Conquering impossible goals with real-time analytics

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“Past data only said, “go faster” or “ride better,” Kelly Catlin, Olympic Cyclist and Silver Medalist, shared with the audience at IBM’s World of Watson event on Monday, October 24. In other words, the feedback generated from all her data sources – the speed, cadence, power meters on her bicycle – was generally useless to this former mountain bike racer who wanted to improve her track cycling performance by 4.5{07c2b926d154bd5dc241f595a572d3349d41d98f2484798a4a616f4fafe1ebc0} to capture a medal at a medal at the Rio Olympic Games.

While I am by no means an Olympic level athlete, I knew exactly what Kelly meant. I’ve logged over 300 miles in running races over 8 years, and just in this past year started to see some small improvements in my 5ks and half marathons. Suddenly, I started asking, “How much faster could I run a half marathon? Could I translate these improvements to longer distances?” I downloaded all my historical race information into an excel chart. I looked at my Runkeeper and Strava training runs. Despite all this data, I was stuck. What should I do to improve? I asked a coach. He said, “run more during the week.” But, I wanted to know more – how much capacity do I really have? How much does my asthma limit me? Should I only run in certain climates? During which segments of a race should I speed up or slow down? Just like Kelly, who spent 4 hours per session reviewing data, I understood how historical data had limited impact on improving current performance.

According to Derek Bouchard-Hall, CEO of USA Cycling, “At the elite level a 3{07c2b926d154bd5dc241f595a572d3349d41d98f2484798a4a616f4fafe1ebc0} performance improvement in 12 months is attainable but very difficult.   For the USA Women’s Team Pursuit Team, they had only 11 months and needed 4.5{07c2b926d154bd5dc241f595a572d3349d41d98f2484798a4a616f4fafe1ebc0} improvement which would require them to perform at a new world record time (4.12 / 15.4 Lap Average). The coach could account for the 3{07c2b926d154bd5dc241f595a572d3349d41d98f2484798a4a616f4fafe1ebc0} in physiological improvement but needed technology to bring the other 1.5{07c2b926d154bd5dc241f595a572d3349d41d98f2484798a4a616f4fafe1ebc0}.   He focused in two area: Equipment (Bike/Tire, Wind Tunnel training) and Real-Time Analytic Training Insights.”

Women's Team USA Cycling 2016 Performance Goal

How exactly could real-time analytic insight change performance?

According to Kelly, “Now, we can make executable changes.” She and her teammates now know when to make a transition of who is leading the group, how best to make that transition, and which times of the race to pick up cadence.

The result: USA Women’s Team Pursuit finished in the race in 4:12:454 to secure the Silver medal behind Great Britain finishing in 4:10:236.

The introduction of data sets and technology did not alone lead to Team USA’s incredible improvement. Instead, it was the combination of well-defined goals, strategic implementation of technology, and actionable, timely recommendations that led to their strong performance and results.

As you consider how to improve an area of your business, keep in mind these three things from the USA Cycling project with IBM Bluemix:

  1. Set well-defined goals. Or, as business expert Stephen Covey would say, “always begin with the end in mind.” USA cycling clearly articulated they needed to increase performance by 4.5{07c2b926d154bd5dc241f595a572d3349d41d98f2484798a4a616f4fafe1ebc0}, and that would take more than a coach.
  2. Choice and implementation of technology matters. Choose the tools that will not only deliver data and insights, but do so in a timely and relevant manner for your business. Explore how to get started with IBM Bluemix!
  3. Data alone doesn’t equal guidance. You must review the data, and with your colleagues, your coach, your running buddy – set clear, executable actions.

The IBM Bluemix Garage Method can help you define your ideas and bring a culture of innovation agility to your cloud development.

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