5 Traits of a Good Data Analyst

The purpose of data analysis is to reveal objective answers about an issue. However, effective data analysis requires the person doing the job must be curious.

Broadly, the traits of a good data analyst are being both analytical and abstract. The person must understand the question, answer, and means to get to the answer. An effective analyst is one who understands how best to extract imperfect data using tactics make the information as objective as possible.

Here are five qualities to look for when trying to find a data analyst for your business.

A good data analyst must understand their work does not reveal perfect answers in any circumstances. For example, if they are tasked with determining the response rate of a marketing promotion that offered a skip-a-payment on an auto loan, the analyst must figure not all customers who skipped a payment that month were responders to the campaign. Some may have forgotten to mail a payment or may have started to default on the loan. The analyst understands how to adjust the analysis to reveal the best data.

A good data analyst understands the nuances of the data they're working with. For example, it's important to understand the difference between null values and blank fields and how to extract data accordingly. In addition, some fields may be formatted differently than intended. For example, a numerical field might be formatted as a text value. The analyst understands how this differentiation is important for doing calculations.

The analyst also needs to understand how the key identifier of a database affects the breakdown. This is important, for example, for a direct response mailing or email blast. If the unique identifier is the social security number, the analyst will know to eliminate duplicate household numbers or email addresses so two pieces are not received at the same home.

There are always unknown or unforeseeable variables that affect the results of data. For example, when answering the question of whether a new manufacturing company has polluted the water supply, a simple time-series analysis might indicate just that. However, there are other variables that might have affected pollution during the same period. One of the traits of a good data analyst is to have an understanding of other variables that could affect the data and account for those variables.

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.