How to Structure Your People Analytics Function: A Practical Guide for HR
In many organizations today, the analytics function isn’t just a nice-to-have accessory — it’s becoming a core strategic capability. But having analytics tools and dashboards is only half the journey. The other half is how you structure your people analytics team so it actually delivers value. That’s where operating models come in.
An operating model describes how the people analytics function is organized: its roles, reporting lines, capabilities, governance, and ways of working. Without a clear operating model, you run the risk of duplication, inconsistent insights, tangled responsibilities — and analytics efforts that don’t make the impact you hoped for.
Four Operating Models HR Can Choose From
Here are four models to consider — each with trade-offs, strengths, and caveats.
1. Centralized Expertise Hub
In this model, a small dedicated analytics team sits inside HR or perhaps as part of a broader analytics or insight function. They service the rest of the organisation as their “clients.” They design dashboards, analyse data, advise business partners on interpretation.
Pros: Standardised methods, coherent governance, good efficiency.
Cons: Less embedded in business units; might lack local context.
2. Hub & Spoke Model
Here you still have a central analytics hub (governance, tools, standards) but each significant business unit has its own smaller analytics resource (the “spoke”). The spoke teams understand that unit’s focus and partner with the hub for shared infrastructure.
Pros: Balances standardisation with bespoke service; quicker business-unit alignment.
Cons: Risk of conflicting priorities between hub and spokes; shared resource tensions.
3. Front-Back Model
In this design, you split your analytics effort into “front office” (client-facing, embedded analytics partners) and “back office” (platforms, data management, infrastructure). The front works with business units; the back ensures the right data, tools, and governance.
Pros: Good clarity of roles; modern design for larger scale.
Cons: Requires strong coordination; back-office constraints may frustrate front allies.
4. Federated Model
This model leans most on decentralisation: multiple independent analytics teams live inside each business unit or region. There’s a small coordinating central team, but autonomy is high.
Pros: High local alignment, speed, flexibility.
Cons: Risk of duplication, inconsistent standards, cost inefficiency.
Choosing the Right Model for Your Organisation
When deciding which model fits your organisation best, ask yourself:
What is the scope of your people analytics mission? Are you solving a few well-defined problems, or are you building a broad enterprise-wide analytics capability?
How embedded in the business do your analytics teams need to be? If you need deep understanding of a specific division, you might need spokes or federated teams.
What’s your current maturity in analytics? If analytics is just getting started, a centralised hub may make sense; if you’re mature, federated might be fine.
What resources do you have? Teams, budget, tools – more decentralisation means more investment.
How quickly do you need insights delivered to the business? Closer alignment often means faster insight, but may cost more.
Why This Matters for HR
As HR professionals, the structure of your analytics function affects how quickly and reliably you translate people-data into decisions. A well-architected operating model means:
Insights get to business leaders in a form that matters.
Data governance and ethical use of people data are built in — not an after-thought.
You avoid “OK, we have dashboards” but no action.
You build a culture where analytics becomes enabler, not silo.
Final Thoughts,
Building the right operating model for your people analytics function is not optional. It’s what defines whether your analytics investment becomes a trusted strategic advantage or just another stack of reports. If you choose the structure thoughtfully, align it with your business strategy and data maturity, and keep it human-centered, you’ll find analytics becoming one of HR's strongest levers in driving better decisions and outcomes.