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Capital One: A Big Bank Heist from the Cloud Capital One Financial Corporation is an American bank holding company specializing in credit cards, auto loans, banking, and savings accounts. It is the eleventh largest bank in the United States in terms of assets and an aggressive user of information technology to drive its business. Capital One was an early adopter of cloud computing and a major client of Amazon Web Services (AWS). Capital One has been trying to move more critical parts of its IT infrastructure to Amazon’s cloud infrastructure in order to focus on building consumer applications and other needs. On July 29, 2019, Capital One and its customers received some very bad news. Capital One had been breached, exposing over 140,000 Social Security numbers, 80,000 bank account numbers, tens of millions of credit card applications, and one million Canadian social insurance numbers (equivalent to Social Security numbers in the US). It was one of the largest thefts of data ever from a bank. The culprit turned out to be Paige Thompson, a former employee of Amazon Web Services, which hosted the Capital One database that was breached. Thompson was arrested in Seattle and charged with one count of computer fraud and abuse. She had worked for the same server business that court papers said Capital One was using. Thompson could face up to five years in prison and a $250,000 fine. The bank believed it was unlikely that Thompson disseminated the information or used it for fraud. But it will still cost the bank up to $150 million, including paying for credit monitoring of affected customers. Amazon Web Services hosts remote servers that organizations use to store their data. Large enterprises such as Capital One build their own web applications using Amazon’s cloud servers and data storage services data so they can use the information for their specific needs. The F.B.I. agent investigating the breach reported that Ms. Thompson had gained access to Capital One’s sensitive data through a “misconfiguration” of a firewall on a web application. (A firewall monitors incoming and outgoing network traffic and blocks unauthorized access.) This allowed her to communicate with the server where Capital One was storing its data and customer files. Capital One stated it had immediately fixed the configuration vulnerability once it had been detected. Amazon said its customers fully control the applications they build and that it had found no evidence that its underlying cloud services had been compromised. Thompson was able to access and steal this sensitive information only because Capital One had misconfigured its Amazon server. Thompson could then trick a system in the cloud to uncover the credentials she needed to access Capital One’s customer records. Thompson’s crime was considered an insider threat, since she had worked at Amazon years earlier. However, outsiders also try to search for and exploit this type of misconfiguration, and server misconfigurations are commonplace. Misconfigurations are also easily fixed, so many do not consider them a breach. Sometimes it’s difficult to determine whether tinkering with misconfigurations represents a criminal activity or security research. Thompson was able to tap into Amazon’s metadata service, which has the credentials and other data required to manage servers in the cloud. Ms. Thompson ran a scan of the Internet to identify vulnerable computers that could provide access to a company’s internal networks. She found a computer managing communications between Capital One’s cloud and the public Internet that had been misconfigured, with weak security settings. Through that opening Thompson was able to request the credentials required to find and read Capital One data stored in the cloud from the metadata service. Once Thompson located the Capital One data, she was able to download them without triggering any alerts. Thompson also boasted online that she had used the same techniques to access large amounts of online data from other organizations. Amazon has stated that none of its services, including the metadata service, were the cause of the break-in and that AWS offers monitoring tools for detecting this type of incident. It is unclear why none of these alerting tools triggered an alarm when Thompson was hacking into Capital One. Thompson began hacking Capital One on March 12, 2019, but went undetected until an outside researcher tipped off Capital One 127 days later. According to C. J. Moses, deputy chief information security officer for AWS, Amazon restricts most staff members from accessing its broader internal infrastructure in order to protect against “witting or unwitting” data breaches. Security professionals have known about misconfiguration problems and the ability to steal credentials from the metadata service since at least 2014. Amazon believes it is the customer’s responsibility to solve them. Some customers have failed to do so. When security researcher Brenton Thomas conducted an Internet scan in February 2019, he found more than 800 