Data products and machine learning solutions rarely deliver as much value as expected when they go live. Apart from production issues that affect machine learning model quality after deployment, there's a whole range of external factors for which a model may not improve product success. Many companies that do not understand this tend to focus mostly on algorithm/model development and model operationalization, without knowing how to clearly track how the presence of these solutions in their product impacts product success and revenue generation. In this session, we will look into how experimentation addresses this problem and eliminates severe bottlenecks for data and product teams. Experimentation is a powerful way of iterating over multiple variations of new models, products or features, and testing their impact in live environments. It enables an organisation to identify the interests and behaviours of various segments of its userbase and serve them accordingly. This talk is targeted at data science practitioners, product managers and senior leaders alike, and will be delivered on 3 major anchors - experimental design, experimental execution and experimental analysis. At the end of the session, participants would have learned about the significance of experimental design, and how to develop testable hypotheses, clear problem statements and clear outcome KPIs. They will also understand key processes in experimental execution from platformization and experimentation ownership, to deciding when to use A/B tests or quasi-experimental methods. The crux of the session will be experimental analysis, with focus on driving business value with analytical methods, supported with use cases from top companies championing this field. It’s my aim that by the end of this talk data practitioners and leaders will go back to their organisations with better understanding of how to implement experimentation initiatives, to massively improve their products/services and business growth potential.