This chapter covers
- Structuring planning meetings for ML project work
- Soliciting feedback from a cross-functional team to ensure project health
- Conducting research, experimentation, and prototyping to minimize risk
- Including business rules logic early in a project
- Using communication strategies to engage nontechnical team members
In my many years of working as a data scientist, I’ve found that one of the biggest challenges that DS teams face in getting their ideas and implementations to be used by a company is rooted in a failure to communicate effectively. This isn’t to say that we, as a profession, are bad at communicating.
It’s more that in order to be effective when dealing with our internal customers at a company (a business unit or cross-functional team), a different form of communication needs to be used than the one that we use within our teams. Here are some of the biggest issues that I’ve seen DS teams struggle with (and that I have had personally) when discussing projects with our customers:
- Knowing which questions to ask at what time
- Keeping communication tactically targeted on essential details, ignoring insignificant errata that has no bearing on the project work
- Discussing project details, solutions, and results in layperson’s terms
- Focusing discussions on the problem instead of the machinations of the solution