based games for players across multiple game providers. Some of the restrictions that we found from our market research and talking to customers is that they're quite restricted to per provider promotional tools. We've now combined that on our platform, allowing our customers to offer promotions across different game providers so they can really tailor and personalise games for their tournaments.
Another key topic is Player Health. We're looking at how we can leverage the data we have around our players to identify problem gambling. What can we do around that to make sure that our customers are intervening with players at the right time and giving them the right level of support? It will be interesting to see what AI can do in this area alongside our existing technology suite to make it configurable to each market. Ultimately, we strive to have a best in class offering for our customers so they can have a mix of our in- house tools and then marry them up with third party providers. We're flexible from that perspective.
Dan: Well, there's a lot going on as always. A key focus is improving the portability of our solutions which would then allow us to deploy on-premises or in public cloud. Terefore, it doesn't matter where we need to deploy. Cost efficiency is a key thing in terms of looking at our current infrastructure and making some technology changes, adding in different technology capabilities to make things more performant or rightsizing. If you've been in the world of cloud for a while, when you get to a certain level of scale, the bill can get expensive. We want to right size some of the workloads to effectively
bring what I call the total cost of ownership of the platform down for our end customer. We want to make sure that they're running as efficiently as possible. Tat's given us a significant USP in terms of our core offering and capability.
We're working with some smart partners who have cutting edge technology. One is called Cast AI through whom we can run against Kubernetes clusters which uses machine learning in real-time to right size workloads. It's super technology and they're great guys. We have felt the benefits of this and so have our customers. We are focusing more on operational excellence and how we automate and tune everything we do. We will be rolling out new data capability this year with a data intelligence platform called Databricks that is based on Apache Spark to provide us with what's called a data lake house. Tis brings together key elements of data warehousing , ETL, analytics and machine learning into a single system. So our challenge is how do we bring millions of data points and records together and make that easy to understand?
We are utilising advancements in artificial intelligence and machine learning and translating that into product value, whether it's spotting changes in player behaviour, enhancing fraud detection, or making it easier for a customer to speak to data, understand what they've got and leverage that to improve their market offering. So, there's a lot of activity happening outside of building new product features. Tis stuff sits below the waterline but is crucial in terms of delivering value for our customers.
69
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142
