Create your own luck with analytics

The US is the third largest country in the world by area, behind only Russia and Canada. Not surprisingly, the US features a variety of terrain — everything from mountain ranges and deserts to plains and forests. Travelers across the US will also encounter a wide range of weather: sweltering heat, high-speed winds, torrential rain — perhaps even sleet and hail. Now imagine cycling across this vast country with barely a break — rain or shine.

That is exactly what a group of highly motivated ultracyclists did in summer 2015. But their story isn’t merely one about cycling. It also mirrors the extreme conditions and audacious goals that people face wherever they are, whether in racing or in business. Consider, for example, how the office of finance can leverage the analytical capablilities of financial performance management technologies to steer business performance through quick, insightful business decisions. Indeed, having the right information and analytics, including real-time, internal and external data, can help you model your business, allowing you to make decisions that turn adversity into advantage. Let’s call it creating your own luck.

Gain insights through analytics

Meet Dave Haase. This year, at age 47, he decided to take part in the Race Across America (RAAM) for his fifth time. In the race, one of the toughest endurance challenges in the world, cyclists race 22 hours out of every 24, traveling 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 fewer 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 race’s end in Annapolis, Maryland.

To put RAAM in better perspective, racers in the Tour de France ride no more than 139 miles in a cycling day — but a cyclist in RAAM might ride up to 400 miles. As described by John Howard — an endurance legend who was named the cyclist of the decade for 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, a three-time top American finisher in RAAM, had plenty of experience already. But this year, rather than relying on experience and intuition alone, he teamed with IBM Analytics to build a digital profile of his capabilities by 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 perfect race execution. The goal was simple: Help Dave finish at the front of the pack! And indeed Dave was in the front of the pack, finishing in second place — only 12 hours behind the winner.

Like Dave, we must develop a perspective on success and failure when we face a challenge. And Dave’s challenge was a significant one: Fewer than half the riders who start RAAM finish the race. The causes are understandable: 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

In temperatures that rose as high as 120°F in the Sonoran Desert, Dave had to keep his core body temperature in the correct range. Every 12–24 hours, Dave swallowed an activated temperature-sensing pill, allowing a radiofrequency device to continuously report his core body temperature, via the cloud, to the mobile devices of the crew following in the team chase vehicle. When Dave’s temperatures rose to 102°F, the crew radioed Dave, giving him the choice to either temper his effort or load more ice into his creatively employed sports bra.

By monitoring his temperature and responding appropriately, Dave made it through a desert that 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 and software are changing our world by using once incidental data to make the difference. And successes such as Dave’s aren’t restricted to individuals. For organizations, the IBM Internet of Things Foundation provides a platform for this essential transformation.

Model performance to boost output

Now consider the issue of fatigue. During RAAM, the clock never stops — racers who sleep too long can lose. Winners ride as many as 400 miles a day and sleep no more than 2 hours in every 24. Such a grueling schedule requires that riders choose the best times and places to rest. Because the route is prescribed, riders make only two kinds of choice: how much to exert 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 allow Dave to make informed choices, we modeled alternatives that allowed us to see differences that would otherwise be hidden by averages. However, 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 horizon, 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 was ever changing.

We anticipated Dave’s location and the expected relative wind speed at each of more than 25,000 geographic waypoints along the specified route, simulating a range of rest times and durations at a variety of locations. And just as these simulations allowed Dave and his crew to identify “lucky” conditions, businesses also can leverage weather data to enhance their own operational performance and decision making — all thanks to the groundbreaking IBM and The Weather Company strategic alliance.

How much did modeling help Dave’s effort? Dave logged seven stops, together lasting slightly longer than 14 hours, during his eight-day, 20-hour ride. And we calculate that the decisions made by Dave and his crew, using data from our models, helped save Dave 12 hours of ride time for no additional expenditure of watts or calories — simply by choosing resting times and places that saw him wake to favorable riding conditions.

Adapt to changing conditions

Dave, like most endurance athletes, holds to conventional wisdom, which says that an “even effort” helps the body sustain effort over long distances, whisking away the byproducts of exercise and maximizing performance. But Dave’s performance didn’t confirm his expectations. Rather, we saw watts strongly and positively correlating with slope. Our racer, we found, had an unforeseen ability to vary his output to meet a wide range of race demands. And when we incorporated this insight into a predictive model, its accuracy improved so much that we could nearly hand him his water bottles blindfolded. What’s the lesson for business? Measure your errors — they can lead to insights and enhance 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 organizational resources that can create opportunities for growth and profit, helping the organization realize its potential and thus create value for all stakeholders. 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 enhance his performance during RAAM.

Learn from the pros

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:
  • Foot traffic in retail.
  • Sentiment among would-be customers in consumer packaged goods.
  • Employee attrition in high-touch financial services and wealth management.
  • Model your business and create a playbook for interventions:
  • Mix descriptive models with both predictive and prescriptive analytics, allowing you to use decision optimization to choose alternatives.
  • Know when you are wrong — and by how much.
  • Learn from errors, and continuously reiterate.
  • Most important, build an excellent team.

Dave isn’t alone in his reliance on IBM Analytics, which is now helping organizations unlock the value of the innovations that Dave demonstrated on the proving ground of the Race Across America:

  • Point Defiance Zoo gained insight into ticket sales and weather correlations, 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 is measuring athlete fatigue and predicting physical stress long before players are sidelined by injury.

Attend IBM Insight 2015 to meet Dave Haase in person at the Insight Solution EXPO and to hear more about his story in a general Insight session. In the meantime, sign up for a 15-day free trial of IBM Planning Analytics, a financial performance management solution designed to help you speed up your budgeting, planning and forecasting.

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Originally published at www.ibmbigdatahub.com.

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