What Makes for an Effective Data Practitioner in 2024?

When Drew Conway created his famous data science Venn diagram in 2010, he postulated that an effective data scientist would have a combination of 3 fundamental skills: hacking skills, math/stats knowledge, and substantive expertise. Now, in 2024, the role and requisite skills of a data scientist/data practitioner have drastically evolved and become even more complex; while we've managed to split out more specialized career tracks (Data Scientist/Analyst, Data/AI/ML Engineer), there remains a widespread expectation that data practitioners, regardless of role, can do all-things-data. As a community of experienced practitioners, managers, and leaders, we continually overload the data scientist/data practitioner definition, making it harder for us - and the upcoming generation of data practitioners - to know where to focus our efforts.
So, what makes for an effective data practitioner in 2024? In this talk, I'll draw from my years of teaching and professional experience in data science to propose several continuums of the technical and non-technical skills currently demanded of novice and experienced data practitioners alike.
Leaders will walk away with new ideas of how to grow and upskill their team members through various career stages. Aspiring and experienced data practitioners alike will benefit from a proposed skill roadmap to help them progress in their careers despite the ever-evolving demands of the role. And for the data community at large, we'll poke some fun at how bad we've been at defining ourselves for so long.