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Data, Performance | Feb 26, 2019

How Cirque du Soleil Use Data to Optimise Core Efficiencies While Gaining the Trust of their Artists

Bryan Burnstein, Head of Performance Science at Cirque du Soleil, says that data must lead to good, intentional conversations.

A Leaders Performance Institute article in association with


By John Portch

“When it comes to data, I think there’s a couple of key things. Firstly, it’s not about how much you collect – it’s about making the most out of what you do collect.”

The Leaders Performance Institute is asking Burnstein about the efforts of circus company Cirque du Soleil to gain the trust of its artists when it comes to data collection and interpretation.

He continues: “You’re only collecting what you need. We collect a fair bit of it but it’s important during that process that you’re explaining to them why we’re collecting the data, what’s going to be collected and who’s going to have the opportunity to look at it.

“The data that we use to manage the performers or their performance, we make sure that we share that with them and it’s transparent between our coaches, our performance medicine team, and the performer themselves so that we’re all talking about the same information when we use that.”

Burnstein goes on to discuss how data is optimising core efficiencies at Cirque, by identifying performance gaps in a way that is sustainable and places the artist at the centre.

What is your guiding principle when it comes to using data at Cirque?

BB: We don’t want to dehumanise a decision. Data should lead to insights, it should lead to good intentional conversations about what’s going on with the individual, but we we’re dealing with human performance and we have to keep the human at the centre of that. Right now where we’re at with our data, we don’t create alarm systems or immediate actions based on certain data points, we use it as a method to become more targeted, more individualised and ensure that the performer has a voice into how we manage them.

Do you collect real-time data?

BB: We’re looking into it but we’re not currently using large numbers of wearables or monitoring-type equipment. What we do in real time is that we have other types of things such as looking at wellness or we’re just now working on a global load management type strategy, which will give us the opportunity to better manage load and the response to load in the day to day and week to week but we’re not doing that through wearables and technology we’re doing that through other forms of data that will be available at the shows in real time.

How often will your team go with their gut feeling?

BB: By and large I think the data we’ve collected has been helpful in supporting a lot of our processes, which had been based on intuition. It’s been nice, especially early on, in the evolution of data and technology to rally support and gain buy-in and trust, is to use that opportunity to test things that you think you know but it’s nice now to be able to support it with some evidence, then you get buy-in from your performers, buy-in from your coaches, you get buy-in from your business units and then you begin to start to create questions around things that you want to know more about. That was early on for us, let’s just see what we can collect and see if it lines up with what we think we know; in some cases we learnt some things but in other cases we were in many cases making good decisions already. And then you have the opportunity to start to challenge that, to start to better understand. Oftentimes we think we know what the problems are but because of that we jump to solutions to biased questions that lead us to finding false answers but instead now you can go back and work with the experts around the shows, the experts working with the performers, and really understand what their challenges are, and once you understand those challenges you can frame a problem that you want to try and address and begin to use your data to the lead to finding the optimal solution.

To what extent can you blend your historical and current data?

BB: We try to make sure that our processes evolve over time, the expertise and knowledge of our team evolve over time. Just because things evolve doesn’t mean that the data we collected in the past isn’t relevant. We try to make sure that we keep all of those datasets and that when possible, when we’re replacing something, are we replacing it for the right reasons. If we’re changing something is it because whatever’s available now gives us better insight and not just changing for the sake of using some new technology or platform. It’s got to be that it really optimises our core efficiencies for our team. Otherwise, if we are going to evolve something then we ensure there’s an appropriate transition, that we still have enough data in time to be able to use that to guide our decision-making process and then make sure that it’s still relevant to our content.

How do you anticipate tech and data usage evolving over the next few years?

BB: I think the secret sauce these days is not in what you’re collecting but it’s in making sense of that. In some senses we’re really fortunate because in the size of our company we’ve got amazing expertise that exist and because we’re not really in competition with a lot of people we’ve got some really great collaborators and partners. Outside of Cirque, we’re able to draw on a lot of expertise from around the industry to help maybe accelerate our learning in some areas. It’s not really a threat, it’s an opportunity to grow and learn. For us to be able to translate that and make it impactful, for us, the data has to be meaningful at the show level, it has to be something that delivers value and it doesn’t create more work day to day for the people who are managing the performance and the show and that’s where sometimes things get tricky for the use of that stuff.

What steps have you taken to ensure your data is sustainable?

BB: There’s a couple of things there. For a company like us we have to balance central intelligence with local need. What I mean by that is yes there’s things that we’re putting in that are minimum essential requirements across the board and then we also provide some latitude and flexibility that on a particular show if there are particular pieces they would like to do that are relevant to the acts or disciplines on their show or provide utility to the experts that are on that show they have the right to do that; so whatever else they feel they want to do above and beyond but not in place of what we already require across the company. That being said, all of it, the sustainability, it dependent on it being valuable and meaningful at every level so it’s something that the performers themselves, the coaches, the therapists, the directors, the business units, all the stakeholders have to be bought in to the use of that kind of stuff and to find value in it; if they are doing it then even if we transition coaches on a show the fact that it’s so grassroots and targeted, you’re not going to change it.

What questions will you be looking to answer in the next 12 months?

BB: Are we getting the most from the tech we currently have? Before investing in new tech, have we maximised what we have and does the new tech or opportunity impact on our pillars of human performance management? If gaps are identified, is there something out there that makes us better? If so, we’ll look at it. We are looking at the landscape of our data and tools to see if we’ve maximised the integration of our data. We have lots of tools and systems each of which continues to evolve, therefore we need to take the time to revisit these connections to be sure we are able to extract the necessary insights from our data.

What tech and data innovation are you most excited about?

BB: Though we have a lot of data, once we start to slice away layers to gain insight in particular areas, the data becomes a little thin. Therefore, I believe the use of AI presents an interesting opportunity to accelerate our learning and maybe help us ask better questions or lead us to insights we are not able to currently detect.


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