ANALYZE THIS
How four of the world’s biggest companies—with strong Tech ties—are leveraging Big Data to develop new products, optimize processes, solve problems and boost sales.
Summer 2016 | by Roger Slavens
Not all business leaders are taking advantage of the recent explosion in data available to them. In fact, many of them have been overwhelmed by all of it and have found themselves and their companies struggling, spinning in place, as they stick to their gut instincts rather than trust empirical evidence.
Meanwhile, those that have embraced analytics and Big Data have raced to the front of the pack—or widened their already comfortable leads.
A recent study by Salesforce shows that 90 percent of high-performing business leaders view data analytics as absolutely critical or at least very important to driving their company’s business strategy and improving operations. Additionally, 84 percent of them believe that analytics will only continue to increase in importance. Such beliefs are translating directly and emphatically to increased investments in analytics activities at their companies.
But how exactly is Big Data making a difference in the business world today? The Alumni Magazine asked top analytics executives at Nike, Boeing, The Home Depot and Hershey—market-leading companies in disparate industries but with a common strategic thrust—if they could share how their businesses are using data to increase their chances at success.
NIKE
Jim Scholefield, MBA 88, Chief Information Officer, NIKE Inc.
Though he’s worked at other big brands such as Coca-Cola, Ford Motor Co. and Procter & Gamble, Jim Scholefield has been floored by the complexity of Nike and its ongoing, fast-paced growth during his first year with the company. “The scope and scale of what it takes to run Nike is staggering,” he says.
As Nike’s chief information officer, he’s responsible for all internal technology enterprise-wide—everything from physical infrastructure such as data storage and workstation PCs to financial and human resources systems to supply chain management and product development. He oversees more than 3,000 people who support Nike’s 62,600 direct employees located in 140 countries.
The company ships north of a billion units of footwear, apparel and sporting goods annually and sells them in more than 100,000 retail locations worldwide, plus 931 company-owned stores. Indeed, for Nike to follow its own motto and “Just Do It” is a considerable undertaking, and leveraging Big Data is vital to its success.
“For us, having good data and being able to act upon it is critical to our supply chain management,” Scholefield says. “If you think about just shoes, then you think of all the styles, then all the colors and the sizes for each style, that’s a lot of SKUs (stock-keeping units) and a lot of data. And then we use even more data—from logistics, retailers, market conditions, etc.—so we can deliver the exact right product at the right place and the right time to our customers.”
Nike’s technology infrastructure allows the company to get near instantaneous data from online and even retail purchases. “That real-time feedback helps us quickly make adjustments and be responsive to customer needs,” he says.
Perhaps a more tangible benefit of Big Data’s impact at Nike happens during product development. “We’ve been live testing Nike products with top athletes for years, gathering data from sports fields, tracks and gyms that helps us unleash new high-tech products,” Scholefield says. “Our Nike Sports Research Lab has some 60 people who work in biomechanics and physiology alone. They run a wide variety of tests then collect and analyze the data to show us how our designs are performing and progressing.”
One of Nike’s biggest success stories in terms of leveraging data has been its Flyknit shoe construction technology. “It’s a high-performance material that’s designed and fine-tuned for each sport,” Scholefield says. “It didn’t come from a hunch, but rather from data-driven decisions to enhance athlete performance, as well as improve manufacturing and reduce waste. The shoe uppers are knit to exactly what is needed—there is no excess material—and through Flyknit we save 2 million pounds of waste annually.”
Scholefield supervises an analytics infrastructure team that works closely with five other analytics groups spread throughout Nike’s business units. “Our strategic agenda is driven by the input we get from athletes and customers as we develop and market our products,” he says. “While some companies are happy with focus groups, we want as much data as we can get. It’s been deeply ingrained since the company’s inception that we need quantitative and qualitative feedback to develop the best products possible. At Nike, we’re obsessed with our consumers, and our commitment to their satisfaction is second to none.”
The Home Depot
Robert Thomas, MBA 17, Data Analytics Manager, The Home Depot
As the world’s largest home improvement retailer—and the fifth largest global retailer period—The Home Depot inventories and sells hundreds of thousands of SKUs in both their trademark orange warehouse-style stores and online. When you consider the supply chain, merchandising and marketing behind these products, the data at play is enormous.
“Without a solid handle on all the data involved, you’d be painting with a broad brush in making decisions, especially in terms of supply chain management and operations,” says Robert Thomas, MBA 17, data analytics manager for the company’s Assurance and Advisory Management Program (AMP). “Big Data allows us to fine-tune our efforts and become more efficient. Having the ability to look at the most granular details allows us to pinpoint where we might be making mistakes, and more importantly, where there are opportunities to improve.”
