I’ve recently started learning Alteryx and can build basic workflows, but when multiple conditions, null handling, and transformations come in, I’m not always confident my logic is right. The workflow runs, but I’m unsure if it’s clean or scalable. Would love guidance from seniors on how you think through workflow design and avoid messy workarounds early on.

Alteryx is often praised for speeding up analytics workflows, but the real value shows up in day-to-day use. From data prep and blending to automation and reporting, many teams rely on it to reduce manual effort and turnaround time.
I would love to hear from practitioners: what’s one workflow or use case where Alteryx saved you the most time compared to traditional scripting or manual processes?

For projects involving customer data analysis, how do you balance data transformation steps (like aggregation, enrichment and deduplication) to maintain both data quality and model performance, especially when using tools like Alteryx before feeding the data into machine learning models?