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 distinctions to ensure that your team collaborates effectively. A data-informed business team mainly seeks to establish a shared understanding of current and past performance. A data-inspired team seeks to identify trends, correlations, and other insights that can inform a better strategy. A datadriven team focuses on using data to answer specific questions and obtain precise insights to guide
#23 Ensure high impact decisions are appropriately resourced
Analytics teams are decision partners in business decision-making. Decisions vary in complexity and impact. Ensure that initiatives are being planned appropriately such that complex, high impact decisions are not being executed as ad-hoc tasks or on unrealistic timelines by the analytics team.