Coaching for Data, Analytics, AI Leaders (21–30)

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!

The blog covers principles and patterns that have proven to be effective in my career leading teams in Data Engineering, Data Analytics/Science, and AI in both startup and large company environments.

#21 Invest in experimenting with new tools

The tools available for data science are rapidly evolving, and their usefulness can vary depending on the org’s data and analytics maturity model. It’s essential to continue experimenting with new tools and techniques rather than solely relying on incremental, internally developed plans. Establish clear responsibility for exploring, experimenting with, and implementing new tools and processes. This will help leapfrog on the maturity curve.

#22 Understand whether your business partners are data-informed vs data-inspired vs data-driven

Business teams within the org vary w.r.t. their data analytics appetite ranging from data-inspired to data-informed to data-driven. It’s crucial to comprehend these…