Nailed the core tension here. The Dataverse example illustrates something I've seen repeatedly where low-code accessibility and operational stabiltiy pull in opposite directions. Back when I worked with a team migrating from prototype to production, they kept treating schema changes like notebook edits until downstream dependencies broke silently. What made it worse was the organizational assumpton that more senior data scientists would solve it, when really what they needed was someone who understood change management. The blast radius concept is spot on.
Really appreciate this perspective. You’ve captured the failure mode perfectly. We see the same pattern where early exploratory habits carry over into operational systems, and the breakage only shows up once the blast radius expands downstream. The assumption that “more senior data scientists will sort it out” is especially telling; it’s almost never a skill gap, it’s a change-management and systems-thinking gap. You’re right that part of the problem is the lack of hard-earned, shared best practices for operationalizing data science, something we’re hoping to surface more explicitly in future posts. Thanks for adding such a concrete example to the discussion.
Point superbly made!
Nailed the core tension here. The Dataverse example illustrates something I've seen repeatedly where low-code accessibility and operational stabiltiy pull in opposite directions. Back when I worked with a team migrating from prototype to production, they kept treating schema changes like notebook edits until downstream dependencies broke silently. What made it worse was the organizational assumpton that more senior data scientists would solve it, when really what they needed was someone who understood change management. The blast radius concept is spot on.
Really appreciate this perspective. You’ve captured the failure mode perfectly. We see the same pattern where early exploratory habits carry over into operational systems, and the breakage only shows up once the blast radius expands downstream. The assumption that “more senior data scientists will sort it out” is especially telling; it’s almost never a skill gap, it’s a change-management and systems-thinking gap. You’re right that part of the problem is the lack of hard-earned, shared best practices for operationalizing data science, something we’re hoping to surface more explicitly in future posts. Thanks for adding such a concrete example to the discussion.