Amazon accounts that allowed similar access to the metadata service. (Amazon’s cloud computing service has over one million users.) But Thomas also found other cloud computing companies with misconfigured services as well, including Microsoft’s Azure cloud. Whatever the cloud service, the pool of talent capable of launching similar attacks is expanding. Given the nature of cloud services, any person who has worked on developing technology at any of the major cloud computing companies can learn how these systems work inpractice. Capital One had a reputation for strong cloud security. The bank had conducted extensive due diligence before deciding to move to cloud computing in 2015. However, before the giant data breach, Capital One employees had raised concerns internally about high turnover in the company’s cybersecurity unit and tardiness in installing some software to help spot and defend against hacks. The cybersecurity unit is responsible for ensuring Capital One’s firewalls are properly configured and for scanning the Internet for evidence of a data breach. In recent years there have been many changes among senior leaders and staffers. About a third of Capital One’s cybersecurity employees left the company in 2018. Sources: Robert McMillan, “How the Accused Capital One Hacker Stole Reams of Data from the Cloud,” Wall Street Journal, August 4, 2019; Emily Flitter and Karen Weiser, “Capital One Data Breach Compromises Data of Over 100 Million,” New York Times, July 29, 2019; James Randle and Catherine Stupp, “Capital One Breach Highlights Dangers of Insider Threats,” Wall Street Journal, July 31, 2019. Peter Rudegeair, AnnaMaria Andriotis, and David Benoit, “Capital One Hack Hits the Reputation of a Tech-Savvy Bank,” Wall Street Journal, July 31, 2019.
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Does Big Data Provide the Answer? Today’s companies are dealing with an avalanche of data from social media, search, and sensors, as well as from traditional sources. According to one estimate, 2.5 quintillion bytes of data per day are generated around the world. Making sense of “big data” to improve decision making and business performance has become one of the primary opportunities for organizations of all shapes and sizes, but it also represents big challenges. Businesses such as Amazon, YouTube, and Spotify have flourished by analyzing the big data they collect about customer interests and purchases to create millions of personalized recommendations for books, films, and music. A number of online services analyze big data to help consumers, including services for finding the lowest price on autos, computers, mobile phone plans, clothing, airfare, hotel rooms, and many other types of goods and services. Big data is also providing benefits in sports (see the Interactive Session on Management), education, science, health care, and law enforcement. Healthcare companies are currently analyzing big data to determine the most effective and economical treatments for chronic illnesses and common diseases and provide personalized care recommendations to patients. For example, the state of Rhode Island has been using InterSystems’ HealthShare Active Analytics tool to collect and analyze patient data on a statewide level. The state’s Quality Institute found that about 10 percent of major lab tests performed in over 25 percent of the state’s population were medically unnecessary—a discovery that has since helped Rhode Island tighten spending as well as improve quality of care. Big data analytics are helping researchers pinpoint how variations among patients and treatments influences health outcomes. For instance, big data’s granularity could help experts detect and diagnose multiple variants of asthma, pointing physicians to the precise treatment plan called for by each patient’s unique case. There are limits to using big data. A number of companies have rushed to start big data projects without first establishing a business goal for this new information or key performance metrics to measure success. Swimming in numbers doesn’t necessarily mean that the right information is being collected or that people will make smarter decisions. Experts in big data analysis believe that too many companies, seduced by the promise of big data, jump into big data projects with nothing to show for their efforts. They start amassing mountains of data with no clear objective or understanding of exactly how analyzing big data will achieve their goal or what questions they are trying to answer. Organizations also won’t benefit from big data that has not been properly cleansed, organized, and managed—think data quality. Big Data does not always reflect emotions or intuitive feelings. For example, when LEGO faced bankruptcy in 2002 to 2003, the company used big data to determine that Millennials have short attention spans and easily get bored. The message from the data led LEGO to de-emphasize their small iconic bricks in favor of large simplistic building blocks. This change only accelerated LEGO’s decline, so the company decided to go into consumers’ homes