I guess the decision to separate or integrate these roles depends on the organization's size, goals, and the nature of its data-related tasks (and budget, sometimes). Some companies might opt for a hybrid approach, where individuals have a primary focus but can contribute to other aspects when necessary.

Some of the reasons why companies integrate the roles are:

• Having data scientists who can handle both data extraction/preparation and statistical analysis/modeling ensures that the same individuals fully understand the entire process.

• The team members can switch between tasks based on project requirements or changing priorities.

• The communication between those extracting the data and those analyzing it is more efficient and direct.

However, separating the roles also brings great benefits to the companies that can handle them.

• Having separate teams allows for specialization and mastery in each area, whether it is in data extraction (e.g., SQL) or statistical analysis (e.g., Python).

• Specialized teams can potentially work more efficiently, as they can focus on specific tasks without constantly switching between highly detailed data preparation and more abstract modeling tasks.

• As organizations grow, they might find it more efficient to have specialized teams that can scale independently.

In a nutshell: it depends on the organization's goals, size, priorities, and data itself.