Chapter 7
EXAMPLES OF BIG DATA
LEARNING OBJECTIVES
After completing this chapter, you should be able to do the following:
• Identify how companies are currently using Big Data.
• Distinguish how organizations can apply the Big Data examples in the chapter to their organizations.
INTRODUCTION
The year 2016 was the year that Big Data was no longer a buzzword. Technology and the capacity to manage data have caught up to the point that everyone can now use Big Data—not just the early adopters.1
This year, 2017, will see an increase in data mining and collection and an ever-increasing amount of data that can be tailored to specific tasks. There will also be increased risk of data breaches. Established and emerging companies are using data to inform decision making, drive customer engagement, close sales, predict spending patterns, and increase revenue.
Additionally, machine learning and artificial intelligence will be key players in data analysis. Forbes predicts an increase in CDO (Chief Data Officers), analysists, programmers, and specialists with Big Data knowledge, though they see the demand for Big Data staffing tapering off as infrastructure and machines become adjusted to the new data load.2
The focus will be on fine-tuning big data into more manageable information, such as "fast data" and "actionable data" to cut down on the extra noise that some companies are getting overwhelmed by when they ask the wrong questions of their data.3
This chapter includes examples of how Big Data is being created and used by businesses. The goal of this chapter is to understand (through the examples provided) that there are many types of data that an organization has access to, or can obtain access to, that allow increased insights into their business. The reader should view each example as a potential application for their organization.
The purpose is not to overwhelm but to review a myriad of examples to heighten the reader’s awareness of the many different applications of Big Data and to trigger thoughts about how Big Data may be applied in your company.
EXAMPLES OF BIG DATA
Uber
Uber uses Big Data to know customers’ every move. Where they live, where they eat, where they work, where they travel, and the timing of these activities. Uber has partnered with "Starwood Preferred Guest," which is an elite club that allows you to earn points whenever you use Uber. The catch is you allow Uber and Starwood to use your data however they see fit.4
The picture reads "By clicking allow, you agree to give Starwood Preferred Guest: Access to your full name, email, photo, and promo code. Access to all of your Uber activity. This includes all pickup and drop-off locations and times, fare amount, distances traveled, and Uber products used. Starwood Preferred Guest will use this information according to its Privacy Policy."
Halo Top
The light ice cream brand is taking the frozen treat sector by storm, at a time when most brands are struggling to keep their market share. They’ve completely ignored traditional marketing routes, and use Big Data provided by Facebook, Twitter, and Instagram to place specific ads and closely monitor their return on investment for these digital ads. Mr. Woolverton, the founder of Halo Top, said, "You can make $100 go a lot further through a Facebook ad where you can target demographically, psychographically, geographically, et cetera, than by taking out ad space in a newspaper. If you can focus on people who actually want to see your ads, everyone is happier." Halo Top’s sales have increased 2,500 percent from 2015 to 2016.5
Electronic Arts
There are more than 2 billion video game players on the planet, and the $20 billion-dollar industry leads in many categories. Electronic Arts has one of the largest market shares, and they use big data to ensure customer engagement throughout the gaming experience. For example, games that are too difficult on beginning levels, or too easy on advanced levels, cause players to quit, and this hampers a videogame’s success. Electronic Arts looks at where they lose players during game play, sees if there are bottlenecks that are causing players to quit, and uses this information to tailor new games going forward.6
NFL
The NFL is using Big Data to track players on the field for multiple purposes—specifically free agency. Using two sensors, one in each shoulder pad, and a refined GPS-like tracking system on two levels of the stadium, the NFL can now track player mileage. Some general managers see this as a way to look at free agency—to judge how much a veteran has put on his body, like injuries, miles, acceleration, deceleration, and so on. "With enough good data, coaches and trainers could more finely tune training sessions for different personnel groups, essentially personalizing practice around the different kinds of needs of, say, nose tackles versus wide receivers."7
Williams-Sonoma
