Cause and Effect Data Science

Genevieve Hayes Consulting Episode 30: Cause and Effect Data Science Correlation does not equal causation, as anyone who has studied statistics or data science would know. But understanding causality isn’t just important when you’re developing models.If you’re working in business and want to be recognised for your work, it’s essential to be able to demonstrate causality between what you do and the benefit flowing through to the business.In this episode, Mark Stouse joins Dr Genevieve Hayes to discuss how data science can be used to comprehend the underlying cause-and-effect relationships in business data. Guest Bio Mark Stouse is the CEO of Proof Analytics, an AI-driven marketing analytics platform. Prior to becoming an analytics software CEO, Mark had a successful career in B2B marketing and in 2014 was named Innovator of the Year at the Holmes Report In2 SABRE Awards for his work in tying marketing and communication investment to key business performance metrics. Talking Points The benefits to organisations of understanding causality.How such techniques can be applied to use cases and disciplines beyond marketing analytics.How data scientists can drive conversations about analytics at the C-suite level to maximise their impact.The potential future impact of generative AI on data science and the world in general. Links Connect with Mark on LinkedInProof Analytics Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 30: Cause and Effect Data Science first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.