In today’s era of artificial intelligence (AI) and machine-assisted analytics, business analysts are crucial for bridging the growing data literacy gap. Successful analytical communicators don’t wait until the end of their analysis to communicate insights. Accurately defining projects, understanding what to data to use, preventing bias, and interpreting and effectively communicating findings are all important skills for helping stakeholders understand results and get the most actionable value from automated machine learning projects.
In this webinar, Jen Underwood will walk through the best way to communicate the value of automated machine learning results with visualizations throughout the entire analytical process, from use case definition to insight implementation.
- How to define a business use case for machine learning and AI storytelling
- The process of planning, designing, and visualizing AI stories using tools like DataRobot, Tableau, Qlik, and PowerBI
- How to effectively translate quantitative insights and tell a compelling story throughout the complete analytical project lifecycle