Metadata
- Author: Erik Bernhardsson
- Full Title:: What Is the Right Level of Specialization? For Data Teams and Anyone Else.
- Category:: 🗞️Articles
- Document Tags:: Data Team Roles,
- URL:: https://erikbern.com/2021/07/23/what-is-the-right-level-of-specialization.html
- Finished date:: 2023-07-14
Highlights
Erik Bernhardsson @bernhardsson I think this specialization of data teams into 99 different roles (data scientist, data engineer, analytics engineer, ML engineer etc) is generally a bad thing driven by the fact that tools are bad and too hard to use (View Highlight)
What are some drawbacks of specialization?
Resource allocation. If you have a chef who only chops onions, they are probably idle most of the time (View Highlight)
Reduction of transaction cost. If every project involves coordinating 1,000 specialists, and each of those specialists have their own backlog with their own prioritization, then (a) cycle time would shoot up, with a lot of cost in terms of inventory cost and lost learning potential (b) you would need a ton more project management and administration to get anything done. (View Highlight)
I often think of people as (and this is an unfair crude generalization etc) roughly on a spectrum between tools-oriented and goal-oriented (View Highlight)
But a lot of the time, experts can also be a huge liability, because they are overly biased towards picking tools that they have deep skills in (View Highlight)
Adding hand-off points because of specialization feels like putting up a Chinese Wall (in the business sense) between two functions that constrains the information flow and obstructs the value. (View Highlight)