Data Must be Understood to Intelligently Use Evidence Based Thinking

All metrics are wrong, but some are useful

Metrics might tell you something about the world in a quantified way, but for the how and why we need models and theories … metrics are generated must be open and transparent to make gaming of the system more difficult, and to expose the biases that are inherent in humanly created data

True, understanding the proxy nature of data (and how well or questionably the proxy fits) is important.

Data can't lie but we often make it easy for others to mislead us when we don't understand (or question) what the data really means (what operational definitions were used in the collection, etc.).

Related: Operational Definitions and Data Collection - Actionable Metrics