the biggest problem here about this lack of process around management, around data engineering, the communication between data engineering and data science, this lack of management, if you want to specialize, you want to have a data liaison…do you want to have a data engineer specialist, because the earliest data science project, like the smallest one, data scientist is doing the data engineering work too

the biggest problem here about this lack of process around management, around data engineering, the communication between data engineering and data science, this lack of management, if you want to specialize, you want to have a data liaison…do you want to have a data engineer specialist, because the earliest data science project, like the smallest one, data scientist is doing the data engineering work too. And probably the platform architecture work too, and the application development.
Once you start specializing, which is why we have data engineers and data scientists now, these two people need to have a process to communicate.
When you have an application developer, now they need a process to communicate and work together.
You have the platform architecture, you got management, you got the advisory liaison person, you got the rest of the business, all is about process and, honestly, I don’t think anybody really knows what they’re doing. I think the number one thing that’s holding us back in this industry, is building large data science teams and organization. The most successful data science teams I see right now are like three people… it could be a massive organization, but those three people are getting a lot of work done, and if they wanted to scale up to 20 people, 40 people, it’s not going to work. — https://blog.dominodatalab.com/collaboration-data-science-data-engineering-true-false/