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.
Understanding the story
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.
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.
Gathering context
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.
Using technology
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.
Starting small
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 the ‘human’ in 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.
Ready to dig into the details of HR data analysis? Check out the full Insights in Action session on-demand or explore other topics from the event, including “When Privacy, Data and Ethics Collide” and “Generative AI and the Workplace of Tomorrow.”
This article originally appeared on SPARK powered by ADP.
