Tracking Business Value with Data Science Portfolio Management

You may not have a formal “portfolio management” function within your data science team, but in all likelihood, you’re executing some of its key components already.

But being more intentional around portfolio management can pay big dividends. Without it, you could be missing out on a powerful and holistic way of demonstrating the value your team provides to the business.

In this episode, Katya Hall, Director of Enterprise Analytics at McKesson, explains how the portfolio management process sets the groundwork for defining KPIs that track the actual value derived from predictive models and insights. Plus, she shares her thoughts on a process for validating model accuracy and managing risk.

We discuss:

• Tips for working with business counterparts
• Data science portfolio management
• Model risk management
• Supply chain analytics