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Gartner’s Big Data Definition Plays Out in Sports

by Svetlana Sicular  |  August 28, 2014  |  3 Comments

As a Gartner analyst, I am fortunate to frequently meet amazing people. Qaizar Hassonjee from Adidas is not only one of them, but one of the most memorable ones among the amazing people. He is at the heart of miCoach, including miCoach Elite, the system developed in partnerships with the top soccer players, coaches and teams of the world where soccer is known as football. For instance, German national team was practicing all last year with miCoach.

We invited Qaizar Hassonjee to talk at our Catalyst conference earlier this month, and he accepted our invitation!  I was tweeting like crazy, “Everyone, drop everything, go to End-User Case Study: Smart Soccer With adidas miCoach Elite Team System!”  This session is recorded by Gartner Events On Demand, which offers analyst and guest speaker presentations from all our conferences, woo-hoo!

Qaizar Hassonjee is a passionate leader who knows how to focus and what to focus on. He leads fantastic innovations, like creation of a sensor t-shirt to monitor an athlete’s heart rate and performance. And this sensor t-shirt is washable! I am writing this blog post, because Qaizar Hassonjee and his team got big data right. Here is the Gartner’s definition of big data (which I explained in the past):

Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.

This is how the big data definition plays out in digital sports.

Part 1. High-volume, high-velocity and high-variety information assets.

This is a screenshot of adidas VP of Innovation Qaizar Hassonjee's talk at Catalyst

This is a screenshot of adidas VP of Innovation Qaizar Hassonjee’s talk at Catalyst

miCoach collects players’ heart rate, physiological parameters, geolocation and much more in real time, with a lot of unexpected uses of data.  For example, a location heat map was important to people who maintained the field.

Part 2 of the definition. Information assets that demand cost-effective, innovative forms of information processing.

It's all about analysis

The miCoach team was focusing on serving the right analytics at the right time. They did not make typical mistakes of relying exclusively on their own expertise, but involved cardiologists, physiologists, equipment managers, and of course, coaches and players.

And finally, part 3 of the definition: Information processing for enhanced insight and decision making.

It's all about the insights!


These are the main points that led to success of miCoach because of the big data insights:

  • Don’t overload with data and information.
  • Don’t sacrifice performance.
  • Don’t over-engineer.
  • Focus on integration of different components to bring a unique user experience and test, test, test.


Follow Svetlana on Twitter @Sve_Sic

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Category: analytics  data  data-and-analytics-strategies  data-paprazzi  information-everywhere  innovation  

Tags: analytics  big-data  catalyst  data-paprazzi  experience  information-everywhere  innovation  

Svetlana Sicular
Research VP
6 years at Gartner
23 years IT industry

Svetlana Sicular is passionate about bringing analytics to domain experts and helping organizations successfully compete by applying their business acumen in analytics and data science. She is convinced that domain expertise and high-value data are the greatest assets that companies should monetize in new analytics applications. Read Full Bio

Thoughts on Gartner’s Big Data Definition Plays Out in Sports

  1. […] Read the source article at Gartner Blog Network […]

  2. Karsten Scherer says:

    Thanks, Svetlana, fascinating post! I had no idea the German national team used the three Vs to their advantage this way. This reminds me of another football example, from the Serie A. AC Milan’s been doing some interesting analytics work for how they manage, train and hire for their team.

    As I remember it, ACM invested in a high-profile player in 2008 who blew his knee out within weeks. They realized they needed different criteria for evaluating talent and overall team contribution, formed a research group that analyzed every aspect of a player’s moves and the likelihood of future injury. Over time, they started using the approach differently, actively working on technique and injury prevention. Now they meet with each player 2x a month to analyze movement data – 50,000 data points per player; 200 just on how they jump. It represents a fundamental change in how the club invest in and trains its most important assets.

    (more here on ACM’s website, they call it Milan Lab –

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