The HR Data Operating Model: Centralized, Embedded, or Hybrid?

How to structure People Analytics for scale, impact, and alignment with the business.

Introduction: Why the Way You Organize People Data Work Matters

It’s no longer enough to “do analytics.” Organizations today expect People teams to deliver insights that are fast, accurate, and actionable. But many HR teams struggle—not due to lack of tools or effort, but because of confusion in ownership, inconsistent data flows, and unclear delivery models.

That’s where the HR data operating model becomes essential. It’s the structure behind how data is captured, processed, distributed, and used. It defines:

• Who owns what data and processes?
• How are analytics requests prioritized and delivered?
• How do insights translate into decisions?

And it’s not just about team structure. It also includes your systems architecture—how your ATS (Applicant Tracking System), HRIS (Human Resources Information System), payroll platform, talent management tools, and engagement surveys are connected and governed.

In short: structure is the foundation for strategy. Without the right operating model, you can’t scale analytics, ensure quality, or meet stakeholder needs.

The Three Core Models: Definitions, Characteristics, and Examples

There are three foundational approaches to organizing your People Analytics function. Each comes with tradeoffs. Most organizations evolve from one to another—or blend elements of all three.

1. Centralized

In this model, one team owns all People Analytics activities across the company. That includes reporting, dashboards, data governance, definitions, and data delivery. This team typically reports to People Ops, People Strategy, or directly to the CHRO.

Characteristics:

• Strong governance and metric consistency
• High data quality and platform control
• Single point of intake for all analytics requests
• Risk of bottlenecks and slower response times

Systems implication:
A centralized model typically pushes for consolidated systems—a single HRIS that integrates with payroll, an enterprise-wide ATS, one engagement platform. The People Analytics team decides which tools are used and how data flows between them.

Example:
A multinational company with 10,000+ employees uses Workday as its single source of truth. The People Analytics team governs metrics like attrition, compa-ratio, and internal mobility. Business units request dashboards via a formal intake form. Insights are reviewed in executive People reviews once a quarter.

2. Embedded

Here, analytics capabilities are placed within business functions or HR sub-teams. Analysts are aligned with regions, HRBPs, or COEs like Talent, DEI, or Total Rewards. They work closely with leaders and tailor insights to local needs.

Characteristics:

• High contextual relevance and business alignment
• Responsive to local issues and leaders
• Risk of inconsistency and fragmentation
• Governance is harder to enforce

Systems implication:
In an embedded setup, different regions or COEs may use different tools, especially if the business has grown through acquisitions or operates semi-independently. A shared ATS might exist, but payroll or performance systems vary.

Example:
A retail business with decentralized HR teams assigns one analyst per region. The Europe-based analyst works in local language, pulling from a separate payroll system and local HR platform. While this allows fast, local insights, metric definitions vary across regions.

3. Hybrid

This model combines centralized governance with embedded delivery. A central team defines standards, manages core platforms, and builds scalable tools. Embedded analysts or data-savvy HRBPs apply those tools in context.

Characteristics:

• Balance of speed and consistency
• Shared tools, but tailored insights
• Requires strong collaboration and operating rhythm
• Enables scale without losing flexibility

Systems implication:
A hybrid model often includes core platforms managed centrally (e.g., one HRIS, one ATS, centralized dashboards), but allows local systems or plug-ins for regional needs. The central team provides templates and pipelines that local teams adapt.

Example:
A fast-growing tech firm uses a hybrid approach: the central People Analytics team owns dashboards in Tableau and maintains data from Greenhouse, Workday, and CultureAmp. Regional HRBPs use these tools to run quarterly business reviews, but add local commentary and action planning.

What About Systems? Who Decides?

Operating models are tightly connected to your systems strategy.

A common challenge is when companies have multiple tools that don’t talk to each other—one ATS, a different HRIS, several payroll systems, and a standalone performance tool. This can work in the short term, but long term, it creates confusion, duplication, and reporting errors.

