What is People Analytics, and how does it benefit HR?

What makes a great manager? How can you improve employee engagement? Is your talent acquisition up to par? There’s one thing these questions all have in common: you can answer them all by digging into your people analytics.

What is people analytics? It refers to analyzing your organization’s employee data to make smarter, data-driven decisions to improve your operations and reach your business goals.

With the sheer number of workforce data sources now available (engagement surveys, exit interviews, attendance sheets, performance reviews, etc.) — it’s possible for HR teams to use people analytics tools to uncover weaknesses, identify areas for improvement, and make better decisions overall.

People analytics is so important to HR departments because it helps form essential HR KPIs (key performance indicators). If you don’t dig through your people data, you’ll have no reliable way of measuring your organizational performance. By leveraging people analytics, you will uncover actionable insights related to all departments, not just HR.

Thanks to artificial intelligence and machine learning, HR data analytics software is more powerful than ever, with enhanced analytics capabilities due to AI’s ability to draw insights from data sets too large for humans to comprehend.

Yet, there’s quite a bit to know about people analytics, such as which HR metrics matter most to your organization. In this article, I’ll cover what people analytics is, its benefits, and how you can start using them in your organization, so stay tuned.

Understanding people analytics

People analytics deals with human-centric data points, such as employee performance, retention, demographics, and your employee experience. While most HR professionals use the terms people analytics and HR analytics interchangeably, there is a difference.

HR analytics refers to data sets exclusive to the human resources department, while people analytics covers data points from all departments (sales, finance, marketing, etc.). That’s why people analytics is so effective for improving your decision-making to achieve your most desired business outcomes – because it provides the ‘big picture’ of how your organization is performing as a whole.

Analyzing your people data will help you:

• Identify skills gaps.

• Gauge the experience for new hires (which will help improve retention).

• Inform development programs to take your employees’ skills and capabilities to the next level.

• Discover areas where you’re underperforming.

• Gauge the engagement levels of your employees.

• Determine the next steps you need to take to achieve your business goals.

People analytics plays a crucial role in performance management, talent management, and workforce planning. They will also help you improve your work environment, especially if your employees are dealing with any harassment or discrimination.

Employee engagement surveys are always anonymous, granting staff the chance to air any grievances without fear of losing their jobs.

What does a people analytics strategy involve?

There’s a systematic approach you should take to your people data analysis; otherwise, you may miss out on valuable insights. In particular, most HR leaders employ a 4-step process for people analytics, which is as follows:

1. Data collection

2. Data cleansing

3. Data analysis

4. Data visualization

Each step is equally important, so let’s take a closer look at each one.

Step #1: Collecting data

The first step is to begin compiling your people data from various different sources, including:

• HRIS (Human Resources Information Systems) software. If your organization uses an HRIS, HCM (Human Capital Management), or HRMS (Human Resource Management System) platform, it will provide you with a ton of useful people data. Most HR software platforms contain employee’s personal information, performance reviews, employee retention rates, employee turnover, and other crucial data sets on their dashboards.

• Employee surveys. You should hunt down all your engagement surveys, exit interviews, manager 1:1s (if documented), and other forms of employee feedback.

• Workforce demographics. Don’t forget to include essential demographic information about your employees.

• Employee pay and benefits information. You’ll also need data related to payroll and benefits, so include things like pay stubs, employee benefit signup forms, and job offers in your data collection.

Once you’ve compiled all the data, you can move on to the next step.

Step #2: Cleansing the data

Next, it’s highly likely that the data you’ve collected isn’t 100% accurate. We’re all human, and it’s normal for some mistakes and inaccuracies to pop up. That’s what the next step of the process is for, which is to check the data for errors. Not only that, but you’ll need to organize the data into a format that’s easy to analyze and verify that no data is missing.

Missing data can lead to ill-informed decisions that negatively impact your organization, so you’ll want to leave no stone unturned when compiling your people data. This step of the process is where people analytics software tools come in handy, as they can automate the data cleansing phase.

The best people analytics tools will organize, validate, and clean the data until it’s ready for you to analyze. These tools can also identify missing data, standardize formats (if everything is different), and consolidate data from multiple sources (like from multiple departments and software platforms).

Step #3: Analyzing the data

This step is where the fun begins, as now you’ll actually begin to analyze the data. The goal here is to identify recurring patterns, trends, and behaviors in the data that you can use to improve your decision-making.

During data analysis, you’ll answer pressing questions like: Which factors are contributing to employee turnover? How engaged is my workforce? Are there any skills that other high-performing teams have that we don’t? Which training programs are most effective for new hires?

As you can imagine, answering these questions with people analytics is extremely valuable for organizations, and it makes future forecasting easier and more accurate.

Step #4: Visualizing the data

We’re humans, not AI bots, so it’s difficult for us to make heads or tails out of giant walls of text. Instead, we use tools to visualize data like graphs, charts, line plots, histograms, and treemaps. This makes it far easier to identify patterns and trends, so you’ll need a way to visualize your people data.

Most HRIS and HCM systems double as analytics platforms, as they serve as a central location for all your workforce data. They also feature visualization tools like graphs and charts that you can generate with the push of a button.

The 4 types of people analytics

There isn’t just one way to approach workforce analytics. In fact, there are 4 distinct types of people analytics — each with unique ways to use data in your decision-making process.

Here are the 4 types of people analytics you can use at your organization.

