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Learn how to transition from traditional estimation practices to data-driven probabilistic forecasting in this conference talk that demonstrates why spending countless hours on estimates often leads to inaccurate commitments and missed deadlines. Discover a transformative approach that reduced estimation time by 60% while improving delivery predictability through real metrics and visualizations from an engineering organization. Explore the fundamental problems with traditional estimation methods using actual data comparisons between story point estimates and cycle times, and understand why individual task estimates fail to account for system randomness and dependencies. Master a three-tiered forecasting approach designed for different planning horizons including annual roadmaps, quarterly planning, and sprint commitments, while learning to implement Flow Metrics and Monte Carlo Simulations across engineering teams. Gain practical techniques for visualizing and communicating forecast confidence and risk to stakeholders, distinguishing between work analysis and estimation exercises, and using historical data to answer "When will it be done?" with appropriate confidence levels. Access actionable methods for probabilistic forecasting that engineering managers can implement without specialized tools, supported by real case studies showing before-and-after comparisons and strategies for overcoming organizational resistance to this data-backed approach.