to try and reconnect with once-loyal customers. After meeting with an 11-year-old German boy, LEGO discovered that for children, playing and showing mastery in something were more valuable than receiving instant gratification. LEGO then pivoted again to emerge after its successful 2014 movie into the world’s largest toy maker. Patterns and trends can sometimes be misleading. Huge volumes of data do not necessarily provide more reliable insights. Sometimes the data being analyzed are not a truly representative sample of the data required. For example, election pollsters in the United States have struggled to obtain representative samples of the population because a majority of people do not have landline phones. It is more time-consuming and expensive for pollsters to contact mobile phone users, which now constitute 75 percent of some samples. U.S. law bans cell phone autodialing, so pollsters have to dial numbers by hand individually and make more calls, since mobile users tend to screen out unknown callers. Opinions on Twitter do not reflect the opinions of the U.S. population as a whole. The elderly, poor people, or introverts, who tend not to use social media—or even computers—often get excluded. Although big data is very good at detecting correlations, especially subtle correlations that an analysis of smaller data sets might miss, big data analysis doesn’t necessarily show causation or which correlations are meaningful. For example, examining big data might show that the decline in United States crime rate was highly correlated with the decline in the market share of video rental stores such as Blockbuster. But that doesn’t necessarily mean there is any meaningful connection between the two phenomena. Data analysts need some business knowledge of the problem they are trying to solve with big data. Just because something can be measured doesn’t mean it should be measured. Suppose, for instance, that a large company wants to measure its website traffic in relation to the number of mentions on Twitter. It builds a digital dashboard to display the results continuously. In the past, the company had generated most of its sales leads and eventual sales from trade shows and conferences. Switching to Twitter mentions as the key metric to measure changes the sales department’s focus. The department pours its energy and resources into monitoring website clicks and social media traffic, which produce many unqualified leads that never lead to sales. All data sets and data-driven forecasting models reflect the biases of the people selecting the data and performing the analysis. Google developed what it thought was a leading-edge algorithm using data it collected from web searches to determine exactly how many people had influenza and how the disease was spreading. It tried to calculate the number of people with flu in the United States by relating people’s location to flu-related search queries on Google. Google consistently overestimated flu rates, when compared to conventional data collected afterward by the U.S. Centers for Disease Control (CDC). Several scientists suggested that Google was “tricked” by widespread media coverage of that year’s severe flu season in the United States, which was further amplified by social media coverage. The model developed for forecasting flu trends was based on a flawed assumption—that the incidence of flu-related searches on Googles was a precise indicator of the number of people who actually came down with the flu. Google’s algorithm only looked at numbers, not the context of the search results. The New York Police Department (NYPD) recently developed a tool called Patternizr, which uses pattern recognition to identify potential criminals. The software searches through hundreds of thousands of crime records across 77 precincts in the NYPD database to find a series of crimes likely to have been committed by the same individual or individuals, based on a set of identifying characteristics. In the past, analysts had to manually review reports to identify patterns, a very time-consuming and inefficient process. Some experts worry that Patternizr inadvertently perpetuates bias. The NYPD used 10 years of manually identified pattern data to train Patternizr, removing attributes such as gender, race, and specific location from the data. Nevertheless such efforts may not eliminate racial and gender bias in Patternizr if race and gender played any role in past police actions used to model predictions. According to Gartner Inc. analyst Darin Stewart, Patternizr will sweep up individuals who fit a profile inferred by the system. At best, Stewart says, some people identified by Patternizr will be inconvenienced and insulted. At worst, innocent people will be incarcerated. Companies are now aggressively collecting and mining massive data sets on people’s shopping habits, incomes, hobbies, residences, and (via mobile devices) movements from place to place. They are using such big data to discover new facts about people, to classify them based on subtle patterns, to flag them as “risks” (for example, loan default