Have you ever created a data science community of practice? How did you get people involved and facilitate their collaboration?
Practice Head, Data Science & Cognitive Analytics in Banking, 10,001+ employees
As a data science and ML leader how to answer following questionHave you ever created a data science community of practice? How did you get people involved and facilitate their collaboration?
This is bit tricky question as per me. I tried this above question as a prompt with many of the popular LLMs too. ALl of the answers I got was high level corporate research or document stuff.
My two cents - Trust is something which you get only by giving first. The same concept applies here to engagement / colloboration is something you get only by giving it. But here be genuine with your purpose and context. Also find the right participants.
President, CEO, & CDAO in Services (non-Government), Self-employed
Find a group of people who are curious and want to grow/develop, and start an informal group. When I did this, I started with “lunch and learns” to gauge people's interest in the more cutting edge technologies and applications like advanced analytics and machine learning. This way, I found a group of people who were interested and had intellectual curiosity — they were intrinsically motivated. Once I knew who wanted to be part of it, we could get free or very low cost training/professional development for the business-related innovations that we wanted to explore. Then we incorporated the things that we were learning into our actual work. When leadership sees results coming out of this, they will create dedicated resources and you can start talking about a formal data science community of practice.
You’re not asking for a budget, resources, or permission. You're showing that there is an opportunity: there are people who are interested in learning and moving the business forward by incorporating newer, bleeding edge technologies and methods. You’re starting at the core with data scientists, data engineers and business analysts who are more advanced in statistics and computer science, and then spreading through to the second layer of the organization. This leads to upskilling citizen data scientists and learning how to federate data.
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Yes67%
No23%
We are working on this10%
106 PARTICIPANTS
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Analytics developers24%
Business analysts34%
Business consumers38%
Data analysts52%
Data engineers30%
Data scientists28%
Data stewards11%
Database administrators (DBAs)12%
Integration architects9%
ML and AI engineers19%
Elsewhere1%
Nowhere, we aren't adding new GenAI capabilities3%
90 PARTICIPANTS
Have you ever created a data science community of practice? How did you get people involved and facilitate their collaboration?