In our daily lives, we use personal biases, intuitions, and gut feelings to make our decisions. And that’s perfectly fine. They serve us well in many ways.

However, when it comes to improving work performances, personal biases, intuitions, and gut feelings just don’t cut it.

Data can improve your own, your team’s, and your organization’s performance; people analytics can help. People analytics is the data that identifies workforce patterns and trends. Here are some questions that can be answered with people analytics:

• How engaged are our employees?
• What skills does my organization need to invest in, to achieve our mission?
• Why are my employees leaving the organization?

These questions and many more are the kinds of questions that people analytics can answer. Even if you don’t regularly use data in your job, you can still learn a lot with people analytics, regardless of your supervisory level.

A brief primer on people analytics

Before we answer why employees are leaving your organization, let’s start by defining a few terms:

Data are facts, statistics, or other items of information. Data are all around us; you just have to know how to look for it, compile it, and make sense of it. We can use data to understand problems and processes at a micro level (between individuals), at a mezzo-level (team-level), or at a macro level (organizational level).

So who uses data?

One group of people who use data are data analysts. Data analysts organize, examine, analyze and use data to draw meaning. They tend to focus on understanding previous events to describe things that have already happened. 

Then what’s big data?

Big data refers to enormous volumes of data. We’re talking millions of cases. Big data require a special skillset to effectively manage, organize, examine, and analyze.

Data scientists are the people with those skills. Data scientists use machine learning and algorithms to work with and draw meaning from big data. While they can use their data to describe things that have already happened, like data analysts, they have additional skills that allow them to use their data to make predictions. These predictions tend to center on how likely something is to happen and estimate the consequences of its occurrence, using data about events that have already happened.

The data analysts and data scientists that work in people analytics use their skills to understand and improve the workforce. People analytics is the application of data science and data analytics to understanding human resources and human capital processes in and across organizations. You may have heard people analytics referred to as talent analytics, HR analytics, or workforce analytics.

Even if data analytics, data science, or people analytics aren’t skills you currently have in your toolkit, you can still benefit from people analytics to better understand your workforce.

I’m just not a data person

Right now though, you may not see your relationship to data. You might even be thinking, “Listen, I’m just not a data person.”

And to that, I’d ask you a few questions. Do you:

• Track your steps?
• Check your ‘likes’ on Instagram, Facebook, or LinkedIn?
• Post on social media at particular times of day, with the hopes of reaching a wider audience?

If you answered yes to just one of these questions, you’re a daily consumer and user of data, even if you don’t actively work with it yourself. So you are a data person and can use it to identify why people are leaving your organization.

Steps to using data

Using data to improve your organization’s performance is the essence of people analytics. Let’s pretend you work for a tech company that builds educational phone apps targeted toward getting girls interested in STEM. In the last two years, the company grew from three people to 75 people! However, you’ve noticed that even though you’ve grown, you tend to be losing some of the best sales people and programmers. They’re getting replaced, but why are you losing the good ones?

Using data to answer questions

That is a people analytics question. Now let’s talk about how we use data to answer these questions.  As I mentioned, we have our intuitions and gut feelings, but these amount to anecdotes. While they help get us interested in topics and can help us to start formulating a more scientific response, they are not subject to the rigorous treatment data require. As a result, we cannot trust the validity of anecdotes over the validity of sound data.

Define your questions

The way that you can harness data to improve your workforce is through a scientific approach. The first step is to define your question as best as you can. Be as precise as possible here; you’ll need to refine your thought to a point where the question is answerable. For example, you might start with a question of “Why are people leaving the organization?” but eventually wind up with, “What percentage of the workforce plans to stay with the company in two years?” It’s not the only question you could ask, but it is a start.

Qualitative or quantitative data?

From here, you need to figure out the type of data you need to answer your question: qualitative or quantitative? Qualitative data is data concerned with descriptions, which can be observed but cannot be computed. On the contrary, quantitative data focuses on numbers and mathematical calculations. It’s important to note that one type of data is not better than another; determining which data to use depends on the question you ask. Answering “Why are people leaving this organization?” will use qualitative data. Determining “What percentage of the workforce plans to stay with the company in two years?” will likely use quantitative data or both.

Collecting data

Once you determine the type of data you need, it’s time to collect your data. Qualitative data can be captured with interviews, surveys, focus groups and workshops, whereas quantitative data is often captured through recorded workforce data.

Cleaning data

Then, before we can analyze it, we have to ‘clean’ the data, which is just fancy-talk for making-the-data-work-for-us. You might need to “recode variables” or “create an index” using the data.

Analyze data

Once the data are clean and ready to go, we analyze them using the appropriate techniques. The technique will vary based on the type of data you have, the type of question you have, and your desired end state.

Putting data to work

The next steps is to use those data for something, and put it in action at the micro-, mezzo-, and macro-level. For example, you can answer questions about attrition by reviewing individuals’ performance appraisals. At the mezzo level, you could also look at how teams compare, or perhaps other sites. We can also take a macro-approach and look at organizational performance over time and see which areas have improved and which areas can use further improvement? Any level can help you identify why employees are leaving your organization.

As you can see, while the idea of people analytics can feel overwhelming, breaking the process into steps will help you make data-based decisions about your workforce. And using a systematic approach and yielding the benefit of people analytics will guide you on your journey. Want to learn more? Contact us now.