HR dashboards aren’t enough — effective use of data means an insights-first approach to talent decisions, says Anna Filice
It may seem for many organizations that they have so much data at their fingertips, they’re stuck in a cycle of collecting and reporting it, without putting it to effective use in their organizational and talent decisions.
When Anna Filice looks at how HR uses data, she doesn’t start by celebrating dashboards. She starts by focusing on the issue at hand first and what data can be used to address it — and keeping people at the heart of the decision-making process.
“If we want to be insights-driven, we have to have a good partnership with the business and clearly identify the problem that we're trying to solve upfront,” says the Chief People Officer at Ontario’s Workplace Safety and Insurance Board (WSIB).
“And often that means challenging some existing assumptions, so from our perspective, and from the business's perspective, there needs to be an openness to understanding what that people-related data is really telling us and how that impacts the result that we're trying to achieve.”
Decisions with data rather than by data
Filice has seen HR swing from ignoring data to drowning in it.
“Historically, I think HR has produced a bunch of dashboards and reports, and we put a lot of information out there,” she says. “And then our experience has been that people will interpret that in ways that benefit supporting a decision that's already been made, interpreting something that’s defined, something as simple as headcount, and then it just gets filed away. And we're really not leveraging that.”
In such circumstances, data is used to dress up decisions after the fact and HR isn’t driving strategy – it’s decorating it, she says.
That’s why Filice rejects the easy label of being purely data-driven. “I think we're more insights-driven [at WSIB] — we use data for making informed decisions,” she explains. For her, insight comes from a more deliberate process: slowing down requests, challenging assumptions, and forcing line leaders to own what they’re really asking for.
Giving data some context
For Filice, that starts the moment a manager comes to HR asking for a report.
“Often the business has approached HR and they ask for data — they'll say, ‘Can you give me a turnover or an attrition report for this part of the business?’” she says. “And a few years back, we would just do that — but now we have a conversation about context: why are you asking for that information and what’s the problem you're trying to solve?”
“From there, we actually provide recommendations for actions right out of our analytics team based on the insights,” Filice explains, adding that reports come with plans for action, not just numbers, and the business is expected to commit to it.
To sustain that model, Filice has had to rebuild HR’s capabilities. Relying on shared analytics support from operations or IT keeps people data stuck at the margins, so she believes that properly resourcing an HR analytics team is important. “It's a skill set that hasn't historically existed in the HR department," she says, adding that at WSIB, that has meant hiring specialists with data backgrounds into HR and putting them in charge of sensitive datasets, governance and dashboards.
Dissecting the HR lifecycle to find successes and challenges
The purpose of this focus on properly using data is operational, as WSIB brings people into frontline customer service and case manager roles that take months of training before they hit full productivity and too many leave just as they reach that point, says Filice.
“We have a lot of internal churn at WSIB, and a customer service or a case manager role directly impacts our key performance indicators,” she says. “The challenge we have is there's a lot of ramp-up time to get these people fully productive, and then often they go on to a new position.”
Her team can now dissect each step of that lifecycle through data to see where the organization is doing well and where there are challenges, she says.
Filice applies the same hard lens to DEI and compensation, where many employers may still rely on basic compliance checks. “It's important to mature that conversation beyond the low-hanging fruit,” she says. “Everyone should be on a journey at this point to look at their data to understand if they have inequities in their compensation related to gender, race, or any other identifiers.”
For Filice, spotting a pay gap is only the beginning, and the test is whether HR can track the root causes and redesign the systems that created it.
Analytics-powered insight
Even as she pushes deeper into analytics, Filice is blunt about the limits of data itself, especially as AI starts to drive more HR tools.
“There's a parallel discussion around the use of AI, how much human intervention is required, what’s ethical, and whether things can become biased,” she says. “I still believe that data is often incomplete and people are inherently biased, especially when factors like resistance to change are at play, for example.” That’s why she positions data as a decision support tool — never a decision maker — and insists that human judgment, context, and ethics stay at the centre of every major people decision.
Some HR leaders may still see analytics and AI literacy as nice-to-have skills that can be outsourced, but Filice doesn’t buy that.
“I think it's important for HR leaders to embrace this, to build structures to support data analytics, because if you're living in a traditional HR structure, you might not have people with the skill sets to actually engage in this work in a way that’s meaningful,” she says. She believes that the organizations that actually shift their talent decisions effectively with analytics are the ones whose HR teams insist that the data, the insight, and the accountability line up before anyone makes a move.
