Case Study

Create your own luck with analytics

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The US is the third largest country in the world by land area, behind only Russia and Canada. Like those vast countries, the US features a variety of terrain—from mountain ranges and deserts to plains and forests. Travelers across the US also encounter a wide range of weather conditions, even in summertime: sweltering heat, high winds, torrential rain—sleet and hail. Now, imagine riding a bicycle through all of that with barely a break—rain or shine.

That is exactly what a group of highly motivated ultracyclists attempt beginning Tuesday, June 14th in Oceanside, California. The intense challenges and painstaking approach to overcoming obstacles and triumphing over circumstances is a story that’s not limited to cycling. It also mirrors the extreme conditions and audacious goals that people face in many areas of life, including business. Indeed, having the right information and analytical capabilities for the business challenges you face can help you model your business accurately, make quick, insightful decisions and make the difference by turning adversity into advantage. Let’s call it creating your own luck.

Gain insights through analytics

Meet Dave Haase. This year, at age 48, he will be in his sixth Race Across America (RAAM). In this race, one of the toughest endurance challenges in the world, cyclists race 22 hours out of every 24, traveling over 3,000 miles from the Pacific coast to the Atlantic. During that trip, they climb 170,000 vertical feet while crossing four mountain ranges and three deserts—and the leaders will do it in less than nine days. Racers begin in Oceanside, California, then travel a prescribed route that takes them through the Sonoran Desert, over the Rocky Mountains, through the rolling plains of Kansas, across the Midwest and over the Appalachian Mountains to the finish line in Annapolis, Maryland.

To put RAAM in perspective, racers in the Tour de France ride no more than 139 miles in a day. But a cyclist in RAAM might ride up to 400 miles a day! As described by John Howard, an endurance legend who was named the cyclist of the decade in the 1970s, and who also climbed to the summit of Mount Everest, RAAM “makes the Tour de France look like a cup of lemonade by the swimming pool.” Climbing Mount Everest is more dangerous, he says—but RAAM is more difficult.

Dave Haase is a four-time top American finisher in RAAM, and he has plenty of experience. But, rather than relying on experience and intuition alone, in 2015 and again this year he teamed up with IBM Analytics to build a “digital profile” of his capabilities, using Watson Analytics to analyze his training records. The IBM Analytics team used insights gained from Dave’s digital profile to calibrate predictive models aimed at executing the perfect race. The goal is simple: Help Dave finish at the front of the pack! And indeed Dave was in the front of the pack, finishing in second place in 2015—only 12 hours behind the winner after eight days of nearly nonstop cycling.

Dave’s challenge was daunting. Less than half the riders who start RAAM finish the race. Many suffer heat exhaustion, heat stroke, dehydration and fatigue. How can such challenges be overcome? The answer lies in the data.

Use data to make the difference

Temperatures often rise as high as 120°F in the Sonoran Desert, and Dave has to keep his core body temperature in the correct range. Every 12–24 hours, he swallows an activated temperature-sensing pill, which allows a radiofrequency device to continuously report his core body temperature, via the cloud, to the mobile devices of his crew following in the team chase vehicle. When Dave’s temperatures rises to 102°F, the crew will radio Dave, giving him the choice to either ease his pace or use other methods to cool down. One of them includes a creatively employed sports bra!

By monitoring his temperature and responding appropriately, Dave will stay safe through a desert that last year claimed 60 percent of racers who were completing a shorter race at the same time. Dave’s success in the Sonoran Desert is a perfect demonstration of how sensors, networks, and software (like those enabled by the Internet of Things, the AT&T network, and the IBM Cloud) are changing our world by using data and connectivity to make the difference. This year, aided by wearable technology from Garmin, new, live feedback on gear selection, and refinements to his strategy, Dave expects to do even better.

Model performance to boost outcomes

Now consider the issue of fatigue. During RAAM, the clock never stops—racers who sleep too long will lose. Winners ride as many as 400 miles a day and most sleep no more than two hours in every 24. Such a grueling schedule requires riders to carefully choose the best times and places to rest. Because the route is prescribed precisely, riders make only two kinds of choices: how hard to push themselves and when to rest. During RAAM, a rider must make the latter decision seven or eight times—about once in each 24-hour period.