Your operating model should help define:

• Who owns the tech stack decision-making?
• Are systems managed centrally or locally?
• Do we pursue best-of-breed tools or a consolidated suite?
• Is there a systems roadmap tied to the analytics roadmap?

If you want to scale People Analytics, you need a systems strategy that reflects your data maturity, regional needs, and operating model goals. One doesn’t come before the other—they evolve together.

Common Pitfalls to Avoid

1. Analytics operating in isolation
   When analytics sits too far from the business, it becomes a reporting team, not a decision partner. Even with great dashboards, insights don’t translate into change.
2. Multiple definitions of the same metric
   This is one of the most common and damaging issues. HRBP A defines “attrition” differently than COE B. Leadership compares apples to oranges. Trust in data erodes.
3. Chasing every request equally
   Without prioritization, analytics teams burn out responding to ad-hoc requests that don’t move the business forward. Not all asks are equal. There must be a triage process.
4. Over-customizing reports for each stakeholder
   Tailoring is important—but doing it for everyone creates unsustainable workloads. A better approach is to build modular, scalable templates with room for local input.
5. Neglecting enablement
   Launching dashboards without training leads to underuse. Self-service works only if users understand data context, know how to filter, and feel confident drawing conclusions.
6. Assuming systems alone will solve the problem
   Buying a new HRIS won’t fix your operating model. Tools are only as good as the process behind them. Implementation without role clarity results in chaos.

Proposal: Build a Model That Evolves With You

There’s no one-size-fits-all. But what matters is that you choose intentionally and revisit the decision as your needs change.

Start with a maturity-based roadmap:

Recommendations:

• Map your current data flows and team responsibilities
• Identify bottlenecks in request intake or dashboard usage
• Define ownership for systems and key metrics
• Co-create your model with both central and embedded stakeholders
• Reassess yearly based on feedback, business growth, and analytics usage

Enablers for Any Model

To make your operating model work, you need more than structure. You need support mechanisms that hold the system together.

1. Data Governance Framework
Define what each metric means, who owns it, how it’s calculated, and where it lives. This avoids the chaos of “multiple truths.”

2. Request Management Process
Don’t let Slack messages or emails define priorities. Build a lightweight intake form and triage logic—what gets done, when, and by whom.

3. Shared Tools and Templates
Avoid reinventing the wheel. Central teams should offer standard templates (dashboards, analysis decks, data dictionaries) that embedded teams can adapt.

4. Business Partnership Model
Assign analytics roles to specific regions or functions. Create routines for check-ins, planning, and post-insight debriefs. Analytics is not just delivery—it’s partnership.

5. Capability Building
Train HR and business leaders not just to consume dashboards, but to ask better questions, understand patterns, and act on insights.

Call to Action: Make the Operating Model a Leadership Priority

Too often, the operating model gets built in the background—by necessity, accident, or historical habit.

It’s time to make it a topic at the leadership table.

Here’s how to start:

• Facilitate a working session with your People LT on “how we work with data”
• Map the current structure and the pain points
• Share models and options—not as “best practices” but as fit-for-purpose choices
• Invite feedback from regional HRBPs, COE leads, and analysts
• Frame the model not as a tech or reporting issue, but as a lever for faster, smarter business decisions

The model you choose (and refine) will impact how fast you can scale, how effectively you support the business, and how much trust stakeholders place in your insights.

Final Thought: Structure Is Strategy

The operating model is not a background decision—it is the infrastructure of your People strategy. It shapes how quickly you spot problems, how well you respond to them, and whether your leaders are equipped to make good decisions.

You can’t expect consistent, high-impact analytics from a disorganized, unclear system. Just like finance or product teams need clear operating models, so does People Analytics.

Treat this like an investment. Design it with intent. Update it with feedback. Align it with your systems. And most of all—make sure it reflects your values and goals as a People function.

In the end, the way you work with data says a lot about the way you lead with people.