By using and refining search optimization and targeted email messaging based on users’ search histories, Williams-Sonoma increased revenue by more than $1 million in three months. Data is involved in every level of decision-making in the company, like algorithmically driven site search to provide more relevant and personalized search. According to Sameer Hassan, Williams-Sonoma’s vice president of e-commerce and marketing technology, "All retail is going to be dependent on our ability to give customers a much more relevant experience."8
eCare21
Using data analytics to track thousands of pieces of health data through Fitbits, smartphones, physical activity monitors, and other easily accessible technology, eCare21 monitors senior citizens that have trouble accessing traditional healthcare. The information is put together in a dashboard so that family,
caregivers, and doctors can get a full picture of the senior’s health without ever seeing them. Shortages in the healthcare community can be alleviated by similar services that also integrate telehealth services. "According to the National Business Group on Health, nine in 10 large employers will provide telehealth services to their employees in 2017. By 2019, NBG predicts, this number will leap to 97 %."9
Ford
Ford is using Big Data to extend their impact on their products. Instead of ending the cycle when the vehicle leaves the factory, Ford can now monitor what items need repair or replacement when they are serviced at the local Ford dealership and see what components break down most easily. Ford can also use this information to re-engineer failing pieces with more durability.10
Heineken
Heineken uses Big Data to track sales as they correspond with weather reports to see where they sell best in regions during different seasons, weather patterns, and so on, so they can always have their beer in the right place at the right time. They are also testing a program called Shopperception, which is based on Xbox Kinect sensors, to see exactly when customers are buying beer, what their behaviors are in front of the shelf, and getting real-time purchase information. This gives them a way to see exactly how their beer is being purchased, where in the store it is being purchased, and when.11
Marist College
Marist College has developed an analytical model that can predict with 75 percent accuracy which students will succeed and which will fail based on the first two weeks of course work. Marist College has used this model to increase degree completion rates. The use of this model raises a lot of ethical questions, but so far it has been used for a positive purpose.12
Taco Bell
Taco Bell uses Big Data to stay in tune with customer experience. Taco Bell has a division they nicknamed the "Fishbowl," which listens to and gathers all the information about its company on Facebook, Instagram, Twitter, and the like. The executives across the company come together for 15 minutes every morning to get a report from the Fishbowl to see in real time how the company is doing. For example, when Taco Bell rolled out breakfast, the East Coast stores had miscalculated the demand, resulting in very long lines, which were broadcast all over social media. By the time the Midwest and West Coast stores were opening that same day, the problem had been resolved, and there were no wait times.13
Sensors—Consumer Health and Safety
The advent of sensors inside or attached to devices is changing the way we experience many activities, from sports to monitoring our health. For example, Fitbit is a tracker worn on the wrist that records steps, distance, calories burned, and active minutes. The Fitbit has a wireless connection to the wearer’s computer system.
At the Consumer Electronics Show in Las Vegas, manufacturers displayed chips to improve an individual’s posture and sensors in sports equipment (including basketballs, golf clubs, and tennis balls) to help train smarter. There is even a sensor to handle mundane but important tasks such as locating lost keys.
Another application of this technology is specialty carpets containing sensors that can keep seniors alive and independent longer by identifying normal walking patterns. These carpets can notice when a person’s gait begins to change, possibly predicting a fall. Falls are one of the major events that result in senior citizens relocating from their residences to senior living centers. Also, broken hips resulting from falls may be the onset of the end of a person’s life. Fitness devices have also been used to identify areas of natural disasters such as earthquakes. Personal fitness tracker Jawbone reported that users woke suddenly in areas close to the epicenter of an earthquake—Napa, Sonoma, and Vallejo—right after it struck. In areas further away, like Modesto and Santa Cruz, fewer users were disturbed. This is a powerful demonstration of how Big Data can allow anyone from emergency relief workers to governments to measure the effects of a disaster.14
KNOWLEDGE CHECK
1. Which of the following was listed as a sensor?
a. A sensor to advise an owner when a pet wants to come in.
b. A sensor to notify you that mail has arrived in your mailbox.
c. A sensor to find your keys.
d. A sensor to detect someone at your front door.
