Decades before the dawning of the information age, fictional sleuth Sherlock Holmes warned, “It is a capital mistake to theorize before one has data.”1
Today, data is abundant, coming in fast and furious. In some online gaming environments, each mouse click is recorded, according to big data analytics expert H.J. Watson. And we have analytics to help businesses, NGOs, the government, and even modern-day crime-fighters form theories, find solutions, and create and expand markets.
In “Big Data Analytics: Concepts, Technologies, and Applications,” an article published in Communications of the Association for Information Systems, Watson shares four such real-world examples from the high-profile corporations Starbucks, Chevron, U.S. Xpress, and Target.
Watson’s article is required reading in Introduction to Big Data Analytics, a Walden University course for students earning an MS in Information Technology degree with a Big Data Analytics specialization. In this excerpt, IT degree candidates learn how big data analytics contributed to business success for each of these high-powered companies. Read along to learn more:2
Let us consider several examples of companies that are using big data analytics. The examples illustrate the use of different sources of big data and the different kinds of analytics that can be performed.
Starbucks was introducing a new coffee product but was concerned that customers would find its taste too strong. The morning that the coffee was rolled out, Starbucks monitored blogs, Twitter, and niche coffee forum discussion groups to assess customers’ reactions. By mid-morning, Starbucks discovered that although people liked the taste of the coffee, they thought that it was too expensive. Starbucks lowered the price, and by the end of the day, all of the negative comments had disappeared.
Compare this fast response with a more traditional approach of waiting for the sales reports to come in and noticing that sales are disappointing. A next step might be to run a focus group to discover why. Perhaps in several weeks, Starbucks would have discovered the reason and responded by lowering the price.
Each drilling miss in the Gulf of Mexico costs Chevron upwards of $100 million. To improve its chances of finding oil, Chevron analyzes 50 terabytes of seismic data. Even with this, the odds of finding oil have been around 1 in 5. In the summer of 2010, because of BP’s Gulf oil spill, the federal government suspended all deepwater-drilling permits. The geologists at Chevron took this time to seize the opportunity offered by advances in computing power and storage capacity to refine their already advanced computer models. With these enhancements, Chevron has improved the odds of drilling a successful well to nearly 1 in 3, resulting in tremendous cost savings.
U.S. Xpress is a transportation company. Its cabs continuously stream more than 900 pieces of data related to the condition of the trucks and their locations. This data is stored in the cloud and analyzed in various ways, with information delivered to various users, from drivers to senior executives, on iPads and other tablet computers. For example, when a sensor shows that a truck is low on fuel, the driver is directed to a filling station where the price is low. If a truck appears to need maintenance, drivers are sent to a specific service depot. Routes and destinations are changed to ensure that orders are delivered on time.
Trucks experience necessary and unavoidable idle time. An example of the former is when a truck is stuck in traffic and nothing can be done about it (unless it is routed around a traffic delay). An example of the latter is when a driver stops for lunch in the winter and keeps the truck running in order to keep the cab warm. By monitoring its trucks, U.S. Xpress can tell which is which and has saved millions in fuel costs and reduced emissions into the environment by incenting its drivers to reduce avoidable idle time.
Target received considerable negative attention in publications such as The New York Times for mining data to identify women who are pregnant. The negative press began when a father complained to a Target store manager in Minneapolis that his daughter had received pregnancy-related coupons. He felt that the coupons were inappropriate and promoted teen pregnancy. Little did he know that his daughter was pregnant. He later apologized to the store manager and said that obviously he’d been unaware.
How did Target identify pregnant women? To build its predictive models, Target focused on women who had signed up for the baby registry—an excellent indicator that they were pregnant. They then compared the women’s purchasing behavior with the purchasing behavior of all Target customers. Twenty-five variables were found useful for identifying this market segment—pregnant women—and when their babies were due. The variables included buying large quantities of unscented lotions; supplements such as calcium, magnesium, and zinc; scent-free soaps; extra-large bags of cotton balls; hand sanitizers; and washcloths.
Using these variables, pregnancy predictive models were built and used to score the likelihood that a woman was pregnant, and when she was likely to deliver. For example, pregnant women tend to buy hand sanitizers and washcloths as they get close to their delivery date. Target used these predictions to identify which women should receive specific coupons.
The story continues, however, with another public relations nightmare. Soon afterward, Target received unfavorable press for predicting engagements. Target was sending out invitations to join its bridal registry before sons and daughters told their parents they were engaged.
In response to the negative press, Target no longer sends out only pregnancy-related coupons, but mixes in others, such as for lawnmowers. Target is also much more guarded in what information it shares about its data mining activities. While Target’s data mining is legal, it strikes many people as creepy, if not inappropriate.
Professionals with information technology degrees are in demand to fill the ever-growing number of computer science jobs.3 Walden University’s MS in Information Technology degree program can prepare you to step into rewarding roles in this dynamic field through its specializations in Big Data Analytics, Health Informatics, Information Assurance and Cyber Security, Information Systems, and Software Engineering.
When you choose Walden for your online IT degree, you’re joining an accredited university respected for its IT expertise. Walden:
Walden makes it possible to earn an information technology degree online and stay engaged in your job by using its flexible online learning platform and receiving world-class student support. You’ll add value to your current position by using the knowledge you gain as soon as you acquire it.
The IT world is ever-changing, and an advanced degree can give you the 21st-century skills you need to tackle challenges, find solutions, and take your career to the next level.
Walden University is an accredited institution offering an MS in Information Technology degree program online with five specializations. Expand your career options and earn your degree using a convenient, flexible learning platform that fits your busy life.
Walden University is accredited by The Higher Learning Commission, www.hlcommission.org.