#59 Creating the Link Between Business and Data with Tony Gruebner - GM Analytics, Insights and Modelling

Tony Gruebner.jpg

Tony Gruebner is the GM Analytics of Insights and Modelling and the Exec Sponsor of Personalisation at Sportsbet. He established a department of 40+ skilled analysts and data scientists tasked with creating innovative data products focused at improving the experience for their customers and supporting the business by providing relevant and timely information and insights that steer decision making across all levels of the business. He has served on the Executive Leadership Team from 2016.

In this episode, Tony explains how he started in data and what led him to get his job at Sportsbet. Tony got a call from a recruiter asking if he wanted to do work with analytics, in a company that does sports and is heavily digital. All of those factors checked the box for Tony, and he took the entry-level analyst role. Over time, the need for analytics has grown, so he has been able to develop some analytics teams. 

One thing Tony does with Sportsbet is getting his data scientists to understand the link with the business, and the company to understand the connection with data science. The main strength of a data scientist is creating models; however, you need someone from the business side to tell them what kind of problems they need to be solved. Then, the data scientists can work on how to solve the issues numerically. Also, the business can consider things they currently cannot do, but data scientists can enable them to do these things. For Sportsbet, everything they do is to improve the customer experience.

Then, Tony explains how his team communicates how data science works. They actually painted out what the modelling cycle looked like and presented it to a lot of people. The feedback he got back is people understood the process and where the difficulties lie in the process. Communicating this always is challenging, especially when embarking on a project that may take six to twelve months to accomplish. Tony suggests breaking up the project into little chunks to avoid miscommunications. In one instance, after six months of work, the company explained the data was not solving the problem they wanted to be addressed. If Tony broke up the project into smaller pieces, this could have been avoided.

Later, Tony explains how Sportsbet is trying to scale globally and all the nuisances that come with the territory. For instance, they need to figure out how to acquire global talent and to overcome uncomplimentary time zones. Also, his team is working on how to utilize artificial intelligence to solve problems. Some of the models work on improving the customer experience directly. Just like how Netflix recommends movies, they are working on recommending a specific horse race to the customer; however, when the race is done, it needs to disappear from the site. Whereas, when Netflix recommends a movie to a customer, it can stay there for potentially years. Sportsbet is working on artificial intelligence to improve these models for their consumers. 

Enjoy the show!

We speak about:

  • [01:20] How Tony got started in data

  • [08:20] Tony’s skills come from the commercial side

  • [11:10] Linking data science and the business

  • [14:30] Communicating how data science works

  • [17:00] Steps to getting others to understand data science

  • [20:40] Getting the best talent for your team

  • [24:00] Structuring teams and the department

  • [28:10] Transiting from analytical roles to commercial roles 

  • [35:30] Working on global expansion

  • [38:10] Solving with artificial intelligence

  • [42:30] Passionate about using numbers to reach an outcome

  • [44:00] Modelling failures with Sportsbet 

  • [47:50] Imposter syndrome in data science  

  • [50:05] Data science is rapidly changing and exciting


Tony’s LinkedIn: https://www.linkedin.com/in/gruebz/

Sportsbet: https://www.sportsbet.com.au

Tony’s Twitter: https://twitter.com/gruebz?lang=en


  • “There is no one path that always works.”

  • “There are literally thousands of things data scientists couldn’t potentially tackle in any business.”

  • “If you’re not making mistakes, then you aren’t pushing the envelope hard enough.”

  • “Not having imposter syndrome is a sign of lack of knowledge.”

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Tony Gruebner is based in Melbourne, Victoria, Australia.

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