Workforce analytics isn’t just about people. It’s about the work that delivers value

At Disney characters like Mickey and Minnie Mouse are sacred. Every movement is choreographed, every action tightly governed. Costumes are designed so not even a hand gesture can go rogue. These roles are critical to brand reputation, so they’re optimised beyond fault.

But that’s not where the biggest performance gains lie. Look instead at the street sweepers. The ones who clean the paths and engage with guests. The ones who, occasionally, burst into song. That moment of unexpected delight? That’s what shapes customer experience. And that’s where the real performance uplift comes from.

This example, shared at a recent event hosted by King’s College London to mark the launch of Workforce Analytics: A Global Perspective, illustrates an important shift in thinking. The story of workforce analytics today is not about people as data points but about understanding the work that drives value – and what that means for how HR leaders invest, analyse and lead.

Workforce analytics isn’t a rebrand of HR or people analytics. It’s a broader, more integrated approach that considers how entire workforces, not just individuals, drive business value. It goes beyond traditional HR metrics to an understanding human capital’s impact on organisational outcomes.

Rethinking performance: Beyond the top of the tree

Analytics in HR has often focused on the obvious performers. The rock stars. The senior leaders. The visible contributors. Mark A Huselid, distinguished professor of workforce analytics at the D'Amore-McKim School of Business, Northeastern University and one of the book’s co-authors, called this out: most analytics and interventions happen at the top. But returns often sit lower down, in roles that directly affect outcomes yet receive little strategic attention.

The question isn’t who the best people are but where their performance makes the biggest difference. A pilot at a logistics company such as DHL, for example, operates in a tightly controlled environment. There’s little room for variation and generally few errors. But drivers for that same organisation? That’s where performance variance can ripple across the entire business.

These insights challenge traditional investment logic. Instead of mapping talent to hierarchy we need to map it to impact. Identify the variance in the work that moves the needle and invest there. That requires looking at teams and not just individuals. At roles and tasks, not titles. 

The ACAI Model: Analytics with purpose

The book proposes a clear framework: ACAI – Ask, Collect, Analyse, Influence.

• Ask the right question: Start with the question. What work drives customer outcomes? What roles generate the most strategic value?
• Collect the right data: Gather relevant, not just available, data. This often requires new methods and partnerships across the business.
• Analyse the data in the right way: Explore patterns but only in the context of their importance. A correlation means little without an understanding of why it matters.
• Influence the right decisions: Use the insights to shape decisions. Guide investment. Adjust strategy. Build capability.

It’s a disciplined process, closer to the social scientific method than a tech dashboard. This model counters the common rush to data. The story from the academics was consistent: don’t mine the data until you know what you’re digging for. The process starts with questions rather than data accumulation.

Refocusing on work to understand performance

The most urgent shift for HR is to stop framing analytics around individual behaviour. Instead, focus on the work being done, the context in which it happens and the interfaces between humans and systems.

Dana Minbaeva, professor of strategic human capital at King’s Business School and another co-author, made this point explicitly. The future of performance isn’t about managing people better. It’s about managing interfaces. Where humans interact with technology. Where teams connect across functions. Where decision-making happens between people, tools and processes.

She also challenged the legacy view of analytics as a linear journey starting with fixing the data then moving up the ladder to the holy grail of predictive analytics. In reality, it’s iterative and messy. 

Organisational development and analytics: natural allies

Several speakers made the case that analytics alone can’t deliver impact. You also need organisational development (OD). Analytics can show what’s happening. OD helps you understand why and how to change it.

We’re operating in a wicked environment: complex, ambiguous and high-stakes. Predictable cause and effect has faded. In this context sense-making becomes more valuable than prediction. And OD provides the frameworks and processes to enable that.

The partnership between OD and analytics also helps overcome a common hurdle: getting air time with executives. As Tim Haynes, VP people and culture at Jazz Pharmaceutical, said building influence often happens outside the room. It means anticipating what questions a CHRO will face six months ahead and preparing evidence that matters. One big mistake is to think of analytics as dashboards. It’s decision support and thus a number of domains are relevant to workforce analytics, including strategic management, economics, industrial and organisational psychology and change management among others.

The ethics of insight

Martin Edwards, professor and deputy head of UQ Business School, The University of Queensland, addressed the ethical dimensions head-on. Every analytics decision involves ethical trade-offs such as privacy, fairness and transparency.

Examples included monitoring tools that track keystrokes or meeting patterns. Even when anonymised the question remains: do employees know what data is being used and how? And, more importantly, is tracking keystrokes where the value is or is that in the thinking time in between? Inference without transparency risks undermining trust.

Selective investment based on analytics also raises equity concerns. If performance data is biased or even missing, for example having been removed as part of the EU’s GDPR legislation’s right to be forgotten, then decisions based on it can be problematic and even deepen inequality. 

There is also concern around the growing use of off-the-shelf AI systems. Many of these solutions are black boxes – their algorithms can’t be interrogated or explained. That lack of visibility makes accountability difficult and raises significant questions for HR leaders who must justify how decisions are made. Some of the HR leaders in the room said this would make them walk away from a vendor. 

From insight to action

Analytics is a strategic capability but only when used with intention. At Shell, for example, a rigorous approach to analytics revealed a casual link between engagement and safety. With line managers key to engagement it therefore became clear how vital their role was in shaping both. 

So workforce analytics isn’t just about finding patterns. It’s about asking the right questions. Acting on what matters. And building the kind of organisational intelligence that can navigate uncertainty.

Workforce analytics helps organisations direct attention and resources where they yield the greatest return rather than where the organisation chart says they should go. This means focusing on roles where performance variance directly affects business outcomes, from frontline innovation to customer delivery. And that means asking less about who is performing and more about where performance matters most.

Ultimately, the value of workforce analytics is measured in business terms – improved outcomes for customers, smarter allocation of resources and more resilient organisations.

So workforce analytics, done right, isn’t about tracking people but about understanding the work that drives value and using that understanding to prioritise issues by strategic impact, aligning analytics efforts with organisational goals to generate the highest return on investment. It requires identifying where interventions will be most effective, maximising resource allocation and improving decision-making. This doesn’t mean removing the individual from the equation; rather, it calls for evidence-based models grounded in what we know about success at the individual, team and organisational levels. 

Above all, it demands early and consistent engagement with senior decision-makers to foster organisational learning and ensure insights are implemented in ways that truly shape outcomes.

Key lessons on workforce analytics

💡 Yes, workforce analytics is still about tracking people but it's far more than that. It’s how and why we track that has fundamentally shifted.

✅ What workforce analytics still involves:

• Analysing individual data – roles, behaviours, outcomes
• Measuring performance, capability and fit
• Using insight to inform decisions about hiring, development and deployment

🚀 What makes workforce analytics strategic:

• Shifts the unit of analysis from individuals to the system of work
• Prioritises performance variance that affects outcomes
• Focuses on interfaces between people, teams and tech
• Aligns workforce insight with strategic business objectives
• Supports smarter investment, higher ROI and more impactful decisions