Most of us have "peaks and valleys" in our knowledge. In any given topic, we know a lot about some parts, but not as much about others and in some cases, there are significant gaps. For instance, if you work with Relational Database Management Systems, you may know a lot about the SQL language, but not as much about High Availability for your platform, or the different types of backups, or perhaps how memory is optimized for a given query.
Gaps in knowledge *can* be dangerous, however. There is "first-level" ignorance - we know that we don't know something. We don't, for instance, run across a room if the lights are off, because we know that we don't know what is in the room. But "second-order" ignorance - we don't know what we don't know - is where things can be problematic. In this case we don't have the information to know that something might be dangerous, or even that it exists. This should never be the case for a Data Professional in the area of Data Literacy.
All Data Professionals should have a firm understanding of the basics, and even the advanced concepts in Data Literacy. Knowing how to find and verify authoritative data, manipulate that data properly, and apply it to a solution is more important than understanding the tools to do that.