Coaching for Data, Analytics, AI Leaders (1–10)

Principles and patterns to build winning performance+team

If you are leading teams responsible for Data, Analytics, Data Science, or AI — this blog series is for you! Throughout my career leading teams in Data Engineering, Data Analytics/Science, and AI in both startup and large company environments, I have uncovered principles and patterns that have proven to be effective. In this series, I will be sharing these principles and patterns.

#1 Look beyond titles to find the right talent

In hiring for your team, look beyond titles! There is currently a deluge of titles in the industry: Business/Technical Data Analyst, Analytics Engineer, Data Engineer, Data Scientist, BI Engineer, DataOps/MLOps engineer, etc. These titles do not have a consistent definition and mean different things based on the data, analytics, and AI maturity of the org. Instead, focus on matching past projects to the jobs-to-be-done within your team. Prioritize on learning grit given the roles of today will be significantly different in the future.

#2 For ad-hoc analytics requests, develop the muscle to ask “so what”

Ad-hoc data exploration requests from business partners can take a significant percentage of the team’s bandwidth. Develop the muscle within the…