Data engineers and ML teams are clashing over project priorities. How do you align their expectations?
When data engineers and machine learning (ML) teams clash over project priorities, it can stall progress and create friction. Bridging the gap requires clear communication and a shared vision. Consider these strategies to align their expectations:
How have you managed to align team priorities in your projects? Share your experiences.
Data engineers and ML teams are clashing over project priorities. How do you align their expectations?
When data engineers and machine learning (ML) teams clash over project priorities, it can stall progress and create friction. Bridging the gap requires clear communication and a shared vision. Consider these strategies to align their expectations:
How have you managed to align team priorities in your projects? Share your experiences.
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To align the priorities of data engineers and ML teams, I have found that fostering a collaborative environment with transparent communication is crucial. We start by establishing common goals that both teams understand, highlighting how each of their roles is vital to the project’s success. Regular check-ins and status meetings help to discuss progress, address any issues, and recalibrate priorities as needed. Developing a shared, detailed project roadmap that outlines specific responsibilities and timelines ensures everyone is on the same page and can see how their work fits into the bigger picture. This approach has helped in reducing friction and promoting a unified effort toward achieving project objectives.
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We can have a joint workshop to map out dependencies and align on timelines. In this way we can clarify roles and also foster mutual respect for each team's contributions.