People Analytics data 101: How to Turn Workforce Insights Into Business Impact
People analytics is the practice of collecting and transforming HR data to improve the way you do best. Here’s how to do it in four steps.
Today, organizations have more workforce data than ever. The challenge is no longer collecting the data, but interpreting it for effective business decisions. This is the People Impact Gap between between workforce data and optimized decision making
We teamed up with Deloitte to study the People Impact Gap and found that seven in 10 execs believe that better access to their workforce insights would help them make decisions fasterMore than half (56%) agreed it would improve the predictability of workforce performance.
The opportunity isn’t simply to collect more data, but rather to make the data you already have more accessible and connected so it’s useful for decision-making. That’s how you uncover the root cause(s) of challenges, identify opportunities for improvement, and make more confident decisions about the future.
In this guide, we'll explore how organizations can use people analytics data to turn workforce insights into measurable business results.
What is people analytics?
People analytics is the practice of applying statistical analysis and data modeling to workforce data to understand what's driving outcomes across your organization and inform decisions about your people.
It combines data from across the employee lifecycle to support better business and human resource decisions—specifically, in the following areas:
Talent acquisition
Retention and turnover
Employee performance and productivity
Diversity, equity, and inclusion (DEI)
What is people analytics data?
People analytics data is the connected layer of information that sits on top of your raw HR records. It’s what you get when you pull info from across your systems and structure them into a connected layer that a people analytics platform can analyze to answer workforce questions.
People analytics data is different from the operational data sitting in your HRIS
Your HRIS holds the records:
Who was hired
What their title is
What they’re paid
When they took leave
And so on.
That data exists to run HR processes like payroll and benefits admin, but it’s not built to tell you, for instance, why turnover is climbing in one location or whether your highest performers are also your highest flight risks.
For insights like those, the data needs to be pulled out from the source platform, connected with other information (ATS, performance, engagement, financials), and structured in a way that tells a cohesive story.
Think of it like following a recipe…
Your headcount, turnover, performance, comp, and engagement data are the raw ingredients. Those get mixed together, processed, and transformed through the analytics solution.
The outcome is the sum of these parts, and it’s a bigger story. It’s the collective imagery of thousands of data points that come together to provide you with some information to work with.
What does this look like in practice?
The Reece Group is a plumbing and HVAC distributor based in Melbourne, but they operate across 800+ branches in Australia, New Zealand, and the United States. Their workforce data was scattered across multiple systems, which made it impossible to optimize performance across their global, distributed workforce.
Just like the combined ingredients in a recipe, Visier People connected that data into one layer. And when they finally looked at operational data (same-day delivery schedules) together with people data (individual availability and absence patterns), they were able to predict absences two weeks ahead. From that, managers could arrange cover before a disruption hit.
What data is used in people analytics?
The above is one example, but people analytics draws on data from across the entire employee lifecycle. Here's what typically goes into the mix:
Recruitment data: Time-to-hire, along with the source and quality of hires and conversion rates at each stage of the pipeline shows you whether you're hiring effectively, and where your best candidates come from.
Performance and productivity metrics: Performance ratings, goal completion, and output levels by role or team help you understand how performance connects to other workforce trends like tenure and team composition.
Retention and turnover data: Voluntary and involuntary exits, tenure, and turnover figures by department, location, and manager tend to be where the biggest cost savings live since they point directly to where and why people are leaving.
Compensation and benefits data: Employees’ pay by role, level, location, and demographic, plus benefits utilization are critical for pay equity analysis and budget planning (plus understanding whether comp is driving attrition).
Learning and development data: Training completions, skills acquired, learning hours, and how those factors correlate with performance and promotions help you tie L&D investment to actual outcomes.
Employee engagement and survey data: Sentiment, satisfaction scores, eNPS, and open-ended feedback from employees give you the "why" behind the numbers from the other categories. They’re also generally the earliest warning signs of a turnover problem.
The myth of data cleanliness and people analytics
There's a persistent myth that you need pristine data before you can start with people analytics. That's backwards. In reality the process works the other way around. Getting your people analytics data into a platform is what makes it clean.
