1. Measuring ROI from data science initiatives is challenging for many organizations as the outcomes are often not clearly defined, quantified, or attributed to the initiatives. Breaking the chain from data to insights to actions to outcomes is common. 2. A framework is presented for quantifying the value of data science initiatives using 5 steps - define success metrics, measure the metrics, attribute outcomes to causal factors, calculate net costs and benefits to determine breakeven, and benchmark results. 3. The framework is applied to a case study of a beverage manufacturer that used analytics to optimize plant costs. Key metrics like cost savings, employee productivity, and process efficiency were defined and attribution methods like A/B testing were used