Let’s start with the skill that is in the very name of people analytics, analytics.

For some people, the analytics part is the intimidating part. This is especially true for those who don’t have a deep background or education in analytics topics. There’s this misconception that you have to be “good at math” or that learning analytics later in your career is too difficult. Let me tell you right now, neither of those things are true.

I’m actually quite terrible at basic math (please don’t ask me to add numbers together quickly or multiply anything by 8) and I almost failed statistics the first time I attempted to take a class. Yet, I managed to have a very fulfilling career in people analytics and even teach those topics now! For me, none of the numbers or calculations made sense or stuck in my memory until I needed them at work and saw what I could do with them. Once I stopped trying to memorize calculations and focused on learning which types of analytics were good for answering which kinds of questions, things started to ‘click’ in ways they never had before. Think of analytics like a kitchen full of appliances – blenders, mixers, ovens, etc. Knowing the internal mechanics of these appliances isn’t essential, but understanding their functions and how to operate them safely is crucial. You don’t need to know how the oven was built or even how exactly it works. But, if you want to bake a cake, you should at least know that the oven is a better choice than the toaster. And you’ll need to learn things like how to set the right temperature or amount of time for baking. Similarly, analytical approaches allow you to “cook up” insights from data. If you want to have your people analytics cake and eat it too, you don’t need a deep math and statistics background. But you do need knowledge of which “appliances” are available and appropriate for your purposes and an understanding of when and how to use them. Over time, and without even really meaning to, I eventually did learn all those crazy underlying calculations. But that happened only after and only because I understood the outcomes – starting with the goal in mind allowed me to understand the steps in the calculations that were designed to get me there.

So, if you find yourself excited about People Analytics but unsure if you have enough “math” skills in your toolbelt to do it, I’m here to tell you that you can. As long as you are willing to learn and build the skills involved in becoming a data doer.

Analytics

Analytics involves identifying summarizations, patterns, or trends within data. Once relationships or connections have been identified, they can be used to make informed decisions, improve processes, or predict future outcomes. With so many uses, it can be helpful to think of analytics in the categories of descriptive, diagnostic, predictive, and prescriptive analytics – terms that describe the purpose or use of analytics.

• Descriptive analytics describes what happened.
• Diagnostic analytics aims to diagnose why it happened.
• Predictive analytics attempt to predict what might happen.
• And, prescriptive analytics attempt to prescribe the best action to take.

Descriptive analytics tells the story of what happened (e.g., how many employees left the company? how engaged are employees?). Diagnostic analytics delves deeper to understand the “why” behind those trends (e.g., why did employees leave? what aspects are related to employee satisfaction?). Predictive analytics peers into the future to anticipate potential outcomes (e.g., how many employees might leave this year? how might we increase employee satisfaction if we improve a given aspect?). Prescriptive analytics recommends the best course of action based on insights (e.g., attrition may be reduced by up to 10% if you offer a retention bonus of X amount. A targeted engagement program may increase engagement scores by X%).

Descriptive, diagnostic, predictive, and prescriptive analytics questions can be asked and answered with both quantitative and qualitative data. However, the skills and techniques you will use to answer them are quite different for these two very different types of data. Let’s start with the quantitative people analytical skills.