This week on The Data Stack Show, Eric and John chat with David McCandless, Founder of McCandless Consulting and former data analyst at Amazon. During the episode, David shares his journey from chemical engineering to analytics, leading to his role at Amazon. He also discusses the complexities of time series forecasting for workforce management, emphasizing the importance of explainability and accuracy, especially during the pandemic. The conversation also touches on the practical applications of forecasting in business, the need for clear communication with leaders, and Amazon’s culture of detailed documentation for decision-making. David’s insights offer a deep dive into the real-world challenges of data analytics in corporate settings, and more. 

This week on The Data Stack Show, Eric and John chat with Ben Rogojan, Owner and Data Consultant at Seattle Data Guy. During the episode, Ben discusses the complexities of budgeting for data teams within organizations. The group explores various funding models, including chargebacks and independent budgets, and considers the implications of each on the perception and effectiveness of data teams. The conversation highlights the potential for unhealthy budgeting practices, such as investing in new systems without clear business benefits, which can lead to financial difficulties for companies. The role of the CFO and the importance of aligning data team budgets with organizational goals are also examined, emphasizing the need for strategic investment in data capabilities to support business outcomes. Don’t miss this data discussion!

This week on The Data Stack Show, Eric and John chat with Ryan McCrary, Product Manager at Rudderstack. During the episode, the group explores the complexities of customer data management, focusing on data activation, identity resolution, and entity management. They also discuss Rudderstack’s profiles product, which aims to bring business users closer to data, making it actionable within their existing tools. The episode covers the challenges of stitching user profiles deterministically, handling anomalies, and the significance of reverse ETL in the data industry. They also touch on the importance of data ownership, visibility between teams, and the role of machine learning in building a data foundation. Overall, the conversation sheds light on the evolving landscape of data management and the need for structured, collaborative tools in the space.