Most computer-savvy employees can figure out how to run an SQL query and retrieve an answer. In fact, these queries are the most risky, as they reveal answers that can be misleading or incorrect. Effective data analysis requires the analyst first perform exploratory investigations. For example, if a company wants you to determine if pollution levels affect mortality rates, it's important to understand the time lag between the cause (pollution) and effect (mortality rate). If the time lag is the variable, the analyst should understand how this might affect a time-series regression analysis. Further, they must recognize when a best-fit model and when a time-series regression analysis are needed.
Perhaps the most important trait of a good data analyst is curiosity. While knowledge and technical abilities are important, it is especially important to be curious about how things work and why. From our earlier example, curious analysts would ask who would most likely respond to a skip-a-payment promotion and why. The analyst would understand which "responders" to the promotion were not responders, but early defaulters and advise decision makers accordingly. A curious data analyst is most valuable because they seek to understand the world, not just develop reports, meaning they seek to reveal truth, not just answers.
Effective data analysis can sometimes be more art than science. When determining if an analyst has what it takes to be a good fit for the role, it's important to determine if they are curious enough to dig for the answers you need. That is, curiosity is important, but a good data analyst must also have the drive and work ethic to find those answers.