AI's rugby score: National Rugby League rallies fans with analytics and unified data

[Music] the National Rugby League is the greatest game for all um passionately loved by our fans whether it's an elite game or whether it's a weekend game that's an under n or under eight playing it's infectious we are heading towards is a Direction Where We are using data for predictive Anis and forecasting and taking the business forward to the next [Music] step the heart of the sports industry is in the statistics and we are excited to work with data bricks on a proof of concept where we are trying to build live commentry uh stats um that can help us uh in the future for our commentators to have Rich stats available in subseconds Rome wasn't built in a day um so exactly the same way how we um uh looked at when having data bricks as our unified data platform and our lake house um it is a long journey to start from stabilize renovate accelerate uh so stabilize your data ecosystem and then renovate and aggregate all the connecting points across your organization then accelerate in building products and use cases um that will help your business have the competitive Advantage we are at the verge of building our one fan view single customer view across our fans participants ticketing customers merchandise customers and how can we get these circles together as much as possible and how can we aggregate the usage of our digital channels our networks and how we see the combined value um of these fan profiles with data bricks we have built this in less than 6 months we have a data and information Guild that we run within technology internally um and we have a wider technology Forum um that works across the business as well we focus heavily on our governance we focus heavily on our foundation as I said stabilized renovate accelerate uh we have spent 14 months on our stabilized phase actually that is what is letting us move with the renovate phase in less than 4 months because we did the hard yards at the start so we have the governance um quite robust and quite systemd and automated most of these data checks data accuracies data lineage everything is automated and system D for us the key factor in managing the change with AI is is to be authentic and transparent I would go with the three PS of implementing that one getting your people process and platforms right first is to train the people with the right tools the second one is the process so having a robust governance transparency uh bias mitigation process that has being socialized within the organization and then the platform choosing the right platform um and for us when we had to choose the right platform we wanted one unified data pipeline that handles security governance encryption that's where data breaks uh fit in uh right um in the mix for us [Music]