With the rapid growth in the data industry, the data roles are beginning to overlap each other. How should one define boundaries and differentiate between these three roles?
Saw this amazing posts from the folks at Monzo along this line - https://medium.com/@mikldd/caught-in-the-middle-life-as-an-analyst-in-2022-abe18d57e74b
The data and analytics domain has moved to hyper specialisation. Where we isolate a subset of the skills required to deliver a Information Product into dedicated roles.
This is more of a lean approach compared to a t-skills approach we look for when delivering in iterations (aka Scrum). When we adopt a Lean approach we should focus on reducing the friction in the hand offs between these roles, how can we streamline the process.
The first thing I would suggest a team does is to map out the t-skills of each role and then create a definition of done for each role. These can be compared and any overlaps will be obvious.
Then we should work to remove the overlaps, by agreeing what role will do the work and what role wont.