To help Dave make better-informed choices, the IBM Analytics team modeled alternatives that allowed us to see differences that would otherwise be hidden by averages. A model need not be complex—indeed, models need be only as sophisticated as the decisions require. Our model, for example, needed to determine how far Dave could ride during a given time period, taking into account his effort (power), the slope of the road and expected wind conditions (courtesy of data provided by The Weather Company) on a road whose direction changed continuously.

We plotted Dave’s location and the expected wind speed relative to the road at each of more than 25,000 geographic waypoints along the route, simulating a range of rest times and durations at a variety of locations.

How much can modeling help Dave’s effort? In 2015, Dave logged seven stops, totaling slightly more than 14 hours altogether, during his eight-day, 20-hour ride. We calculate that the decisions made by Dave and his crew, using data from our models, helped save him 12 hours of riding time for no additional expenditure of watts or calories—simply by choosing resting times and places that enabled him to awake to more favorable riding conditions. (Just as these simulations allowed Dave and his crew to identify “lucky” conditions, businesses can leverage weather data to enhance their own decision making—thanks to The Weather Company, An IBM Business.) We can’t wait to see what’s possible this year!

Adapt to changing conditions

Dave, like most endurance athletes, holds to the conventional wisdom that an “even effort” helps sustain the body over long distances, whisking away the byproducts of exercise and maximizing performance. But Dave’s performance didn’t conform to his, or our, expectations. Rather, we saw the watts expended strongly and positively correlating with the slope of the terrain. Our racer, we found, had an unforeseen ability to vary his output to meet a wide range of race demands. When we incorporated this insight into a predictive model, its accuracy improved so much that we could nearly hand him his water bottles blindfolded.

Learn from the pros

What lessons for business can we glean from this extreme sporting event? One of the best is ‘measure your errors’— they can lead to insights and enhanced foresight.

Dave’s test of endurance teaches us many things that we can apply to our performance in business. We must strive for continuous, dynamic alignment of resources that can create opportunities for growth and profit, helping the organization realize its full potential and thus create value for all stakeholders. While this pertains to all areas of business life, finance professionals for example, can use IBM financial performance management solutions to perform sophisticated financial analysis and adapt quickly through rolling forecasts, just as Dave Haase adapted to changing conditions during RAAM.

The next time you’re trying to win big in business, take advantage of the lessons we learned while helping Dave create his own luck to race his perfect race:

  • Know what drives performance, whether internal resources and capabilities, external and environmental factors or competition.
  • Instrument and continuously monitor the things that matter in your sector:
    o Foot traffic in retail.
    o Sentiment among would-be customers in consumer packaged goods.
    o Employee attrition in high-touch financial services and wealth management.
  • Model your business and create a playbook for interventions:
    o Mix descriptive models with both predictive and prescriptive analytics, using decision optimization to choose the best alternatives.
    o Know when you are wrong—and by how much.
    o Learn from errors, and continuously reiterate.
  • Most important, build an excellent team.

Dave Haase isn’t alone in his reliance on IBM Analytics. Organizations around the world are unlocking value with many of the same innovations that Dave demonstrated on the proving ground of the Race Across America:

  • Point Defiance Zoo found correlations between ticket sales and weather conditions, allowing it to adjust staffing, scheduling and promotion efforts to best effect.
  • BC Egg is modeling supply chains and monitoring safety in an entire Canadian province whose populace loves omelets.
  • Waratah Rugby in Australia is measuring athlete fatigue and predicting physical stress long before players are sidelined by injury.

Sign up for the IBM Planning Analytics self-service technical preview and see how this financial performance management solution can help you speed up your budgeting, planning and forecasting.

Join Dave Haase, the four-time top American finisher of Race Across America and last year’s runner up at Shape, an AT&T Tech Expo. Explore the possibilities of technology and innovation throughout scenic AT&T Park in San Francisco on Friday, July 15 and Saturday, July 16. Register now. (Kids 17 and under get in free!)

And be sure to track Dave and his Team 288 crew online. Go Dave!

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