A Study by Volantsys Analytics & George Brown College, Part 4: Communication & Business Collaboration

Welcome back to our continuing series on Volantsys Analytics’s recent study on data science methodologies and improvements, in partnership with a team from George Brown College. In Part 3 of this series, we examined the common expectations and responsibilities for various job functions within a data science team. In Part 4, we will wrap up the survey results with a look into how communication and business collaboration within a data science team might look.

Communication & Business Collaboration in a Data Science Team

As touched upon in our Roles & Responsibilities blog, the business analyst is the main conduit between business stakeholders and the data science team. The respondents of the survey shared a consensus that business communication was most often done by having the BA share data insights and results with business stakeholders (82%). Another useful communication method includes conducting workshops or demos to get approval for data science project approaches (76%). Unsurprisingly, simple email correspondence was not as favored for communication, and may only be a complementary method of relaying important messages and is insufficient on its own.

Aside from the business analyst, other members of the data science team were mainly involved in general data analysis, capturing key insights in reports and creating visualizations for those insights. With these outputs in hand, business analysts are still often the preferred relayer of information back to stakeholders. However, survey responses do suggest that the whole data science team finds value in holding regular meetings and updates with the business as well as using dashboards and other visual tools to demonstrate solutions to stakeholders. In this way, each member still has an important role and relationship with business leads and the business analyst is no longer the sole messenger of information.

That finishes it for the results and learnings from our survey! Be sure to watch out for our final two parts of this series, where we will share learnings from our expert interviews, as well as share some final thoughts on how data science methodologies can be improved.