Finbarr Joy – SuperbetGroup CTO
Finbarr Joy – Superbet
Finbarr is Group CTO for Superbet, a new entrant into the online gaming industry. With over 20 years’ leadership experience of technology ‘at the leading edge’, Finbarr is dedicated to developing breakthrough business models, powered by tech. His prior roles have included stints driving technology transformation at global operators such as BT, William Hill and Lebara, and he also co-founded and ran Answer Digital, a leading software services company, through the 2000’s. Finbarr’s early experiences included two years with Netscape, as a member of the team that brought the web to the world, and established the enduring model for digital innovation.
Increasingly, Machine Learning is the ‘what’ (& ‘how’?) for software development initiatives. This talk explores the structure and culture mechanisms that underpin the transition from regular ‘corporate’ development to machine learning-enabled teams.
Session Full Description
Many organisations in ‘mature’ industries are now challenged to change rapidly, to keep pace with the leaders of the Consumer Internet that are now encroaching on – and often dominating -all verticals. In the pursuit of such ‘digital’ excellence, the capability for software engineering is key – and increasingly, the context for software engineering is now AI & Machine Learning – the ability to automate, and continuously optimise the customer experience, together with the core processes under ‘enterprise’ functions. This talk presents examples from varied organisational contexts for transition of teams and their work, from ‘regular development’ within ‘corporate’ functional silos to multi-disciplinary delivery units, focused on Machine-learning by combining the practices of software engineering, quantitative analysis and data science; Knowing where to start, and navigating the myriad tools options can be daunting- a particular scenario is explored for automation of Security operations using open source technologies. Participants will learn of the factors that made a difference to the organisations presented, such that the lessons learned may speed their own transitions
Actionable Takeaway #1
Appreciate the techniques that tackle ‘culture’ and help mobilise change and alignment across ‘data’, ‘development’, ‘analyst’, and ‘business’ practitioner communities
Actionable Takeaway #2
Identify scenarios that have generated successful outcomes, and pitfalls discovered in the organisations profiled
Actionable Takeaway #3
Understand freely available (open source) tools options that enable teams to get up and running with machine learning approaches quickly