RECONVERGE:G2 Tuesday morning sessions concluded with a presentation by Doug Barton of Hyper Innovation who spoke about “Imagining the Perfect Race.”
Barton discussed the source of supranatural competitive advantage via example of IBM’s sponsoring of bicyclist Dave Haas in the Race Across America (RAAM) .
Barton’s presentation was unconventional and focused on curiosity in the intent of winning the race.
Drawing on insights accumulated over 6,000 miles of competitive bicycle racing IBM used RAAM as an example of a proving ground for creating the perfect competitive race. It serves as an apt analogy for successful use of CI in business processes.
How biking serves as an example
The RAAM covers 6,000 miles, four mountain ranges, and three deserts–in eight days. The race is the toughest endurance test in the world.
Day one, for example, consists of 36 hours cycling; sleep is found two hours at a time for over 8-9 days. There are no stages, no rest days, and an open road. The race is 50% longer than the Tour de France, but contestants finish in one-third the time.
Conditions vary: there are extreme temperature shifts and 170,000 feet of vertical climb for over 32 miles. The length of the race is four times the height of Everest, and more than 15 times as many people will reach the top of Mt. Everest than complete the RAAM.
Only 200 people have finished RAAM.
Only 40-50 people will attempt the race going solo.
When Dave Haas approached IBM for sponsorship, he was a three-time top finisher, 47-years old, and had done the race 7 years prior. The odds were against him. In 2015 he took second place, finishing in 8 days, 20 hours. This was the fastest time ever by an American, and the fastest time not to win.
Dave went on to best his time by a full day with IBM’s help.
IBM put together a team to exploit technology to find the challenges Dave would meet and used the race as an exercise to test their own analytics and technologies in a creative way.
In strategizing, IBM combined Dave’s intuition about himself as well as measuring power, cadence, speed, respiration, heart rate, and his core temperature.
Metric devices connected IBM to the athlete throughout the race via Bluetooth, transport with a cellular network, and organized a crew in a follow vehicle that monitored weather conditions—they mapped the best time to ride, not just the course.
Weather data helped map all data points to describe the route. One example is they evaluated results as a consequence of wind direction, humidity, temperature, and so on to find Dave’s best time to ride, and best times to rest.
25,000 data points were measured in this effort.
Modeling performance: weather, biometrics, and elements not visible in real time were used to schedule down time. These variables were consulted as skills plus luck equal performance, but insights bring power—and did so in this race.
IBM’s competitive design goals were twofold: 1) Assure that Dave stayed safe by getting real-time data; and 2) Race smart to achieve model outcomes.
They advised Dave to hold back when his temperatures became high. They ultimately guided his effort with interventions when approaching thresholds.
Model outcomes for IBM: once assuring the athlete is safe, every pedal turn is important, so take advantage of best conditions to travel on the bike. Know how will the rider respond to road slope? How to incorporate wind to his advantage? IBM knew they could figure out impacts on his progress and determine options for best results.
For example, they could figure out wind direction relative to the road conditions and calculate tail winds over the route.
Foresight, then, creates luck:
• Dave logged seven stops and 14 hours off the bike over 8 days, 20 hours in 2015.
• Just seven decisions made well created “luck.”
• Willingness to adjust rest location and timing of it made the difference—the athlete had to be willing to adjust.
• Consider unavoidable bad luck: Differential weather.
The lesson learned is: Tune the strategy to a likely evolution of conditions; gain differential advantage; limit risk of disadvantages; and assess risk and reward.
The winner of the race was able to take advantage of a tail wind as result of a storm, Dave did not have such advantage.
Improving the foresight can improve outcomes.
In 2016, a weather forecaster was consulted to prevent the previous year’s issue, and 2016 was an easier year due to better temperatures.
Also in 2016, IBM simplified sensor arrays. Another advantage was the athlete knew his capabilities and what would push him too hard. IBM improved the life of the crew: keeping Dave connected, best methods in feeding him, etc. were simplified for efficiency.
Dave spent 17 hours off the course during the race due to an illness, but in the spirit of always learning: what was the impact of more sleep? He was able to catch up and claim second place. This RAAM was a race like no other; technology and process was improved, but the illness was a complicating factor.
“This is more than just a race—it’s an allegory about overcoming personal limitations, self-discovery, and the power of the human spirit.”
• There is a frontier of what is possible with technology and we choose where to play. But new talent, new data, and creativity of putting these things to use will allow new products and services, processes internal to the business—all will lead to growth
• Notes Barton, “The greater danger for most of us lies not in setting our aim too high and falling short; but in setting our aim too low, and achieving our mark.”—Michelangelo
Barton says to find progress, ask yourself:
• What does perfect look like? (Product, interactions, supply chain—what’s the difference? Find that gap.
• What would go wrong and how would we know?
• What questions have we not asked?
• Projects that help us learn faster are the most value projects we can invest in.
• Be mindful of your objectives and know what does “perfect” look like compared to what does a good day look like? Analytics help to predict farther by looking at individual patterns of people.
What matters is the questions we ask ourselves, says Barton. Where are we vulnerable to disruption? How do we make operations more intelligent? How do we create ecosystems?
You’re the source of imagining the perfect.
How would the questions that we ask unlock potential?