O Onboarding gives you the opportunity to look at your data at a holistic level. It helps you spot the misses,the mistakes, and the areas requiring cleanup.
In other words, the analytics process itself becomes your data audit. Issues that have been sitting invisible in spreadsheets and disconnected systems for years become impossible to ignore once you're comparing and combining them.
What does "good enough to start" look like?
As HR leaders like Khun Teerawat, Talent Management Director for APAC, PepsiCo remind us, good enough data used now beats perfect data that's never used.You don't need every field populated, nor do you need every record perfectly standardized.
Start with your core HRIS data and the fields you actually have; the platform will help you identify and prioritize what to fix next, rather than you trying to fix everything up front with no idea what actually matters.
First West Credit Union is a good example of this in practice. Their team didn't start with clean data, but once they connected it in Visier People, the platform surfaced coding inconsistencies across their branches, which they then used to standardize their processes going forward.
The cleanup followed the insight, not the other way around.
3 Common data quality problems you'll likely uncover
The main issues we see among companies that are new to people analytics data are:
Inconsistent job codes: When the same role is titled differently across business units or regions, it’s impossible to compare like-for-like.
Missing termination reasons: Exits logged without a reason and with vague catch-all categories gut your ability to understand why people are leaving.
Duplicate employee IDs: The same person represented multiple times across systems (e.g., after a transfer, rehire, or system migration) throws off headcount and tenure calculations.
None of these need to be solved before day one. They're exactly the kind of issues that the analytics process is designed to surface. And once they're visible, they're fixable.
4 steps to use people analytics data to meet your business goals
Once you set the foundation for people analytics at your organization, the next question is: How can you use it to drive business value? People Analytics Consultant, Marie Mineur walked through how First West Credit Union accomplished this in four steps:
Step 1: Understand where you are
Before you can make any progress on business initiatives, you have to understand the current state of your organization as it relates to the outcome you want to achieve. For First West, this was initially a D&I goal.
“If your goal is to reach 50% diversity then I would suggest starting by calculating the female ratio. Divide the count of female employees by the headcount. Then the headcount is the total number of employees that you have or the total number of active employee IDs,” Marie said.
Step 2: Visualize the trend
Once you know the ratio, you need to figure out how the numbers are trending. In this case, you would identify the areas where you have the lowest female ratio and work to understand what’s driving the numbers down.
Marie found that there were two departments that were under 40% diversity, very far from the company goal of 50%. One way to start finding a solution to this problem is to start with the department with the lowest diversity rate. In this case, it was sales.
Step 3: Drill down into problem areas
Next, Marie used Visier to see the female ratio by location and department. Then she compared the female headcount to male headcount over time. She explained that when facing an issue like this, there are a couple of possible causes. Either enough women aren’t being hired, or they are leaving the company and being replaced by men. She found that both of these things were contributing to the problem.
Step 4: Find a solution
At this stage, you’ve found the root of the problem and can do a bit more digging. For this example, that meant figuring out if enough women were applying and why they were leaving. Looking at the recruitment process, this raises several questions. Are you reaching enough female applicants? Is there bias in the recruitment process?
When it came to people exiting the company, Marie found that women were leaving for reasons related to compensation, personal challenges, and lack of career growth. “Now compensation, you might wonder, is there a big equity issue here? When you’re talking about personal challenges, can you come up with different work arrangements? Then the lack of career growth, you might question if you have equal opportunities,” she said.
This is the depth of insight you can uncover with basic data and a people analytics solution.
Transform your workforce with Visier People
You have the data, but now what? You need a system that connects the dots, and that’s what Visier People is. It brings in data from across your HR and business systems and automates the analytics so your team isn't building and maintaining dashboards by hand.
From there, it surfaces what matters through Vee, its built-in AI conversational analytics partner. Business leaders, even those outside of HR, can ask a workforce questions in plainEnglish to uncover insights that lead to act
If you're ready to see what your own people analytics data could tell you, request a demo of Visier People.