Call Centers—Displeased Customers
United Healthcare Services Inc. is gaining insight from Big Data by investigating customer speech patterns in data call centers to see whether displeasure can be identified. By noticing patterns in customer interactions, UHS can pinpoint situations that lead to customer turnover.15
Speech analytics systems can analyze tone and sentiment of voice and talk or silence patterns to gauge emotion and satisfaction and tie detection of user-defined phrases to specific agent actions—in short, identify and prioritize what needs fixing, and then contribute to the resolution.16
Machines Sensors—GE
Another great example of companies using Big Data is GE. GE puts devices into anything "that spins" in order to find what machines need maintenance, which processes can be curtailed, and what machines are experiencing wear and tear at greater rates. Because GE makes almost half of its income from maintenance services for those machines, it gains a huge advantage in its services business from all of that data. This results in one gas turbine’s sensor producing more information in a day than all of Twitter does in a week.17
Weather and Flight Information—Lodging Chains
People travel less during bad weather, which results in fewer purchases of overnight lodging. However, some chains are using bad weather to their advantage. Red Roof Inn made the connection between bad weather, canceled flights, and stranded passengers. When flights were canceled due to inclement weather, passengers turned into potential and desperate customers. Red Roof Inn sourced readily available flight cancellation and weather information and used it increase bookings. The company built an algorithm, sorted based on inn and air terminal areas. This algorithm could be used to target passengers with hotel deal information. Incorporating the knowledge that travelers would be searching for hotels via their cell phones, Red Roof Inn used multiple mobile platforms to specifically target travelers searching hotel accommodations and made it as easy as possible for them to book a hotel.
Consider the impact of Big Data. Note that 1 percent to 3 percent of flights are canceled every day, which means 150 to 500 flights (in other words, 25,000 to 90,000 stranded passengers). Using Big Data to target these passengers, Red Roof Inn managed to increase its business by 10 percent from 2013 to 2014.18
KNOWLEDGE CHECK
2. What percentage of flights are canceled (on average) every day?
a. 1 percent to 3 percent.
b. 2 percent to 4 percent.
c. 3 percent to 5 percent.
d. 4 percent to 6 percent.
Weather Information and Power Outages—Pizza Chains
Pizza chains also use weather to target potential customers. One pizza chain delivers coupons based on severe weather and power outages. The business understands that people will be unable to cook when the power is out, and thus targets these individual with mobile ad campaigns to entice them to purchase pizza. The targeted marketing campaign has a 20 percent response rate, which is higher than the national average.19
Field Sensors—John Deere
Agribusiness pioneer John Deere is making waves in the industry with Big Data. The John Deere Field Connect system screens dampness levels and sends the information over a wireless connection for
farmers to see. The natural sensors likewise gauge "air and soil temperature, wind speed, humidity, solar, radiation, and precipitation and leaf wetness."
The information will help farmers to know when crops are approaching ideal moisture levels. Equipped with this data, ranchers can settle on watering system choices. Pattern information can likewise demonstrate how much the season influences moisture retention.20
Social Networks-EMI
Record company EMI uses Big Data to predict future music trends. EMI pays close attention to how music purchasers are consuming newly released music. For example, EMI looks at how the music is being referenced on social media networks, as well as how it is being played on streaming services. The company then analyzes the data and breaks it down by location, demographics, and subcultures and assists the music distributor to deliver pinpoint advertising and forecast product demand with a high confidence level. This concept is applicable to other retailers who can also aggregate feeds from social networks to build an understanding of how new products will be received by new or existing markets, or even how their products and company reputation are perceived among the public.21
eBureau—Financial Services
After new customer acquisitions had hit an all-time low, a financial services firm sought to use Big Data as a means to identify which new clients would evolve into the most worthwhile investment opportunities. The organization supplemented its client demographic information with outsider information acquired from eBureau. Sales lead opportunities were fortified with other consumer information including incomes, ages, occupations and related factors. The improved data set is then entered into an algorithm which recognizes which new customer leads ought to get extra attention and which should be passed over. Using Big Data has resulted in an 11 percent increase in new customer win rates. Simultaneously, the firm has brought down deals related costs by 14.5 percent.22
KNOWLEDGE CHECK
3. EMI partially obtains music trends from
a. YouTube.
b. Google.
c. Social media networks.
d. Instagram.
