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# What is the right level of specialization? For data teams and anyone else.

**[Erik Bernhardsson](https://daily.dev/sources/erikbernhardsson)** · 6 min read · 0 upvotes · 0 comments

## Summary

Erik Bernhardsson argues that the growing fragmentation of data teams into highly specialized roles (data scientist, data engineer, ML engineer, etc.) is largely a symptom of poor tooling rather than an inherent organizational need. He frames the debate not as pro- or anti-specialization, but as a question of finding the right level. Key drawbacks of over-specialization include resource underutilization, high coordination costs, and information silos between product lifecycle stages. He contends that better, easier-to-use tools would reduce the need for narrow specialists, and that goal-oriented generalists often deliver more end-to-end value than tools-oriented experts who may bias decisions toward their own skill set. The post ends with a call to build tools that empower individuals to own the full lifecycle of their work.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://erikbern.com/2021/07/23/what-is-the-right-level-of-specialization.html>

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#devtools](https://daily.dev/tags/devtools), [#kubernetes](https://daily.dev/tags/kubernetes), [#data-engineering](https://daily.dev/tags/data-engineering)

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