Reliable analysis needs structure
A notebook can answer a question once. A well-structured workflow can answer it again when the data changes.
That repeatability is where software design becomes part of data quality.
Modularity helps teams think
Separating loading, cleaning, modeling, and visualization makes a project easier to inspect. It also makes mistakes easier to isolate.
The same principles that help a web app scale can help an analysis stay understandable.
Product thinking closes the loop
Good data work does not end at a metric. Someone has to use the result, trust it, and make a decision with it.
That is why I keep software design close to data science. It turns analysis into something people can actually work with.