For companies that don’t have dedicated DevOps teams to help with these infrastructure issues, the responsibility often falls on the data scientists to fend for themselves.

Relying on disparate technologies can be incredibly challenging as they all follow different release cycles, lack institutional support mechanisms, and have varying performance deliverables.

By viewing data through separate lenses, collaboration is very difficult, trust in the analytics can be misplaced, and speed of innovation is slowed.

Exploring data at scale can be difficult and costly.

Training complex machine learning models against massive data sets can be very challenging in isolation without the ability to collaborate on models with peers.

Part of the role of a data scientist is the need to share results with team members and stakeholders for input and decision making. The trick is sharing the insights in a way that resonates with non technical audiences. The inability to do so can hamper cross team collaboration and slow progress.