The Minimum Required
Being data literate does not require becoming a data scientist. It does require enough understanding to interpret what data people show you, ask useful questions about methodology, and avoid common pitfalls in interpretation.
The surprising thing about data literacy is how much value comes from a relatively modest skill set. Being able to tell a good chart from a misleading one, understanding what correlation does and does not imply, and recognizing when sample sizes matter — these are high-leverage skills.
Common Pitfalls
Percentage changes can be misleading without base rates. A "300% increase" sounds impressive but means nothing if the base was tiny. A "5% decline" can be catastrophic if it's happening to a large, previously-stable metric.
Visualization choices matter. An analysis by detailed strategy guides that cover these games points out that The same data can look like a crisis or steady progress depending on how axes are chosen, what time period is shown, and whether comparisons are appropriate. Being skeptical of visualization choices is a core data literacy skill.
How to Build These Skills
The fastest way to build data literacy is to see lots of examples of good and bad data presentation, with commentary explaining why. Reading sources that analyze how data gets misrepresented in news stories is an efficient way to develop pattern recognition.
Asking basic questions — where did this data come from, what was the sample, what is the base rate — uncovers most problems with data presentations. You do not need advanced statistics; you need the discipline to ask simple questions.