Baby Shower Registry—Target
One retail Big Data example that merits special attention is Target’s pregnancy detection. Target correlated baby shower registry information with its Guest ID program so as to discern when a customer is likely to be pregnant. Target’s Guest ID is a unique customer ID that tracks buying history, credit card use, survey responses, customer support interactions, email click-throughs, and website visits. The organization supplements the shopper activities with demographic information like age, ethnicity, instruction, conjugal status, the number of children, assessed salary, work history, and life events (such as divorces, filing for bankruptcy, or moves).
By looking at customers who registered on the baby shower registry with the purchasing history from their Guest ID, the retailer could follow changes in shopping tendencies as the customer advanced throughout her pregnancy. For instance, during the initial 20 weeks, pregnant women started acquiring supplements like calcium, magnesium, and zinc. In the second trimester, they started purchasing bigger pants and larger amounts of hand sanitizers, unscented salve, scent-free cleanser, and enormous packs of cotton balls. Altogether, the retailer could pinpoint around 25 items pregnant women purchased throughout their pregnancies.
By applying these buying practices to all customers, Target had the capacity to recognize when customers were pregnant despite the fact that they had not directly notified Target or anyone else. Target then forecast every shopper’s likelihood of being pregnant with a pregnancy-prediction model. With this model, Target assigned customers with a score that rated their likelihood of being pregnant. These numbers were used to identify the specific pregnant segment, which then received targeted promotions aimed at each step of the pregnancy. These customers did not stop at purchasing items for their infants. Their purchasing behaviors as a whole were heightened. From using Big Data, Target increased its income from $44 billion in 2002 to $67 billion in 2010. Though the retailer did not freely remark on this program, Target’s CEO is on record telling financial specialists that the organization’s "heightened focus on items and categories that appeal to specific guest segments such as mom and baby" significantly added to the retailer’s success.23
Despite the customer security and advertising considerations which must be questioned, this is an important lesson for retailers.
KNOWLEDGE CHECK
4. Target is known for a Big Data trend identifier. What trend does Target believe that it can predict?
a. Marriage.
b. Pregnancy.
c. Illness.
d. Divorce.
Hadoop—Morgan Stanley
Morgan Stanley took a lesson from Target’s book and tried to follow suit. "We dumped every log we could get, including the web and all the different database logs, put them into Hadoop and ran time-based correlations," says Gary Bhattacharjee, executive director of enterprise information management.24The firm was then able to see market activity and how it corresponded with web issues and database read-write problems.
At the point when Morgan attempted to do some portfolio investigation, it found that conventional databases and grid computing wouldn’t scale to the vast volumes of information that its information researchers needed. The IT office connected 15 old servers using Hadoop. As opposed to working with smaller sets of data, Hadoop allowed the bank to work with vast volumes of information from a multitude of angles. It allowed Morgan Stanley to bring cheap infrastructure into a framework, install Hadoop, and let it run. The company now has a very scalable solution for portfolio analysis.
Automotive Data Generator—Ford Fusion
Ford has a large, fragmented environment from which to acquire Big Data. On top of that, Ford has invested heavily in creating more connected machines, including their cars. For instance, Ford’s current hybrid Fusion model produces up to 25 gigabytes of data every hour. This information is a potential goldmine for Ford provided they can extract knowledge from the information.
Ford had been pushing to adopt Big Data technologies for around a year. The company’s IT group has been able to get a high-level view of all the data sources and the complex analytics puzzle that bridge across the whole organization. This effort and the need to process information in new ways were driving forces for IT in selecting Hadoop.25
Internet—Virgin Atlantic
Virgin Atlantic has connected numerous Boeing 787 airplanes and cargo devices via the Internet. Every plane has numerous connected parts producing an extensive volume of information.
