Building a world-class data org | Jessica Lachs (VP of Analytics and Data Science at DoorDash)

Building a high-impact data organization requires a centralized model that maintains a seat at the table, ensuring analytics functions as a business driver rather than a service desk. By organizing data teams into pods that mirror business units, companies align incentives and foster cross-functional collaboration while preserving a consistent talent bar and clear professional development paths. Effective data leadership relies on ruthless prioritization, where teams communicate trade-offs to protect time for exploratory, high-ROI deep dives. Metrics should remain simple and actionable, often utilizing proxy measures that correlate with long-term business outcomes like gross order value. Cultivating a culture of extreme ownership—where data scientists engage directly with customers and operational challenges—transforms data teams from passive observers into proactive partners capable of identifying and eliminating systemic inefficiencies, such as referral fraud or service failures.