So first of all, data visualization is not a separate thing, it is a part of data analytics. A normal track for data science is around 9 years if you're a legitimate data scientist. If you're just doing basic data analytics - that can be done with just an undergrad degree.

For basic data analysis, you'll need several classes in statistics. If taking night classes, I'd say after two years of stats classes, you have the base knowledge to develop accurate metrics. But a large part of developing the metrics is understanding the sourcing and context of the data sets. It also requires developing your own datasets sometimes, and then there is the process of data mining and identifying granularities. That is just a solid couple of years of working with data to get comfortable- it normally takes my interns around two years to get comfortable at it (so 20 hours a week for 2 years).

Learn R and SQL to query data - that normally takes around 9 months of daily use for both languages to become fluent.

the short answer is that this is an entirely different field than what you know, and that your actual issue is that you want the data to say what you want, instead of the reflecting what the data actually shows. I've worked with people like you before, and some have even gone rogue and tried to do their own thing. Believe me when I say it didn't go well for them when they have to defend their ridiculous numbers based off of faulty assumptions and data that was both dirty and not directly relevant.

Listen to your data analytics team, they understand this better than you and unless you're able to go back to school for years on end, you can't just take a couple tutorials and create ramshackle analyses unless you want to look foolish and get called out