Every associated flight can deliver more than half a terabyte of data. The information could be used to anticipate maintenance issues or to enhance flight and fuel effectiveness. Unfortunately, Virgin Atlantic reported that it has not been able to make much use out of all this data. Trials with Hadoop did not generate the insights needed; therefore, the company had to look at other software vendors.26
Industrial Internet—GE
GE has invested heavily in Big Data as seen in the previous machine services example—from harnessing trains and planes to power the Internet and beyond. General Electric is best known for its machine making, yet it has begun marketing itself as a major information organization as well by pushing its vision for an "Industrial Internet"—the idea that machines ought to be associated with the web to build productivity and decrease downtime. In 2012, it dispatched programming to help carriers and railways move their information to the cloud and joined forces with Accenture to shape Taleris, a startup that will help the air industry foresee mechanical problems and decrease flight delays or cancellations.27
Manufacturing Equipment—TempuTech
TempuTech’s Big Data framework is having an impact on the agribusiness business. The organization offers connected systems that screen ideal grain inventory and identify potential hazards (in other words, grain elevators) in systems.
Dangers, such as broken belts or bearings, can be observed. Grain management systems can track the moisture and temperature of grain bins, permitting air circulation, and fan settings to be manually adjusted to allow for changes. This information is additionally sent to farm operators, who can use it to anticipate dampness and temperature changes taking into account changes in weather.28
Automated Food Production—King’s Hawaiian
King’s Hawaiian Bread Processing Plants partnered with Rockwell Automation to build a highly automated bread-baking facility with specialized machines. The employees have an additional tool to screen bread production and can monitor operations from anywhere via the Internet. The information gathered permitted the organization to decrease potential downtime of machines and lower maintenance costs.29
KNOWLEDGE CHECK
5. TempuTech was being used in which aspect of agribusiness Big Data?
a. Grain elevators.
b. Combines.
c. John Deere tractors.
d. Fertilizer spreaders.
Logistic sensors—UPS
UPS uses sensor information and Big Data analytics to save money, increase efficiency, and reduce its environmental impact. The vehicle sensors screen speed, miles per gallon, mileage, the number of stops, and motor engine health. The sensors catch more than 200 data points for every vehicle in the 80,000 fleet every single day. As a result of this data idling time, fuel consumption, and harmful emissions are reduced.
UPS also uses On-Road Integrated Optimization and Navigation (ORION) to optimize delivery routes. ORION optimizes the routes using hundreds of millions of location data points.30
Data Analytics—Big Box Stores
Walmart is performing data analytics on customer and transaction information from 10 sites. Sears and Kmart are attempting to enhance the personalization of promotional campaigns, coupons, and offers with Big Data to contend better with Walmart, Target, and Amazon. As the pioneer in the space, Amazon uses 1 million Hadoop clusters to bolster its affiliate network, risk management, machine learning, and website updates.31
Data Analysis—Video Games
The video game industry is using Big Data to follow gameplay, anticipate distribution patterns, and break down more than 500GB of organized information and 4 TB of operational logs every day.
One side effect of the growth in the industry is an increase in the amount of data generated from video games. Video game data comes from several sources: gameplay data, micro-transactions, time stamps, social media, price points, payment systems, in-game advertising, virtual goods, multiplayer interactions, real-time events and content updates, to name a few.
To analyze the massive amount of structured and unstructured data that’s generated every day by the nearly 2 billion users of video games, companies are using a variety of tools such as Hadoop. Big Data allows video game publishers to track a player’s progress and activity to recognize any bottlenecks or trouble spots in games. This information can be used to improve many aspects of a gamer’s experience, including reevaluating certain aspects of a game, so that frustrated gamers don’t quit.
Supercomputers and genome databases—Icahn School of Medicine at Mount Sinai
Mount Sinai is also important in the Big Data world for embracing data scientists and supercomputers to build the future hospital. The New York City medical center is recruiting top Silicon Valley talent to build a facility that will predict diseases based on patients’ genomes. This will allow the hospital to anticipate sicknesses, diminish the quantity of normal doctor’s facility visits, and streamline electronic therapeutic records. At the heart of Mount Sinai’s endeavors are a $3 million supercomputer named Minerva, which rapidly forms gigabytes of well-being information, and BioMe, a database of genomic examples from more than 25,000 patients.32
Customer Databases—Banking
The banking industry is pursuing Big Data as well. JPMorgan Chase creates significant amounts of credit card information and other value-based information about U.S. shoppers. Eventually, the bank consolidated that database, which incorporated 1.5 billion bits of data, with freely accessible economic stats from the U.S. government. Chase used new analytics to understand customer buying patterns and insights and offered those reports to the bank’s customers. The innovation permitted the bank to segment its Visa Card customers into smaller segments in short amounts of time. It could then investigate customer retention patterns within those segments.
