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5 Brutal Truths Why Data Scientists Get Treated Like Support (And How to Stop It)

Data Science becomes “support” the moment anyone can bypass your roadmap.

Author

Glenda Carnate
February 12, 2026

Read time: 2.5 minutes

Your request will be manageable at first, but overtime requests become too large and complex, where all of your requests are mixed together into one heap of work, with all of your communication occurring via Slack, along with random meetings you were not prepared for.

As a result, the stakeholders will continually choose to engage with data science as their assistant rather than as a partner due to their experiences with you solving their problem quickly.

Enforce Tech and Commercial Leverage:

1. When everything is crucial, the DS function becomes a help desk.

Ad-hoc project work leads to no standard.

Technical Solutions

  • Ticket intake to OKR(s)

  • Requests without business value are not accepted

  • Non-roadmap work will have capped sprint capacity

Business Solutions

  • Quarterly allocation of DS's capacity is published

  • If the executive trades away work to take on others, they must approve them

  • There will be no trade-off or re-prioritization without executive approval.

2. Reporting on models vs. outcomes has a tactical feel.

Volume of models is just noise.

Technical Solutions

  • Log impact metrics in the Model Registry

  • Measure the lift created by the changes, cost avoided or risk delta

Business Solutions

  • Present only impact metrics to Leadership

  • Eliminate the slide related to results from an experimentation volume

  • If no impact can be measured, no spotlight.

3. If you are bypassing your roadmap, it is not strategic.

Reactive teams cannot provide direction.

Technical Solutions

  • Version control the roadmap in Git

  • PR approvals are required for a scope change

  • Link the product roadmaps to production models

Business Solutions

  • Link the roadmaps to the company's OKR(s)

  • All new initiatives must have an executive sponsor

  • No executive sponsor → no resources.

4. Data Science (DS) only provides functionality as long as models are not utilized.

Optional models result in optional behaviour.

Technical fix:

  • Models deployed to core via API

  • Predicted output before sign off

  • Track model adoption and overrides.

Business Solutions

  • Align incentives to model usage

  • Performance review basis for AI usage

  • AI won't produce different behavior without changes in that behavior.

5. Low-impact work reduces influence.

Speed with no selectivity results in a lack of leverage.

Technical Solutions

  • Establish minimum impact criteria for models

  • Enforce quarterly termination criteria (ROI, drift, adoption).

Business Solutions

  • Publicize the termination of low-impact projects

  • Maintain capacity on the top 3 initiatives

  • Visibility maintains focus.

💡Key Takeaway: 

If you do not enforce a functioning data architecture, your data science function becomes an extension of your insight team. The architecture, as well as the accountability for the data, will set the confines of how and why data science functions as a function... therefore, data science functions as leverage, not support.

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