported. Mammography is
the primary tool used to detect breast cancer. It usually involves two or more x-ray pictures or images of each
breast. Doctors use mammograms to check for breast tumor in women who have no signs or symptoms of the
disease. There are about 33 million screening mammograms performed each year. However, manual tests
miss 20 percent of breast cancers due to false negatives. There is also a seven to 12 percentile of false
positives reported (American Cancer Society, 2019, as cited in Laudon & Laudon, 2022).

Researchers from Google Health, Northwestern University in Chicago, and Britain’s Cancer Research
Imperial Centre and Royal Surrey County Hospital developed a computer algorithm using AI technology to
read the X-ray images. The result is that the new AI-based system was more accurate than the radiologists.
The system was able to reduce false negatives by 9.4 percent and false positives by 5.7 percent. This
research is not yet ready for widespread clinical use. However, it demonstrated the potential advantages of
using AI to assist radiologists in reading mammograms as part of their routine practice (Grady, 2020;
Lichtenstein, 2020, as cited in Laudon & Laudon, 2022).

Decision-making Technologies

Decision support system (DSS). A DSS is an information system that is utilized to assist in the decision-
making process. A DSS consists of several different components, but the most important thing to understand
is that the DSS accepts information from both internal and external sources to make quick decisions under
rapidly changing conditions. The DSS technology provides easy graphical user interfaces and models to aid
in decision-making processes (Laudon & Laudon, 2022).

Business intelligence (BI). BI is a broad term used to describe an infrastructure designed to store, integrate,
process, analyze, and report data. All types of data that are included in this definition include what is referred
to as big data. Big data is simply a term to describe data so large that it does not fit effectively in standard
databases. BI information can be stored in transactional databases, especially as database management
systems improve and become more efficient. More likely, though, the data will be moved to a data warehouse
built appropriately for the organization and its needs. A part of the BI infrastructure includes tools for analytics
and handy user interfaces such as dashboards, reports, and scorecards.

BI offers rich information quickly to the decision makers, and the analytic tools help them to make sense of
that information. You might even say that the analytic tools help us to turn information into knowledge.

One of the more valuable uses for analytics involves forecasting or predictive analytics. Many times,
managers need to be able to use models to predict the outcome of future events. Predictive analytics use
statistical tools and data mining, for example, to predict future trends. One or more predictors may be
changed to see what the outcome might be. A good example might be marketing expenditures. If your
marketing budget is $100,000 per year and you have four product lines, what might be the best way to
allocate those funds? If you have historical data, you can use it along with your predictive variable to predict

ITC 5301, Management Information Systems 3

UNIT x STUDY GUIDE
Title

what the outcome will be. For example, if you spend $50,000 of the marketing allowance on product line A,
what will be your predicted net profits?

The opening case study in Chapter 12, “Big Data and the Internet of Things Drive Precision Agriculture,”
demonstrates how data analytics can be used to help organizations make better decisions more effectively. At
Purdue University’s College of Agriculture, students are learning about data-driven farming. Students and
researchers can study and learn how to improve plant growth and food production processes using an
agriculture-oriented network with advanced Internet of Things (IoT) sensors and devices. This technology has
the potential to improve every process from farm to table.

The IoT network oversees a 1,408-acre farm, analyzing and storing terabytes of data gathered daily from
sensors, cameras, and manual data inputs. With the use of Wi-Fi hotspots, the data from the farm is sent to a
supercomputer. Data is also collected by automated drones and rovers that examine the farm plots and
capture real-time data that are sent to the supercomputer. This project is an example of precision agriculture,
where data collected are analyzed with digital tools to assist with decision-making such as fertilizer
distribution, planting depth, watering cycles, and optimal harvesting times. This project demonstrates that
farmers can produce higher crop yields with less energy and waste.

Information systems are also used for other types of decision support. For example, senior management may
use executive support systems (ESS) to fully understand the performance of the organization. The balanced
scorecard methodology allows for the organization’s strategy and objectives to be operationalized into four
dimensions. This just means it breaks them down into four areas to be measured more effectively. The four
dimensions are financial, customers, business process, and learning and growth.

Another management method is business performance management (BPM), which allows management to
translate an organization’s objectives into operational targets. This means that management will have
understandable and achievable targets to measure against. Finally, there are group decision-support systems
(GDSS), which are interactive systems that allow decision makers to work together to solve problems. It is a
collaborative system that focuses on decision-making tools (Laudon & Laudon, 2022).

Ethical Issues of Information Systems

In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman in
Tempe, Arizona. Since the crash, Arizona has suspended autonomous vehicle testing in the state, and Uber
is not renewing its permit to test self-driving cars in California. The company has also stopped testing
autonomous cars in Pittsburgh and Toronto, and it is unclear when it will be revived.

Even before the accident, Uber’s self-driving cars were having trouble driving through construction zones and
next to tall vehicles like big truck rigs. Uber’s drivers had to intervene far more frequently than drivers in other
autonomous car projects. The Uber accident raised questions about whether autonomous vehicles were even
ready to be tested on public roads and how regulators should deal with this.

