MICS#6

MBA 5401, 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. 3.1 Explore the role of artificial intelligence (AI) and machine learning in business. 3.2 Analyze how business intelligence and business analytics can support decision-making.

4. Evaluate the prevailing ethical issues of information systems.

4.1 Examine ethical and social issues presented by automated machines.

Course/Unit Learning Outcomes

Learning Activity

3.1 Unit Lesson Chapter 11, pp. 418–447, 453–455 Unit VI Case Study

3.2 Unit Lesson Chapter 12, pp. 458–482, 487–489 Unit VI Case Study

4.1 Unit Lesson Chapter 11, pp. 418–447, 453–455 Unit VI Case Study

Required Unit Resources Chapter 11: Managing Knowledge and Artificial Intelligence, pp. 418–447, 453–455 Chapter 12: Enhancing Decision Making, pp. 458–482, 487–489

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.

UNIT VI STUDY GUIDE

Managing Knowledge, Artificial Intelligence, and Enhancing Decision-Making

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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 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, “Machine Learning Helps Akershus University Hospital Make Better Treatment Decisions,” a good example is provided regarding how organizations use new tools and technologies to improve their business performance. Today, there are many healthcare organizations struggling to manage large volumes of patient and treatment data, and the university was no exception. The data that they had amassed was in unstructured and textual reports that did not provide information in a meaningful way. To solve this problem, the university implemented IBM Watson Explorer, an artificial intelligence technology that could analyze structured and unstructured data and present the information in a useful way. Because Watson is a cognitive computing platform, it could analyze all of the data to discover trends and patterns that were previously extremely difficult to discern. This technology uses natural language processing to evaluate the data and machine learning algorithms to enhance search results. Machine learning is a process by which the software identifies patterns in databases. This is often called data mining. Watson can sort through all of the data, interpret speech and text, identify nuances of meaning and context, determine conclusions, answer questions, and learn from its experiences. In this context, Watson was able to learn medical terminology and understand how the language was used. Once the software was able to do this, it achieved a 99% accuracy level on its ability to recognize and classify the data. As a result, Watson determined that the frequency in which computerized tomography (CT) scanning was used at the hospital was acceptable and that the hospital was able to successfully balance the probability of gains against any harmful effects. Without Watson, it would have taken a team of professionals months or years to evaluate the data and make sense of it all. Some technologies that aid in decision-making are discussed in the sections below. 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, 2020).

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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 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, 2020). 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.

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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. 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. (2020). Management information systems: Managing the digital firm (16th ed.).

Pearson.

Suggested Unit Resources In order to access the following resources, click the links below. To reinforce the concepts from this unit, view the Chapter 11 Presentation (PDF for Chapter 11 Presentation). To reinforce the concepts from this unit, view the Chapter 12 Presentation (PDF for Chapter 12 Presentation). Your textbook has video cases that correlate with the information being presented in the assigned chapter readings. You are encouraged to review the video cases relating to Chapter 11 below. Alfresco. (2012, April 25). Alfresco case study: The New York Philharmonic [Video]. YouTube.

https://www.youtube.com/watch?v=UWI5RZRC56A Transcript for Alfresco Case Study: The New York Philharmonic video Alfresco. (2012, June 7). Alfresco tour video [Video]. YouTube.

https://www.youtube.com/watch?v=p266dTL6oJQ Transcript for Alfresco Tour Video video

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IBM. (2011, July 18). IBM Watson: The science behind an answer [Video]. YouTube. https://www.youtube.com/watch?v=DywO4zksfXw

Transcript for IBM Watson: The Science Behind an Answer video IBM Watson. (2014, October 7). IBM Watson: How it works [Video]. YouTube.

https://www.youtube.com/watch?v=_Xcmh1LQB9I Transcript for IBM Watson: How It Works video You are encouraged to review the video cases relating to Chapter 12 below. GE Digital. (2016, May 26). Predix: GE digital's platform for the industrial Internet [Video]. YouTube.

https://www.youtube.com/watch?v=6mb0gnBUmNk&t=1s Transcript for Predix: GE Digital's Platform for the Industrial Internet video GE's Power Services Business. (2016, April 14). Predix and the digital power plant - PSEG customer story

[Video]. YouTube. https://www.youtube.com/watch?v=WFsmJfVWCHk Transcript for Predix and the Digital Power Plant - PSEG Customer Story video