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Predictive Analytics
Discussion table
Learn from
Richard Davis
Chief Data Officer
Co-Host
Richard is Chief Data Officer at Ofcom, the UK communications regulator. He is a member of the Technology, Data and Innovation Group, and is responsible for enabling data and analytics capabilities across Ofcom. Richard joined from Lloyds Banking Group where he headed up the Innovation and Analytics Centres of Excellence.
Richard has a wealth of experience delivering advanced analytical applications based on machine learning to fields as diverse as economics, ecology, biochemistry, financial services and telecommunications. He has given expert advice in international forums on the development of data science products for businesses and establishing analytics and data science functions in large multinational companies.
Richard is a world leader in advanced analytics, machine learning and applied mathematics, he has been keynote speaker at a range of international conferences on the use of novel analytical methods applied to industry. His key strengths are in building expert teams of data scientists that are able to challenge businesses to adopt the most up to date techniques and help them adopt data driven decisioning in all areas of their work. He has a keen understanding of the strategy required to deliver data driven and analytics first business. He has experience establishing programmes of data literacy to ensure there is a culture of using data to make decisions, and working with IT and engineering teams to ensure data specialists have the best tools for their roles.
Richard was educated at University Of York, obtaining a PhD in Chemistry, comparing genetic programming and other advanced analytical methods to standard multivariate statistical approaches for disease detection. He gained a MRes in Mathematics in the Living Environment, developing artificial neural network methods for predicting insect migration patterns and dynamical systems approaches for carbon dynamics in the atmoshere. He has a BSc in Mathematics and Economics. At Henley Business School he achieved an executive MBA with a dissertation for realising strategies for real time risk management.
Richard has a wealth of experience delivering advanced analytical applications based on machine learning to fields as diverse as economics, ecology, biochemistry, financial services and telecommunications. He has given expert advice in international forums on the development of data science products for businesses and establishing analytics and data science functions in large multinational companies.
Richard is a world leader in advanced analytics, machine learning and applied mathematics, he has been keynote speaker at a range of international conferences on the use of novel analytical methods applied to industry. His key strengths are in building expert teams of data scientists that are able to challenge businesses to adopt the most up to date techniques and help them adopt data driven decisioning in all areas of their work. He has a keen understanding of the strategy required to deliver data driven and analytics first business. He has experience establishing programmes of data literacy to ensure there is a culture of using data to make decisions, and working with IT and engineering teams to ensure data specialists have the best tools for their roles.
Richard was educated at University Of York, obtaining a PhD in Chemistry, comparing genetic programming and other advanced analytical methods to standard multivariate statistical approaches for disease detection. He gained a MRes in Mathematics in the Living Environment, developing artificial neural network methods for predicting insect migration patterns and dynamical systems approaches for carbon dynamics in the atmoshere. He has a BSc in Mathematics and Economics. At Henley Business School he achieved an executive MBA with a dissertation for realising strategies for real time risk management.
Ed McKeown
Director of Analytics at Experian
Co-Host
I have worked for the credit reference agency for just under 15 years across a range of business lines and roles. I'm focused on supporting Experian's clients to get the best out of the available data to make balanced and fair lending decisions.
About the session
What kind of challenges and risks in government can predictive analytics solve? With the data we have currently available, how best can departments preempt problems rather than react to them.
- What are the public sector's low hanging fruit when it comes to predictive analytics? Where should departments look for quick wins?
- How can predictive analytics be used to support preventative interventions in the areas of healthcare, social welfare, or crime prevention? What kind of data infrastructure and governance frameworks are required?
- How can predictive analytics be used to support more personalised and targeted service delivery? What kind of data set-up is necessary to tailor services and interventions to the unique needs and circumstances of different groups of citizens?
- What kind of governance is required to ensure that predictive analytics is used in a way that is reliable, accurate, and unbiased?