As recently as 2012, though there was a growing IT infrastructure capturing data throughout The Home Depot businesses, the analytics efforts within the AMP group were not centralized, Thomas says. Some senior business analysts quickly realized that as the tools and abilities to collect this data grew, the AMP group needed a dedicated team to lead efforts to capitalize on all this valuable business information.
Today, that data team consists of two senior analysts and Thomas, who together find themselves always working on a major data project—usually in store operations—while also providing assistance to other teams in the company faced with data challenges. “We leverage both our knowledge of the data environment and technical skillset to support AMP teams with advanced analytical support and guidance, but we also work with other groups at The Home Depot to provide them with a solid foundation in the basics, arming them with tools necessary to tackle most analytics issues,” he says.
One major business challenge at The Home Depot that data analytics has helped shine a light on is store theft, fraud and financial loss. Store associates have always been one of the first lines of defense when trying to detect these issues, Thomas says. “For example, when they see something that appears awry in their stores, they will report it to our Store Support Center so someone can research what happened and identify the impact the theft or fraud has had on the company.”
With new tools and improved IT infrastructure, the retailer is now able to capture hundreds of details about products as they move through their lifecycle—from ordering and receiving to sales and payment—and make this data from all stores readily accessible. “This has allowed us to take a more proactive approach in identifying sources of loss,” Thomas says. “Today we can quickly analyze billions of transactions and develop methodologies to identify abnormal patterns or inappropriate behavior. We can see trends across stores that any one store manager can’t. By researching these anomalies, we can detect problems more quickly and minimize the financial impact to the company.”
Thomas and his data team also have been able to use Big Data to help understand what a perfect online buying experience is for customers and identify pain points so The Home Depot could avoid them. “The end result was a scorecard that gave several business groups in the company enhanced insight into the end-to-end customer order experience and ideas for improving the process. We took care when designing the methodology around pulling and analyzing the data so that refreshing the scorecard would be as automated as possible. After completing the project, it took little effort to re-run the analysis so that we could track the progress in real time and identify any problems soon after they start to occur.”
Boeing
Ted Colbert III, IE 96, Chief Information Officer and Senior Vice President of Information & Analytics, The Boeing Co.
Boeing—the world’s largest aerospace company and manufacturer of airplanes and defense, space and security systems—is betting big on analytics. In fact, they recently doubled down by giving Chief Information Officer Ted Colbert III an additional title: senior vice president of information and analytics. “My heightened role as leader of information and analytics at Boeing recognizes the value and opportunity of data—from design to engineering to product support—throughout the company,” says Colbert, who has worked at Boeing since 2009 and has served as CIO since 2013.
“The reason we added this role to my responsibilities is to bring the data and analytics resources of Boeing together so we can identify what can and should be leveraged across the business,” he says. “It’s also to signify a culture change and drive home how important good data is to us. Our data goals are to drive productivity, to become faster and better every day.”
As CIO, Colbert oversees the global technology and information infrastructure of the company, which has offices in 65 countries and employs 160,000 people. “I’m in charge of application development and systems architecture, and everything in between,” he says. That includes the management of roughly 6,000 IT employees.
When it comes to data, Boeing is constantly looking for better ways to serve its customers, to improve processes and, of course, generate revenue. “We have lots of analytics tools at our disposal, both off the shelf and self-developed, both diagnostic and predictive,” Colbert says. “We’re not just watching data dashboards—we’re capturing massive amounts of information and working to find solutions we wouldn’t find elsewhere.”
Take Boeing’s factory in Everett, Wash., Colbert says. “It’s the world’s largest building by volume, where roughly a billion RFID [radio-frequency identification]readings are tracked a day. We have massive amounts of expensive equipment there—tools, vehicles and more that we need to keep tabs on. It’s easy to lose things in this building.”
By having sensors and RFID tags on the equipment, Boeing can pinpoint the location of all of them. “And we can learn when and why things end up being somewhere they’re not supposed to be,” Colbert says. Such data has a direct impact on the productivity of the factory’s mechanics and engineers when they’re building aircraft.
Similar data technology can be used with Boeing’s airline customers, especially when it comes to maintenance. “Just like you do with your car, airlines want to be able to anticipate when parts on their airplanes will need to be replaced,” he says. “We can help them with that by capturing data from sensors on those parts, allowing us to predict when they might have problems. Some of this is a longevity issue, and the sensors can help the airlines know when to order replacement parts. It saves them time and money and keeps their airplanes on schedule.”
