Post Title
Many data science projects produce impressive notebooks and very little change in how the business operates. The difference usually comes down to how the work is framed.
Start with the decision
Before touching data, define what decision the model will improve, who makes it, and how success will be measured. "Reduce churn" becomes "flag the 5% of customers most likely to cancel next month so retention can call them."
Get the features right
Good features beat complex algorithms. Build reusable, well-documented features in the lakehouse so they are consistent between training and production.
Choose the simplest model that works
Start with a baseline, then add complexity only when it clearly improves the business metric. Interpretable models are easier to trust and adopt.
Validate like it matters
Use proper time-based splits, test on realistic data, and check performance across segments so the model is fair and robust, not just accurate on average.
Close the loop
Put predictions where people already work, such as a Power BI report, a CRM field, or an API, and measure the outcome. Then iterate.
Data science creates value when it changes what people do. Everything else is research.