risks or health risks), to predict their behavior, and to manipulate them for maximum profit. Privacy experts worry that people will be tagged and suffer adverse consequences without due process, the ability to fight back, or even knowledge that they have been discriminated against. Insurance companies such as Progressive offer a small device to install in your car to analyze your driving habits, ostensibly to give you a better insurance rate. However, some of the criteria for lower auto insurance rates are considered discriminatory. For example, insurance companies like people who don’t drive late at night and don’t spend much time in their cars. However, poorer people are more likely to work a late shift and to have longer commutes to work, which might increase their auto insurance rates. More and more companies are turning to computerized systems to filter and hire job applicants, especially for lower-wage, service-sector jobs. The algorithms these systems use to evaluate job candidates may be preventing qualified applicants from obtaining these jobs. For example, some of these algorithms have determined that, statistically, people with shorter commutes are more likely to stay in a job longer than those with longer commutes or less reliable transportation or those who haven’t been at their address for very long. If asked, “How long is your commute?” applicants with long commuting times will be scored lower for the job. Although such considerations may be statistically accurate, is it fair to screen job applicants this way? Sources: Grant Wernick, “Big Data, Small Returns,” Data-Driven Investor, January 13, 2020; “Big Data 2020: The Future, Growth and Challenges of the Big Data Industry,” www.i-scoop.com, accessed January 25, 2020; Brian Holak, “NYPD’s Patternizr Crime Analysis Tool Raises AI Bias Concerns,” searchbusinessanalytics.com, March 14, 2019; Lisa Hedges, “What Is Big Data in Healthcare and How Is It Already Being Used?” October 25, 2019; Alex Bekker, “Big Data: A Highway to Hell or a Stairway to Heaven? Exploring Big Data Problems,” ScienceSoft, May 19, 2018; and Gary Marcus and Ernest Davis, “Eight (No, Nine!) Problems With Big Data,” New York Times, April 6, 2014.
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The Internet of Things Aids Waste Management In 2003 the city of San Francisco set a very ambitious goal: Zero waste, meaning 100 percent of the waste generated by the city would be recycled and composted rather than dumped in landfill. Today San Francisco has come close to achieving that goal. Thanks to a large political, economic, and educational program, the city has been able to divert 80 percent of its waste away from landfills—more than any other major U.S. city. Information technology has also played a major role by providing more efficient methods of waste-sorting and improving citywide waste collection services. San Francisco partnered with recycling waste-management company Recology, which has an ambitious vision of its own—“a world without waste.” This hundred-year-old company proudly calls itself a “resource recovery leader,” and continually researches and implements new technologies for waste processing. These include optical sorting, which automatically sorts plastics with an infrared sensor based on their size, shape, and structure, and a landfill gas capture system that turns the methane gas generated by landfill into electric power. Much of the Recology waste-management work takes place on its 2,000 trucks. Recology updated its JD Edwards EnterpriseOne ERP system to support paperless fleet maintenance. Mechanics can now view and fill out their work orders immediately online using the system while managers are able to view the orders online instead of chasing down paper orders on vehicles. Recology truck drivers used to report fuel levels using manual forms that office workers had to key into the system manually. Now an IoT sensor attached to each truck’s fueling system automatically senses and sends the data directly to the JD Edwards fleet management module. No human effort is required. New trucks will be equipped with IoT devices linked to their Controller Area Network (CAN) bus, a protocol that enables devices to communicate with each other in applications without a host computer. The IoT devices will collect more than 1 million data points per day from every vehicle, including throttle position, speed, hydraulic actuator movement, and the amount of fuel burned. Recology Director of IT Mike McLaughlin and his team will be able to decide which data to send to the ERP system for managing the truck fleet more efficiently. Recology managers can also use the Enterprise One Orchestrator capability to take action on the data. For example, if a truck’s fuel level is low, the Orchestrator can send the truck driver an email to refuel the tank. If a truck component displays an error, the Orchestrator can schedule the truck for repair. Reducing human effort frees up manager and employee time to focus on more ways to create value, such as conducting waste audits to identify more opportunities