But the impact of part sensor data doesn’t stop there. Colbert says the information can then be fed back to product design and development teams, providing them a feedback loop on how to improve a part’s lifecycle.
Another data-driven solution that Boeing put in place recently revolves around safety. “We devised a real-time tool that constantly captures data about safety concerns around our facilities worldwide and alerts us to potential issues,” Colbert says. “It also lets us track information over time, and see if, say, a torque wrench used in a certain situation may result in a repetitive stress injury for anyone who does that kind of work. Again, we can take what we learn back to our designers to improve how planes are built and perhaps eliminate steps that might introduce safety problems.”
Of course, Colbert says, data and analytics pervade all aspects of Boeing’s business—especially when their commercial planes are in flight. “Think of our ultimate end users, the pilots,” he says. “Think of all the instruments and sensors and data they use during any given flight. The planes are providing them with constant streams of data, as well as automatically looking for anomalies in systems to ensure the safety of flights and continuity of operation.”
Hershey
Carlos Amésquita, MBA 85, Chief Information Officer, The Hershey Co.
The Hershey Company has been around for more than 120 years, and it has remained a global market leader in the candy business through its ability to innovate and adapt to the times. Today it boasts more than 80 brands of chocolate, gum, mints and snacks—with iconic names such as Hershey’s, Reese’s, Kisses, Jolly Rancher and Breath Savers, among others—that generate more than $7.4 billion in revenue across the globe.
“Yes, we are known for chocolates, but we pride ourselves on being first and foremost a consumer-centric company that uses knowledge and insights to build brands,” says Carlos Amésquita, Hershey chief information officer. Amésquita leads the IT function globally, setting IT strategy and aligning it with overall corporate goals.
Big Data is a big part of Hershey’s commitment to knowing its customers and knowing its industry. “We collect diverse sets of data—retail point-of-sale information, shipping factors and market demographics—and ingest, integrate and harmonize it,” Amésquita says.
The company’s analytics department is completely centralized. “We all sit together,” he says. “That means there’s no disconnect or deficiencies between our IT infrastructure and architecture and our business needs for data tools and methodologies.”
While Hershey generates plenty of its own data, it also relies on its retail customers to provide data such as what products are selling, where the products are placed on shelves and what local trends are. “This data is very granular, at the SKU level and by individual stores,” Amésquita says. “We have proprietary algorithms to make sure we maintain the proper inventory levels. Just like everybody else, we’re chasing an efficient supply chain, but with our analytics focus we’re better equipped than most.”
The company also seeks out other external data such as macroeconomic forces and demographics to determine consumer demand. “Analyzing all these different streams of data and making sure it’s clean data is critical so that we can identify causality and not just correlation,” he says.
Even weather forecasts and patterns provide important insight into Hershey’s business. “Many of our products are very seasonal and winter storms can come into play at times like Christmas, Valentine’s Day and Easter—for both distribution and selling,” Amésquita says. “It’s important for us to make sure we can get products to our stores ahead of bad weather, and to know how to react if sales take a hit because our customers themselves can’t get to the stores to buy our products.”
Hershey also gobbles up research data when developing and testing new offerings and flavors. “Without that direct consumer feedback, we wouldn’t be successful in rolling out new products to market,” he says. “Innovation is a large, complex process that requires a lot of data.”
When asked if he could supply a specific area where Hershey’s analytics have driven success or uncovered surprising findings, Amésquita demurred, stating that he has to keep such case studies confidential for competitive reasons. However, he did point out one example where data analytics helped solve a market mystery.
It was no conundrum to the company why gum sales have fallen off industrywide when other sweets have been on the rise. It’s mainly because of declining cigarette usage. “As people smoke less, they chew less gum,” Amésquita says. “It’s been happening for years.”
But Hershey wasn’t sure that was the whole story. The company’s analytics gurus looked at numerous data sources for clues. Finally, they realized that as some smokers moved to e-cigarettes, gum sales did not pick up—because vaping didn’t result in bad breath they had to cover up as they did with traditional cigarettes. And by finding such cases of true cause and effect, Hershey can make better business decisions, he says.
While analytics is a fast-moving field, it’s been around for an eternity, he says. “We’ve been making data-based business decisions forever. There’s nothing really new except for the technologies that enable us to capture more data than ever and the advanced tools and expertise for finding hidden knowledge within that data.”