Citibank is another bank that is exploring different avenues regarding better approaches to offering business clients value-based information gathered from its worldwide client base, which customers can use to distinguish new trade patterns.33
KNOWLEDGE CHECK
6. Chase integrated credit card data with what?
a. Economic stats from the industry.
b. Economic stats from the U.S. government.
c. Economic stats from associations.
d. Drivers’ license records.
Shared Information—Financial Services
Organizations are not simply using information themselves; they are welcoming others to explore and use their information.
Intuit has launched a program for developers that provides access to the following:
• More than 65 million records and 11 million users supported today.
• Financial information from more than 19,000 financial service organizations over the United States and Canada.
• Aggregate consumer and business financial account data, in addition to auto-categorized transactions.
• Secure Application Programming Interface for cost-effective, self-serve data access.
• Software Development Kits for .NET and Java to speed up development of apps.
Another example of how a financial services institution has taken advantage of access to open customer data to improve services is Credit Agricole (CA). The French bank has created a developer program with the slogan "applications for you and by you." The CA app store is a co-creation platform that unites the bank’s clients—and their application needs—and independent developers and gives the developers access to anonymized banking information.34
Kaggle—Big Data Competitions
Kaggle is a hosting platform for Big Data competitions. Companies and researchers post their raw data on Kaggle so that professional and amateur statisticians from all over the world can analyze it. Whoever comes up with the best predictive model or script wins a cash prize and sometimes a job at the hosting company. This crowdsourcing approach to analyzing Big Data has attracted big-name companies like Walmart, State Farm, and GE, as well as the brightest talent in the data science sphere.35
Gnip Social Network Interface—Library of Congress
Gnip is an application program interface (API) that allows its clients to access every online social media networking stream, including Twitter, Facebook, and Disqus. Gnip’s service lets clients screen and parses social networking streams by characteristics like keywords, patterns, trends, and geographical locations. In addition to being acquired by Twitter and offering access to Twitter’s full authentic stream of tweets— which the Library of Congress uses—the organization also offers turnkey solutions that collect social data from up to six different sources.36
KNOWLEDGE CHECK
7. Gnip is designed to access Big Data in what areas?
a. Unstructured internal email systems.
b. Video streams only.
c. Social media streams.
d. Governmental databases.
Big Data—Fraud Vulnerability
The sooner that claims or transaction fraud are recognized, the sooner it can be stopped and rectified. Big Data can mask fraud, but it also may be the avenue for quicker recognition of fraudulent patterns. Much of the time, fraud is found long after it occurred, when the harm has been done, and all that is left is to minimize the damage and adjust processes to keep the fraud from recurring. Big Data platforms could use analytics to search for patterns that indicate fraud, determine which regions or locations have higher rates of fraud, and even test for network vulnerabilities.37
MongoDB—City of Chicago
The City of Chicago is using MongoDB to minimize crime and enhance municipal services by gathering and breaking down geospatial information in real-time from more than 30 unique divisions. For example, in a given territory, the city may assess the number of 911 calls and complaints, broken lights, stolen trash cans, liquor permits, and abandoned structures, verifying that an uptick in crime is more likely than normal. The city needs to merge structured and unstructured data, do so at scale, and conduct the analysis in-house. Eventually, the city hopes to use this data in a predictive way, to prevent crimes or safety issues before they occur. Chicago has opened its data to the public, allowing others to create new services, such as an app that alerts residents when street sweepers are coming to their location.38
Microsoft Azure—City of Barcelona
Barcelona is the annual host of the Mobile World Congress technology show and is becoming a hub of innovation itself.
The city offers savvy parking meters that work on city-wide Wi-Fi, giving residents up-to-speed reports on where to stop and permitting them to pay using their telephone. Smart bus stops give travelers ongoing updates through touch-screen panels, and a city-wide sensor system informs residents about temperature, air quality, noise level, and pedestrian activity.