for companies to engage in recycling and composting. All of these technology improvements have provided significant benefits, but Recology wants to do more to manage the growing volume of compostable and recyclable materials it is charged with handling. One possibility is to install IoT sensors at various points in the waste stream to monitor waste generation, recycling, and composting. Compology, a San Francisco startup, has developed technology for monitoring and analyzing data from IoT sensors attached to dumpsters. Waste pickup truck drivers generally follow a specific route every day, stopping to collect trash at every container on the route whether it needs emptying or not. They don’t know how full a trash bin is before they encounter it, and the amount of trash in each container can vary by day, week, and season. The Compology sensors take high-resolution photos of the interiors of waste containers multiple times per day, sending the images to the cloud. From there, waste haulers can monitor container fullness and optimize truck routes and schedules so that trucks do not waste time picking up trash at empty or half-full containers. This technology also has potential uses for estimating the percentage of nonrecyclable items in the trash. Armed with this information, cities like San Francisco could target households or businesses responsible for too much waste going to landfills. In addition to waste processing, Recology provides outreach and educational services, actively working with the community to promote its zero-waste goal. Eliminating the remaining 20 percent of San Francisco’s waste will be much harder than the first 80 percent, and it can’t be accomplished with new technology alone. San Francisco residents continue to send nearly 600,000 tons of waste to landfills each year. To lower this number significantly, city residents will need to become even more vigilant about using recyclable products and curtailing wasteful habits. People tend to underestimate how much they personally waste and how little they recycle or compost. Consumer behavior studies have found that behavior can be influenced by the level of knowledge a person has. It is hoped that the data San Francisco gathers about waste generation and the impact of recycling and composting will make residents more aware of their wasteful behavior and encourage them to take action. Sources: www.recology.com, accessed March 2, 2020; Monica Mehta, “Zero-Waste Innovation,” Profit Magazine, Spring 2019; Neil Sequeira, “IoT Applications in Waste Management,” IoT for All, January 22, 2019; www.compology.com, accessed June 18, 2019; and Anne Poirot, “How IoT Technology Could Solve San Francisco’s Waste Problem,” Medium.com, May 15, 2017.
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Google, Apple, and Facebook Battle for Your Internet Experience Case Study Three Internet titans—Google, Apple, and Facebook—are in an epic struggle to dominate your Internet experience, and caught in the crossfire are search, music, video, and other media along with the devices you use for all of these things. Mobile devices with advanced functionality and ubiquitous Internet access are rapidly overtaking traditional desktop machines as the most popular form of computing. Today, people spend more than half their time online using mobile devices that take advantage of a growing cloud of computing capacity. It’s no surprise, then, that today’s tech titans are aggressively battling for control of this brave new online world. Apple, which started as a personal computer company, quickly expanded into software and consumer electronics. Since upending the music industry with its iPod MP3 player, and the iTunes digital music service, Apple took mobile computing by storm with the iPhone, iPod Touch, and iPad. Now Apple wants to be the computing platform of choice for the Internet. Apple’s competitive strength is based not on its hardware platform alone but on its superior user interface and mobile software applications, in which it is a leader. Apple’s App Store offers more than two million apps for mobile and tablet devices. Applications greatly enrich the experience of using a mobile device, and whoever creates the most appealing set of devices and applications will derive a significant competitive advantage over rival companies. Apps are the new equivalent of the traditional browser. Apple thrives on its legacy of innovation. In 2011, it unveiled Siri (Speech Interpretation and Recognition Interface), a combination search/navigation tool and personal assistant. Siri promises personalized recommendations that improve as it gains user familiarity—all from a verbal command. Google countered by quickly releasing its own intelligent assistant tools Google Now and then Google Assistant. Apple faces strong competition for its phones and tablets both in the United States and in developing markets like China from inexpensive Chinese smartphones and from Samsung Android phones with sophisticated capabilities. iPhone sales have started to slow, but Apple is not counting on hardware devices alone