Turning Work Data into Better Performance Decisions

As HR leaders seek more objective ways to evaluate performance, workforce data is emerging as a powerful tool. Sam Naficy, CEO of Prodoscore, an employee data intelligence platform, shares how insights drawn from employees’ daily work patterns can reveal hidden influencers, improve collaboration, and flag early signs of burnout, while also helping organizations maximize technology investments.

Naficy met with SHRM at the Unleash America conference in Las Vegas in March.

SHRM: Many organizations are moving toward more data-driven performance management. How can insights from employees’ daily work patterns help leaders better understand productivity, collaboration, and engagement across their teams?

Naficy: At a high level, our thesis has been: let’s make management data-driven rather than managing by gut or feeling. Decision-making should be metricized, data-driven, and objective. Any performance metric can be backed up by analytics and data to support a decision. Using data to analyze employee engagement, productivity, retention, and collaboration across enterprises is the best way for management to operate their business. Payroll is the number one item on a P&L. It’s the most expensive item for any company.

Are you getting the most out of it? That’s the ultimate question for a CHRO: What outcome am I getting from my people? Prodoscore answers that question by surfacing workforce intelligence from the tools employees already use every day, so leaders get a clear, continuous picture of team health without adding any friction to how people work.

Specialty Credential: People Analytics

SHRM: Why are “hidden influencers” so important, and how can companies better recognize and support them?

Naficy: Organizational network analysis (ONA) works well when you have all of the data associated with collaboration. E-mail, Slack, chat, calendar. By analyzing those communication patterns, we can map how information actually flows through an organization, rather than how an org chart says it should. That is a fundamental difference.

What ONA surfaces is that the most influential people in an organization are often invisible on a traditional org chart. They are the ones bridging teams, disseminating information, and connecting colleagues to expertise. ONA data gives leaders a visual, data-driven way to identify those contributors so they can be recognized, supported, and used as a model for building a stronger, more connected workforce. Similarly, when employees and teams are siloed, showing little internal collaboration, it impacts culture, morale, overall engagement, and retention.

SHRM: Some organizations are using behavioral data to flag potential burnout or disengagement. How can managers use these insights to coach employees proactively without creating concerns about monitoring or trust?

Naficy: We are very employee centric. We are capturing data from business tools only, nothing personal. It’s based on the business email address. If you’re watching YouTube in the background, we don’t capture it. This is not meant to be punitive. Employees get their own dashboard. They get to compare themselves to their peers. Good employees love it. Bad employees, they might not like it.

We can predict attrition based on disengagement. Again, it’s not punitive. An employee may be having some issues that can be addressed by their manager. HR can offer support. But underlying it all is a data-based assessment, not a gut feeling about someone’s performance.

Historically, performance management has been very subjective. We can do a 360 review, completely objectively, with data. We can find outcomes, and leading and lagging indicators, which leads to a much fairer performance review. Transparency is central to how Prodoscore is designed: when employees have visibility into their own data alongside their peers, the conversation shifts from surveillance to self-improvement, and managers are equipped to coach with context rather than conjecture.    

SHRM: Many organizations invest heavily in workplace technology, yet adoption often lags behind expectations. What do these gaps between tech investment and employee usage reveal about how companies evaluate, implement, and support new workplace tools?

Naficy: Tool adoption is one of the top three metrics we provide, just behind employee engagement and productivity. You’re spending $1000-1,500 per person on average on tools. How are they being used? Most companies have very little visibility into how these tools are being used. We can provide real-time analytics on total versus utilized licenses, associated costs, high and low adopters, etc. IT and finance leaders, for example, need to know where there is money sitting on the table. Maybe it’s a training gap and maybe there are different tools better suited to certain roles. Adoption data enables leaders to optimize the tech stack, reduce app sprawl and unnecessary spend, and ensure a privacy-first approach, so organizations are not exposing data within AI tools where it shouldn’t be. And speaking of AI, what AI tools are working? Who has access? Who needs access? The proliferation of tools and the subsequent need to visualize adoption are top of mind for every leader today.

Beyond cost savings, these analytics often reveal something more strategic: when you understand which tools are actually being adopted and by whom, you gain insight into where change management efforts need reinforcement and where your technology investments are genuinely driving value.