While proponents of self-driving cars like Tesla’s Elon Musk envision a self-driving world where almost all
traffic accidents would be eliminated and the elderly and disabled could travel freely, most Americans think
otherwise. A Pew Research Center survey found that most people did not want to ride in self-driving cars and
were unsure if they would make roads more dangerous or safer. In fact, 87% wanted a person always behind
the wheel and ready to take over if something went wrong.

Some pundits predict that in the next few decades, driverless technology will add $7 trillion to the global
economy and save hundreds of thousands of lives. At the same time, it could devastate the auto industry
along with gas stations, taxi drivers, and truckers. People might stop buying cars because services like Uber
using self-driving cars would be cheaper.

This could cause mass unemployment of taxi drivers and large reductions in auto sales. It would also cut
down the need for many parking garages and parking spaces, freeing up valuable real estate for other
purposes. More people might decide to live further from their workplaces because autonomous vehicles
linked to traffic systems would make traffic flow more smoothly and free riders to work, nap, or watch videos
while commuting.

ITC 5301, Management Information Systems 4

UNIT x STUDY GUIDE
Title

Some people will prosper. Most will probably benefit, but many will be left behind. Driverless technology is
estimated to change one in every nine U.S. jobs, although it will also create new jobs. Another consideration
is that the tremendous investment in autonomous vehicles, which is estimated to be around $32 billion
annually, might be better spent on improving public transportation systems like trains and subways. Does
America need more cars in sprawling urban areas where highways are already jammed?

Summary

As you can see from the list of available technologies and decision support tools, information systems are a
key component in their development and use. Technology is ever-changing, and the benefits are evident in
the tools that are available to assist managers.

Reference

Laudon, K. C., & Laudon, J. L. (2022). Management information systems: Managing the digital firm (17th ed.).

Pearson.

Suggested Unit Resources

In order to access the following resources, click the links below.

To reinforce the concepts from this unit, you are encouraged to review the following:

• Chapter 11 PowerPoint Presentation
(PDF for Chapter 11 PowerPoint presentation)

• Chapter 12 PowerPoint Presentation
(PDF for Chapter 12 PowerPoint presentation)

The following video cases mentioned in your eTextbook augment the information presented in the assigned
chapter readings. You are encouraged to review the Chapter 11 and 12 video cases linked below.

A transcript can be accessed for each video within the YouTube player by clicking on the three dots below the
video title on the right. Closed-captioning can be turned on by clicking the “cc” icon at the bottom of each
video.

Alfresco. (2012, June 7). Alfresco tour video [Video]. YouTube.

https://www.youtube.com/watch?v=p266dTL6oJQ

Alfresco. (2012, April 25). Alfresco case study: The New York Philharmonic [Video]. YouTube.

https://www.youtube.com/watch?v=UWI5RZRC56A

IBM. (2011, July 18). IBM Watson: The science behind an answer [Video]. YouTube.

https://www.youtube.com/watch?v=st-_yEvQeK0

IBM Watson. (2014, October 7). IBM Watson: How it works [Video]. YouTube.

https://www.youtube.com/watch?v=U6rvaWaiZNg

Learning Activities (Nongraded)

Nongraded Learning Activities are provided to aid students in their course of study. You do not have to submit
them. If you have questions, contact your instructor for further guidance and information.

Review the following case study, FreshDirect Uses Business Intelligence to Manage its Online Grocery, which
discusses how FreshDirect adopted a comprehensive enterprise data base system from SAP utilizing a
number of business intelligence applications to track orders and customers. There is a link within the case
study to an accompanying video. (A transcript can be accessed for the video within the YouTube player by

[https://online.columbiasouthern.edu/bbcswebdav/xid-150632003_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150632003_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150605739_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150605739_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150632005_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150632005_1)

[https://online.columbiasouthern.edu/bbcswebdav/xid-150605741_1](https://online.columbiasouthern.edu/bbcswebdav/xid-150605741_1)

[https://www.youtube.com/watch?v=p266dTL6oJQ](https://www.youtube.com/watch?v=p266dTL6oJQ)

[https://www.youtube.com/watch?v=UWI5RZRC56A](https://www.youtube.com/watch?v=UWI5RZRC56A)

[https://www.youtube.com/watch?v=st-_yEvQeK0](https://www.youtube.com/watch?v=st-_yEvQeK0)

[https://www.youtube.com/watch?v=U6rvaWaiZNg](https://www.youtube.com/watch?v=U6rvaWaiZNg)

ITC 5301, Management Information Systems 5

UNIT x STUDY GUIDE
Title

clicking on the three dots below the video title on the right. Closed-captioning can be turned on by clicking the
“cc” icon at the bottom of the video.)

After watching the video and reviewing the case study, answer the video case questions at the end.

– Course Learning Outcomes for Unit VI
– Required Unit Resources
– Unit Lesson

– What Is Knowledge?
– The Business Value of Decision-Making
– Information Systems in Management Decision-Making
– Decision-making Technologies
– Ethical Issues of Information Systems
– Summary
– Reference

– Suggested Unit Resources
– Learning Activities (Nongraded)

– 1
– 2