Hi @Alida great question. Sorry for the slow reply - I’m catching up 
There are a lot of different parts to this question and they’re things I think about a lot.
First of all, having really standard data definitely makes it easier for data workers. And 9 times out of 10, my opinion is going to be that data workers should work harder rather than trying to collect data which makes being a data worker easier. However, this is not, of course, always true in all settings.
Second, the question of whether demographic data should be standardized across data sets is a really tricky one when thinking about it from a data equity lens. Standardizing demographic data, particularly things like ethnic and racial background, sexual orientation, and gender can be extremely important in building a population-level evidence base about people who aren’t getting what they need in terms of health care, education, access to opportunity, etc. It also makes it much easier and sometimes more reliable to track changes (hopefully progress) over time.
However, of course, standardized demographic data is always going to prioritize certain groups and not be a good fit for lots of people. And if all our data is standardized, these folks are never going to see evidence that reflects their lived experience.
There are pros and cons. The relative weight of the pros and cons is going to depend on the purpose of the data collection exercise, what other data is available, and who we are trying to develop meaning for in the project.
We talk about this often in our Talking Data Equity sessions because it’s such a big deal.
Both Marieka and Naomi shared their viewpoints on this recently. If you have other folks you’d like to see as guests on Talking Data Equity, let me know!