Since you’re reading this, I assume you’re at least willing to consider my advice. So, I want to recommend that you focus first on your ability to apply people analytics rather than your ability to do people analytics.

Applying something doesn’t require having made or created something first. As an example, let’s say you decide to paint a room in your home. Your goal is to make it more pleasing and well-suited to the lifestyles of the people who live in it. To meet your objective (a nicely painted room), you do not need to learn the chemistry of creating paint, or the color science to pigment it. You don’t even need to learn how to paint (you can always hire a painter). But, you will need to understand what the room is used for (a child’s bedroom? a kitchen? the entryway?) and make decisions like choosing a color best suited to the purpose of the room. And, you may need to learn some paint-specific knowledge in order to make your decision (what does matte, semi-gloss, and satin even mean? And, who knew there were so many shades of yellow to choose from?!).

Afterwards, you invite others to your home and you hear, “Wow, I love how you painted the walls.” It doesn’t matter if you didn’t physically make the paint or apply it yourself. You met your objective of making the room pleasing and suited to purpose by connecting the needs, desires, and situation to the right products and services that would create that result.

That’s what I hope you will aim for in people analytics. If you want to learn to be a data analyst/scientist (a painter in our analogy), that’s awesome! Step 3 will give you a lot of great technical skills you can build to get there. But, I still encourage you to focus on ensuring you can apply people analytics first.

In our paint analogy, these are the skills that allow you to identify which rooms need a new coat, to assess the room’s characteristics and the needs of its occupants, and translate them into informed decisions regarding colors and paint finishes, and possibly oversee the process to achieve the desired outcome. In people analytics these are the skills that allow you to identify business priorities and opportunities, that help you translate business needs into people analytics questions, and enable you to leverage data to create results.

So, let’s get into some of the best non-technical skills for accelerating your people analytics career.

The Data Consumer, Data Translator, and Data-doer Approaches

I came across an article explaining how important it is to connect analytics across different areas of the business. It talked about the role of a ‘data translator’ as one of the most important roles in analytics. Building off of this, I propose that there are three styles in which you might be able to approach your own career in people analytics. That of a data consumer, a data-doer, and/or a data translator.

A data doer is my own made-up term for anyone whose job is specifically to gather, analyze, manage, and/or interpret data to respond to a specific question, problem, or deliverable. A data consumer is anyone who uses data results that they did not create themselves. You are already a data consumer today as you read articles, take in information, and use available insights to make decisions in your day-to-day life.Finally, a data translator is a person who serves as the link between those doing the analytics and those using them to make decisions; this is a person who knows enough about the ‘doing’ to understand and translate the work of doers to the consumers who may not well versed in the data or analysis being used. The translator is a critical link between information and decision-making.

For the moment, we will skip past the role of “data doer” – more on this in Step 3 – to focus on the two roles that arguably require deeper excellence in non-technical skills for success: the data consumer and data translator.