How HR Leaders Use Workforce Data and Analytics for Better Business Decisions
Part of a series | Insights in Action Series
HR leaders can use workforce data to make better business decisions by focusing on four steps: uncovering the real stories inside survey data, adding context that reveals what the numbers actually mean, applying AI to surface patterns humans miss and starting small to build reliable processes before scaling. The key is treating workforce analytics not as a reporting exercise, but as a tool for understanding the people behind the data and acting on what you find.
Organizations using ADP's workforce analytics tools have reported measurable business benefits, including a 27-day average decrease in time-to-hire, a 60% reduction in voluntary turnover costs and overtime cost reductions of more than 55%, demonstrating how data-driven HR decisions can directly improve business performance.
Key takeaways
Engagement and culture surveys are the starting point for workforce analytics, but who is included in the survey — and who is excluded — shapes the quality of insights.
Standard pay equity survey methodology can systematically overlook nonbinary employees, creating blind spots in workforce data.
Context changes what data means: men and women may interpret "pay equity" differently based on caregiving responsibilities and flexible work expectations.
AI can uncover patterns that people might miss in large data sets, but its insights should be reviewed critically, just as organizations would evaluate recommendations from employees or stakeholders.
Starting small with workforce analytics, even imperfectly, builds the data collection habits and process reliability needed before scaling enterprise-wide.
HR data analytics offers the potential for substantive and sustained business growth — if the organization's leaders can turn analysis into action. A panel of experts at the 2023 Insights in Action virtual summit discussed the key steps of cutting through the noise of big data to create meaningful change. Here's what they had to say.
How do organizations turn HR data analytics into actionable insights? It's all about the details.
In the 2023 ADP Insights in Action session "How to Use Data to Ignite Growth and Progress," three industry experts — Darren Root, chief strategist at Rightworks; Devin Engelsen, head of total rewards and people analytics at Databricks; and Giselle Mota, chief of product inclusion at ADP — explored how businesses can cut through the noise of big data to creating meaningful change in their organizations.
Hosted by Aileen Gemma Smith, head of business strategy for diversity marketing at Amazon Web Services, the panel tackled four key components of successful data insight: understanding the story, gathering context, using technology and starting small. Here's a look at each in more detail.
How survey data reveals workforce narratives
In getting the discussion going, Smith notes getting the right data starts with asking the right questions.
"Something I find to be helpful," Engelsen says, "is implementing surveys, such as culture or engagement surveys, just to have a general pulse on the organization." By understanding the diversity of experience across teams and staff members, businesses can begin to uncover commonly occurring narratives, such as issues with pay equity or performance expectations. This type of insight matters because ADP Research Institute has found that for every 1% drop in full employee engagement, the likelihood of voluntary attrition rises by 45%, making survey data an important early indicator of retention risk.
Mota points out, though, that these stories are just the beginning. "It's important to know that data tells a story," she says, "but who's telling the story? Who are the actors in the story?"
For an illustrative example, Mota points to gender pay equity surveys among nonbinary staff. Because they may not identify as either male or female, they may not be asked to answer pay equity questions. If they are, the average number of nonbinary employees (typically reported at about one percent of staff), means their results may be considered statistically insignificant. As a result, organizations may skip over large parts of the HR story if they don't look deeper. More broadly, ADP research has found that workforce sentiment can vary dramatically across employee groups and demographics, reinforcing the importance of ensuring smaller populations are represented rather than overlooked in analysis.
Empower your HR team with actionable insights
Why context changes what workforce data means
Next comes context. With a solid grip on who's telling the story and what their perspective looks like, Mota says employers need to dig deeper into survey questions, find out how they're being interpreted and consider who's answering the question 100 percent.
For example, when men and women were asked about pay equity, both said they felt better included at work when they were paid fairly. They were also asked if gender played a role in workplace inclusion, and both men and women said no. For Mota, this raises an interesting question: "How is there a correlation between pay equity and not gender when pay equity has a lot to do with gender?"
The answer is context. For women who both work and are caregivers in the home, pay equity may include flexible hours in addition to salary. In contrast, men may view the question as purely about money. Gathering context helps deepen data value. This idea aligns with broader ADP Research findings that different employee groups often experience work differently. For example, ADP research shows only 18% of workers age 55 and older strongly agreed they have the skills needed to advance their careers, compared with 29% of workers ages 18 to 26, showing how demographic context can shape survey responses and workforce perceptions.
How AI can expand — and complicate — HR data analysis
When it comes to the story and the context of HR data, technology such as artificial intelligence (AI) can play a role in the analytics.
"I think there's a lot of opportunity for AI to help us do better and have a different perspective than our own," says Engelsen, "because I think we don't always provide all the context." AI tools can access massive data sets that can help teams discover emerging trends or previously overlooked correlations.
Of course, AI solutions also come with potential drawbacks. One of the biggest is bias: What if AI comes back with the wrong answer because of how it has been trained or because of the data supplied?
Engelsen has a simple reply: "Have you ever talked to a person? Everybody has their own bias. There are many different reference points, and we trust people so much because we have a shared understanding of the human experience to a certain degree, but there are also differences in our own human experiences, too."
In other words, AI shouldn't be discounted simply because it may carry bias. Instead, organizations need to take AI insights the same way as those from staff — with a grain of salt.
Why starting small accelerates workforce analytics adoption
Detailed data insights can help fuel organizational change, but enterprises are often unsure where to start and what meaningful change looks like.
The solution is to start small. Even if efforts aren't perfect, they can kickstart the development of better data collection and processes that can lead to better outcomes.
As one example, Mota points out how voice-activated AI tools were originally designed as a means for better disability inclusion. However, it became clear that these tools could offer advantages for all staff. Now, they've made their way into mainstream product design, making certain work tasks more efficient for everyone.
Root highlights how just a little bit of responsibility can get the ball rolling.
"It just has to become top of mind for somebody in the organization," he says. "Somebody in the organization needs to feel empowered to think more broadly about the business." That person may think of a solution with a specific group or task in mind, and it can end up developing into a universal application.
Keeping people at the center of HR data analytics
Human data helps set the stage for increased diversity, enhanced inclusion and improved business growth. But data itself isn't enough. Instead, businesses need to focus on the combination of people and processes to deliver ideal outcomes.
By understanding the story and gathering context, organizations are better prepared to apply AI tools and discover new insights. And it doesn't all have to happen at once — starting small helps build data and process reliability before scaling up models to tackle enterprise-wide insights.
Frequently asked questions
What is the first step in turning HR data into actionable business insights?
Start by asking the right questions through engagement or culture surveys to understand the workforce experience across teams. Then examine who is represented in the data and who may be excluded — such as nonbinary employees in pay equity surveys — before drawing conclusions. This ensures your workforce analytics reflect the full story, not just the most visible parts of it.
How can AI help with HR data analytics without introducing bias?
AI tools can access massive data sets to surface emerging trends and overlooked correlations that human analysts might miss. However, AI carries its own biases based on training data. Organizations should treat AI-generated insights the same way they treat human input — as one perspective that requires validation and context before informing decisions.
Do you need a large analytics team to start using workforce data effectively?
No. Experts recommend starting small — even imperfect initial efforts can kickstart better data collection processes that lead to improved outcomes over time. What matters most is empowering someone in the organization to think broadly about how workforce data connects to business decisions, then scaling from there.