for future growth. Services have always played a large part in the Apple ecosystem, and they have emerged as a major revenue source. Apple has more than one billion active devices in circulation worldwide, creating a huge installed base of users willing to purchase services and a source of new revenue streams. Apple’s services business, which includes Apple’s music (both downloads and subscriptions), video sales and rentals, books, apps (including in-app purchases, subscriptions and advertising), iCloud storage, and payments, has been growing 18 percent year over year. As Apple rolls out more gadgets, such as the Watch and HomePod, its services revenue will continue to expand and diversify, deepening ties with Apple users. According to CEO Tim Cook, Apple has become one of the largest service businesses in the world. This service-driven strategy is not without worry because both Google and Facebook offer stiff competition in the services area and Apple will need to offer some of its services on non-Apple devices to remain in this market. Google continues to be the world’s leading search engine, accounting for about 75 percent of web searches from laptop and desktop devices and over 90 percent of the mobile search market. About 84 percent of the revenue from Google’s parent company Alphabet comes from ads, most of them on Google’s search engine. Google dominates online advertising. However, Google is slipping in its position as the gateway to the Internet. New search startups focus on actions and apps instead of the web. Facebook has become an important gateway to the web as well. In 2005, Google had purchased the Android open source mobile operating system to compete in mobile computing. Google provides Android at no cost to smartphone manufacturers, generating revenue indirectly through app purchases and advertising. Many different manufacturers have adopted Android as a standard. In contrast, Apple allows only its own devices to use its proprietary operating system, and all the apps it sells can run only on Apple products. Android is deployed on over 85 percent of smartphones worldwide; is the most common operating system for tablets; and runs on watches, car dashboards, and TVs—thousands of distinct devices. Google wants to extend Android to as many devices as possible. Google’s Android could gain even more market share in the coming years, which could be problematic for Apple as it tries to maintain customer loyalty and keep software developers focused on the iOS platform. Whoever has the dominant smartphone operating system will have control over the apps where smartphone users spend most of their time and built-in channels for serving ads to mobile devices. Google is starting to monitor the content inside Android mobile apps and provide links pointing to that content featured in Google’s search results on smartphones. Google cannot monitor or track usage of iPhone apps. Since more than half of global search queries come from mobile devices, the company revised its search algorithms to add “mobile friendliness” to the 200 or so factors it uses to rank websites on its search engine. This favors sites that look good on smartphone screens. The cost-per-click paid for mobile ads has trailed desktop ads, but the gap between computer and mobile ads fees is narrowing. Google instituted a design change to present a cleaner mobile search page. Seven Google products and services, including Search, YouTube, and Maps, have more than a billion users each. Google’s ultimate goal is to knit its services and devices together so that Google users will interact with the company seamlessly all day long and everyone will want to use Google. Much of Google’s efforts to make its search and related services more powerful and user-friendly in the years ahead are based on the company’s investments in artificial intelligence and machine learning (see Chapter 11). The goal is to evolve search into more of a smart assistance capability, where computers can understand what people are saying and respond conversationally with the right information at the right moment. Google Assistant is meant to provide a continuing, conversational dialogue between users and the search engine. Facebook is the world’s largest social networking service, with 2.6 billion monthly active users. People use Facebook to stay connected with their friends and family and to express what matters most to them. Facebook Platform enables developers to build applications and websites that integrate with Facebook to reach its global network of users and to build personalized and social products. Facebook is so pervasive and appealing that it has become users’ primary gateway to the Internet. For a lot of people, Facebook is the Internet. Whatever they do on the Internet is through Facebook. Facebook has persistently worked on ways to convert its popularity and trove of user data into advertising dollars, with the expectation that these dollars will increasingly come from mobile smartphones and tablets. As of early 2020, 98 percent of active user accounts worldwide accessed the social