Barcelona constructed a Big Data system on Microsoft Azure to process and investigates the many data points it was getting. With the information generated by the framework, the city can offer better services, for example, open transportation, plan for occasions like the La Mercé Festival all the more effectively, and better evaluate the effect of tourism.39
KNOWLEDGE CHECK
8. What does Barcelona offer its guests and citizens?
a. Up-to-speed reports on where to stop.
b. Up-to-speed information about car rentals.
c. Up-to-speed information about dining locations and hours.
d. Up-to-speed information on hotel accommodations.
RFID Sensors—Disney World’s MagicBand
Disney World is using Big Data with their MagicBand project. MagicBand is a $1 billion investment in a wearable, sensor-loaded wristband that vacationers use to do everything from registering their inn room, purchasing their lunch, and reserving a spot for particular attractions.
Wearers use the band to "’weigh in" at specific posts by tapping it against a recipient, and it tracks their path through RFID, so Disney gathers information on guest movement all through the amusement park. Using this information, Disney can accommodate more visitors, efficiently staff rides and attractions, and better regulate inventory at highly accessed shops and eateries.40
Bluetooth Sensors—Alex and Ani
Alex and Ani is a jewelry store chain. It has installed Bluetooth sensors in stores that can track activity and push more customized offers to clients’ telephones as they enter.
The company has also collaborated with Swirl, a technology innovation company. The application tracks clients’ walking habits inside the store, like a heat map, so the organization will have the capacity to better sort out and present items to customers.41
MongoDB—MetLife Customer Service
MetLife has more than 100 million clients and more than 100 products. Its back-office systems involve an expansive network of siloed applications that make it difficult for clients and agents to get the right data. Using MongoDB, MetLife created an application that gives a single perspective of the client, totaling client and product data from more than 70 current frameworks and making it accessible to clients and agents. Furthermore, it manufactured the application in only three months. Thus, the supplier decreased the time to determine client issues, which increased customer happiness, giving representatives a chance to cross-offer and upsell using real-time analytics.42
KNOWLEDGE CHECK
9. How does MetLife use Big Data?
a. To evaluate the risk of individual customers.
b. To cross-sell products.
c. To evaluate risk by aggregated customer data by geographic area.
d. To detect fraudulent transactions.
IBM’s Predictive Traffic Management—Lyon, France
Anyone who has been to a big city knows that traffic congestion is a real problem. It affects the health and well-being of residents who must endure rage-inducing delays and affects the economic health of the city as well. One city in France decided to proactively manage traffic congestion using IBM’s predictive traffic-management software.
Transportation department officials in Lyon, France combined real-time traffic data with advanced analytics to help proactively manage traffic congestion. That means drivers spend less time in traffic because detours can be put into place quickly, along with alternate route suggestions.
How did they do it?
• Giving cities information on how they can reroute traffic to avoid traffic jams.
• Schedule delivery trucks for a less congested time.
• Traffic managers evaluate accidents, predict outcomes and make quick decisions on how to restore traffic flow. The solution could be to adjust traffic signals to allow cars to detour quickly or posting messages to alert drivers of the accident.43
Evolv—Xerox Human Resources
Evolv is also an important part of Big Data for mining employee performance to help reduce turnover and minimize HR. Big Data is likewise changing the way organizations contract and deal with their workforces. Like other HR programs, Evolv helps executives better comprehend workers and employment applicants by looking at their abilities, work experience, and identities. Yet, Evolv takes it to a more profound level, crunching more than 500 million information focuses on gas costs, unemployment rates, and online networking utilization to help customers like Xerox—which has cut attrition by 20 percent—anticipate, for instance, when a worker is destined to leave employment. Evolv’s information also offers other insights that Big Data researchers have uncovered: People with two social networking records perform much higher than those with more or less, and in numerous professions, for example, call-center work, workers with criminal records perform better than those who are squeaky clean.44
Practice Questions
1. What were some of the sensors that were highlighted from the consumer electronics show?
2. It was estimated that the Ford Fusion produced how many GB of information per hour?
3. What product is Disney World using to improve the customer experience as well as the flow of traffic in the amusement park?
4. Describe the Big Data app that Lyon, France, was working on with IBM.