Four Similarities Between Triathlons and Big Data Analytics Success

[Former Member]

I recently completed a full iron-distance triathlon (2.4 mile swim, 112 mile bike, 26.2 mile run) after 9 months of training.  There are similarities between my experience as a triathlete and as a Big Data Analytics team member at SAP.  Elements of success are the same in both venues.

(1)    The first similarity is acquisition of multiple skill sets. While triathlons involve swimming, biking and running, the Big Data Analytics team must have skills in machine learning/statistics, coding, business and communication. Having all skill sets leads to the best chances of success.

 

But what is success? In triathlons, there is a well-defined finish line clearly marked as such but, in Big Data Analytics it is more obscure. I believe that crossing the finish line is not when models are built or even when results are placed into business reports.  In Big Data Analytics we cross the finish line when predictive results are baked into everyday business decisions (total business integration).  This requires we communicate and train business experts and leaders as to why predictive is important and even how to use predictive results in decision making.

This is hard work. Business integration can take longer than building predictive models and can encounter obstacles along the way.  In business we face aversion to change and ego while in triathlons we face flat tires and wind.  But only by overcoming the obstacles can we attain full success.

(2) The second similarity is that Big Data Analytics success requires dedication and determination like iron-distance triathlons. This is not a sprint race! It took me 9 months to prepare for 140.6 mile race. You can expect business integration to take months of hard effort.  

(3) Success also takes the right mix of ingredients like triathlon nutrition.  A few of the right ingredients are:

• Access to the right data
• Personnel devoted to business integration
• Curriculum that teaches business stakeholders the value of predictive results
• Communication that describes what specific predictions are made… without techno-speak
• Descriptions of how predictive fits into the business story

So what is the current status of predictive success in organizations?

I propose that many organizations fall far short of success and struggle to solve the problem of how to infuse predictive results into decision making processes. 

(4) At SAP, the Big Data Analytics team uses “Adoption Leads” to drive successful integration.  Adoption Leads are “coaches”. Their role is to communicate and train business leaders about predictive and work with them to achieve optimal results.  They are key to success of the Big Data Analytics program like coaches are to a triathlete. 

Adoption leads enter the process at the very start like coaches.  They facilitate project objectives and work to build relationships with stakeholders. Regular meetings help shape strong, trusting relationships that make it more likely model results will be used for decision-making. This is at the heart of the SAP program.

So whether you are a triathlete or a predictive analytics expert … multiple skill sets, dedication and determination, the right mix of ingredients and coaching will get you across the finish line and guarantee your success.