network via smartphone and tablet. Facebook ads allow companies to target its users based on their real identities and expressed interests rather than educated guesses derived from web-browsing habits and other online behavior. In early 2019, over 98 percent of Facebook’s global revenue came from advertising,and 92 percent of that ad revenue was from mobile advertising. Many of those ads are highly targeted by age, gender, and other demographics. Facebook is now a serious competitor to Google in the mobile ad market and is trying to compete with emerging mobile platforms. Together, Facebook and Google dominate the digital ad industry and have been responsible for almost all of its growth. Facebook has overhauled its home page to give advertisers more opportunities and more information with which to target markets. The company is expanding advertising in products such as the Instagram feed, Stories, WhatsApp, Facebook Watch video on demand service, and Messenger, although the majority of ad revenue still comes from its news feed. Facebook has its own personalized search tool to challenge Google’s dominance of search. Facebook CEO Mark Zuckerberg is convinced that social networking is the ideal way to use the web and to consume all of the other content people might desire, including news and video. That makes it an ideal marketing platform for companies. But he also knows that Facebook can’t achieve long-term growth and prosperity based on social networking alone. During the past few years Facebook has moved into virtual reality, messaging, video, and more. Facebook is challenging YouTube as the premier destination for personal videos, developing its own TV programming, and making its messages “smarter” by deploying chatbots. Chatbots are stripped-down software agents that understand what you type or say and respond by answering questions or executing tasks, and they run in the background of Facebook’s Messenger service (see Chapter 11). Within Facebook Messenger, you can chat with friends or a business, send money securely, and share pictures and videos. Zuckerberg has said that he intends to help bring the next billion people online by attracting users in developing countries with affordable web connectivity. Facebook has launched several services in emerging markets designed to get more people online so they can explore web applications, including its social network. Facebook wants to beam the Internet to underserved areas through the use of drones and satellites along with other technologies. Zuckerberg thinks that Facebook could eventually be an Internet service provider to underserved areas. Monetization of personal data drives both Facebook and Google’s business models. However, thispractice also threatens individual privacy. The consumer surveillance underlying Facebook and Google’s free services has come under siege from users, regulators, and legislators on both sides of the Atlantic. Calls for restricting Facebook and Google’s collection and use of personal data have gathered steam, especially after recent revelations about Russian agents trying to use Facebook to sway American voters and Facebook’s uncontrolled sharing of user data with third-party companies (see the Chapter 4 ending case study). Both companies will have to come to terms with the European Union’s new privacy law, called the General Data Protection Regulation (GDPR), that requires companies to obtain consent from users before processing their data, and which may inspire more stringent privacy legislation in the United States. Business models that depend less on ads and more on subscriptions have been proposed, although any effort to curb the use of consumer data would put the business model of the ad-supported Internet—and possibly Facebook and Google—at risk. Also pressuring Facebook and Google’s ad-driven business models are Apple privacy protection features that allow users of its devices to opt out of targeted advertising. These tech giants are also being scrutinized for monopolistic behavior. In the United States, Google drives 89 percent of Internet search, 95 percent of young adults on the Internet use a Facebook product, and Google and Apple provide 99 percent of mobile phone operating systems. Critics have called for breaking up these mega-companies or regulating them as Standard Oil and AT&T once were. In July 2018 European Union (EU) regulators fined Google’s parent company $5 billion for forcing cellphone makers that use the company’s Android operating system to install Google search and browser apps. Less than a year later, EU antitrust regulators fined Alphabet an additional $1.7 billion for restrictive advertisingpractices in its Adsense business unit. Have these companies become so large that they are squeezing consumers and innovation? How governments answer this question will also affect how Apple, Google, and Facebook will fare and what kind of Internet experience they will be able to provide. Sources: Brent Kendall and John D. McKinnon, “DOJ, States Plan Suits Against Google,” Wall Street Journal, May 16–17, 2020; Tripp Mickle, “Apple