Bridging the Gap: How to Effectively Communicate Between Data Science and Business Teams

I am often asked about how I manage to effectively communicate between the Data Scientist/ML Engineers (technical team) I work with and the business team (non-technical team).

Here are some of the strategies that really work for me:

1. Outcome, not process: If there's something to discuss with non-technical teams, then the business outcome and value of what we're doing is the key interest, rather than the technical method of achieving that outcome.

2. Analogies and visualization: Difficult concepts are less daunting when related to something more understandable or visual.

3. Establish a common vocabulary: Provide a common language to the team members for discussing project-related matters both at technical and nontechnical levels.

4. Regular 'translation' meetings: Establish meetings in which technical progress is explicitly translated into business terminology.

5. Encouraging curiosity: Creating an environment where the non-technical staff is unafraid to ask questions and your technical staff feel motivated to explain their work.

6. Two-way learning: the technical teams learn about business goals, and business teams learn about the possibilities from a technical standpoint.

7. Summary Iterative feedback loops: Provide for regular check-ins to ensure that the projects remain aligned with business needs and technical feasibility.

This ability to translate technical complexity into business value, and vice-versa, is not a nice-to-have skill. It drives making sure our AI efforts serve business goals and provide actual value.