Abstract: 

Leaders across industry have been increasing investment in advanced analytics, data science, and AI. Yet, many have struggled to recognize a return on their investment.
Many of these technical teams are making transformative contributions to their companies, yet they aren’t being acknowledged for it. This usually occurs simply because their success not being properly measured.
Other teams are becoming frustrated because their successful data science projects are not being translated into success business projects. This often occurs because leaders are unable to differentiate high impact data science projects from low impact ones. Without the ability to do so, leaders cannot effectively lead a team to choose impactful projects.
If leaders keep struggling to understand and identify data science impact, they will eventually stop investing in it. When that investment stops, they’ll lose the competitive advantage that these teams are indeed bringing to their business, but which is regretably not being recognized.
Fortunately, we can do better. We can lead teams to pursue high ROI projects—if we know what questions to ask and when to ask them. In this talk we will walk through what you need to be asking before, during, and after the lifetime of a data science project. This talk will give leaders the tools to:
• Identify and assess possible impact of potential data science projects
• Support projects with high probability of success
• Identify sunk costs and appropriate stopping points
• Transform data science success into success in your business

Bio: 

As the Head of Corporate Training Executive Programs, Kerstin Frailey leads the executive, management, and data literacy program development at Metis. Prior to joining Metis she worked as a data scientists for the Data, Growth, and Marketplace teams at Postmates and as the Director of Data Science at GuideStar. She holds graduate degrees in statistics, mathematical statistics, and mathematical computer science form Cornell University and the University of Illinois at Chicago. She was a data science fellow at the University of Chicago and is PhD ABD in Statistics from Cornell.