Showing posts from June, 2019

5 rules for a productive Science team

Data Science is a new discipline. As such, companies (incl. Tech) are still trying to figure out the optimal configuration for the team. However, there are a few guiding principles that are important to follow so that your Science team does not fall apart. I have complied my own in this blog article and I'm sharing them with you to generate a discussion: 1- Have a single source of truth During my consulting years, I cannot recall how many of my clients had challenges that emerged largely from a single problem: teams looking at different data.  Aligning on metrics and methodology is very important as it forces the team(s) into a "single view of the world". Without agreement, team progress is hampered due to definition conflict, and confusion (when metrics disagree). In practice, this can be achieved through a single "fact table", adequate documentation (with thorough definition and pointers to code) and availability of metrics where they matter