Posts Record Reveue on Strong iPhone, App Sales,” Wall Street Journal, January 28, 2020 and “With the iPhone Sputtering, Apple Bets Its Future on TV and News,” Wall Street Journal, March 25, 2019; Daisuke Wakabayashi, “Google Reaches 41 Trillion in Value, Even as It Faces New Tests,” New York Times, January 16, 2020; “Wayne Rush, “How Google, Facebook Actions Could Bring Big Tech Under Attack in US,” eWeek, March 22, 2019; Tripp Mickle and Joe Flint, “Apple Launches TV App, Credit Card, Subscription Services,” Wall Street Journal, March 25, 2019; Associated Press, “EU Fines Google a Record $5 Million over Mobilepractices,” July 18, 2018; “Search Engine Market Share,” www.netmarketshare.com, accessed March 16, 2020; “Device Usage of Facebook Users Worldwide as of January 2020,” statista.com, accessed March 17, 2020; David Streitfeld, Natasha Singer, and Steven Erlanger, “How Calls for Privacy May Upend Business for Facebook and Google,” New York Times, March 24, 2018.
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Management Information Systems Instructions For this assignment, you will discuss what you have learned in Unit III and Unit IV by creating a 15-slide PowerPoint presentation that addresses the case studies listed below. The purpose of this presentation assignment is to research emerging technologies that impact businesses and society and how individuals, businesses, and government organizations go about protecting users in the cyberworld. The technologies we focus on in this research are in telecommunications, the Internet, and wireless technologies. These technologies improve the human workforce, bring value not only to frontline workers but also entire organizations, and change the industrial landscape. This assignment will help to develop your critical thinking and research skills as you research each of these scenarios. You will review each case study and create a PowerPoint presentation that provides a thorough analysis and a demonstrates your synthesis of the concepts presented in units III and IV. Based on your reading of the case study “Does Big Data Provide the Answer?” from Chapter 6, address the prompts below in a minimum of four slides. Explain the term big data in your own words. Discuss how Amazon, YouTube, and Spotify used big data to better serve their customers. Describe the limitations of using big data. Discuss at least one ethical or security issue that big data can pose to individuals. After reviewing the case study “The Internet of Things Aids Waste Management” from Chapter 7 of your eTextbook, create at least three slides for your presentation that address the prompts below. Identify the problem described in this case study. Is it a management problem, an organizational problem, or a technology problem? Explain your answer. What role has information technology and the IoT played in helping cities deal with their waste management problems? Describe the IT applications that are being used for this purpose. How successful are these IT applications as a solution? Explain your answer. Next, review the case study “Google, Apple, and Facebook Battle for Your Internet Experience” from Chapter 7, and address the prompts below in at least four slides. Explain what is meant by mobile technology. Discuss how telecommunications and mobile technology networks are vital to companies and how they are fundamentally changing organizational strategies. Discuss the mobile strategy used by Google, Apple, and Facebook. Discuss at least two challenges posed by the Internet and networking. Finally, review the case study “Capital One: A Big Bank Heist from the Cloud” from Chapter 8, and address the prompts below in at least four slides. Discuss at least two security threats to cloud data. What should companies do to protect cloud data? Discuss why both the company and the cloud vendor are responsible for security. Discuss at least one security control that companies can use to increase security. In formatting your PowerPoint presentation, do not use the question-and-answer format; instead, use bullets, graphs, and/or charts in your slides to identify important points, and then discuss those points in the speaker notes of each slide. The speaker notes section of each slide should not repeat slide information, but serve as an area in which you augment or elaborate on slide information so that your audience has a better understanding of the material. You must have a minimum of 100 words in the Speaker Notes section of each content slide. Your PowerPoint presentation should be a minimum of 15 slides in length (not counting the title and reference slides). You are required to use a minimum of two peer-reviewed, academic sources that are no more than 5 years old to support each case study. You may use your eTextbook once in each case study. All sources used, including the eTextbook, must be referenced; all paraphrased material must have accompanying in-text citations. At least two sources must come from the CSU Online Library. APA style and formatting is required.

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