Type #1: Descriptive analytics

The first and most common type is called descriptive analytics, which refers to analyzing patterns in historical data to gain insight into past events. It’s the most basic form of people analytics, and it involves looking at historical data like turnover rates, engagement levels, demographics, and employee performance reviews. HRIS systems are an excellent source for descriptive analytics since they contain all your people management data in one location.

An HRIS will contain all your employee records, including:

• Pay stubs

• Performance reviews

• Disciplinary actions

• Attendance records

• Employee demographic information

• Benefits administration paperwork

• New hire paperwork

• Job offers

• Engagement surveys and other forms of employee feedback

Armed with this type of data, you can answer questions such as, “How many employees voluntarily quit last year? How diverse are our demographics? What was the average salary for a sales manager?”

As you can see, descriptive analytics is all about gaining insights from the past. As such, they aren’t used for predicting the future. Descriptive analytics are more about laying a foundation for other types of people analytics that aim to predict and prevent future issues.

Type #2: Diagnostic analytics

Next up is diagnostic analytics, which aims to identify the root cause of past issues to prevent them from happening again. Diagnostic analytics go a step beyond describing past events as they attempt to ‘diagnose’ what went wrong.

For instance, your descriptive analytics may uncover that you had unnaturally high turnover rates in the previous year. A diagnostic approach involves following up on that and attempting to determine why your turnover spiked.

By digging through data sets like employee feedback surveys, performance reviews, exit interviews, and new hire rates – you can determine whether your high turnover had to do with poor onboarding, a lack of development opportunities, or something else entirely. Just as it’s beneficial to diagnose an illness before it becomes too severe, the same is true for issues affecting your workforce.

If you don’t use diagnostic analytics to discover why your employees are quitting at rapid rates, the problem will continue to intensify until it either becomes A) too big to ignore or B) cripples your entire organization.

Therefore, you should always employ diagnostic analytics for any problems you find with your descriptive analytics.

Type #3: Predictive Analytics

The best future decisions are backed up by data, which is where predictive analytics enters the picture. Just like diagnostic and descriptive analytics, predictive analytics is all about identifying trends and patterns in your historical workforce data. The difference is that predictive analytics assume that these trends will continue to make future decisions.

In other words, you extrapolate the patterns and trends that consistently arise in your historical data into the future. Predictive analytics involve complex statistical algorithms, so artificial intelligence and machine learning programs come in handy.

For the most part, extrapolated workforce trends hold true — unless a truly disruptive and unexpected event occurs. Predictive analytics are great for identifying potential skill gaps, runaway turnover rates, and other concerns regarding employee trends. Some models predict which employees are most likely to leave your organization within the next year based on a wide set of parameters.

Type #4: Prescriptive analytics

We’ve already described and diagnosed the issue, so now it’s time for a prescription. That’s the idea behind prescriptive analytics, which involves using your historical workforce data to find tangible solutions to problems.

This type of analysis involves combining data, algorithms, AI, and machine learning to determine the best actions an HR department can take to prevent issues from happening in the first place. It’s crucial to use all 4 types of people analytics in tandem with one another. For example, your diagnostic analytics build off your descriptive analytics. You first identify the problem and then determine what caused it.

From there, you attempt to predict how this problem will continue to occur in the future with predictive analytics. Lastly, your analytics team puts everything together to prescribe an ultimate solution to the issue.

What are the benefits of people analytics?

Now that you know how people analytics solutions work, let’s take a look at the top benefits your organization will enjoy by implementing them.

Boosted employee and organizational performance

Using people analytics is truly how you get your organization to fire on all cylinders. By combining the 4 types of analytics, you will stand a much better chance of meeting your business initiatives.

At the same time, you’ll identify areas of concern, diagnose the underlying cause, and then prevent them from happening again in the future. In fact, evidence shows that using people analytics can nearly double employee performance, which isn’t something you should ignore.

If your organization has been falling behind performance-wise, adopting people data analysis will help you diagnose and resolve the issue.

People analytics benefits all departments

HR is the one department that affects all other departments. After all, every department, be it sales, finance, or customer service — has employees with needs. If your sales department is experiencing a serious lack of engagement, don’t expect for your team to win any awards this year.

Yet, you can turn things around by turning to people analytics to determine why your sales force is disengaged. It could be that your company isn’t offering competitive salaries, or there could be a lack of challenge or incentives for your sales team to give it their all.

Whatever the cause may be, diving into your people data is how you’ll solve the engagement problem.

Reduced employee turnover and a better new hire experience

The strength of your recruiting and onboarding processes will make or break your organization. If you aren’t paying attention to your new hire experience, you may gain a bad reputation as an employer, which is not what you want.

It’s also integral for business leaders to pay attention to their turnover rates, as they can easily get out of control if you aren’t keeping watch. With people analytics by your side, you’ll strengthen your new hire experience and reduce turnover rates.

People analytics enable evidence-based HR

Data science is exactly that: a science. Accordingly, practices backed up by people analytics are evidence-based HR, which will always outperform HR policies implemented by hunches or guesses. Organizations that engage in people analytics will see better performances, excellent new hire retention rates, and positive effects on their bottom line.

Final thoughts: What is people analytics?

HR departments backed up by data are powerful forces capable of solving organization-wide issues. Whether your finance department is underperforming or you can’t seem to retain new hires, there’s nary a workplace issue that people analytics can’t solve – as long as you approach the process correctly.

Don’t forget to use all four types of people analytics to ensure you’re making the best decisions and future predictions.