A well-designed maturity model helps organizations identify gaps in skills, tools, and processes, optimize HR efficiency, and align more closely with business goals. Looking ahead, it enables companies to anticipate trends such as AI and data integration, transforming HR Analytics into a strategic driver that enhances both employee experience and overall performance.
What is a Maturity Model?
Broadly speaking, a maturity model is a framework that evaluates how advanced a function, process, or capability is within an organization. In the context of HR Analytics, it assesses how mature a company is in using data to support strategic HR decisions by mapping progress across various stages.
Key Challenges in Building and Managing a Maturity Model
Numerous challenges impact the implementation of HR Analytics maturity models. Among the most common:
- Data integration: disparate sources (e.g., Payroll, Learning) require interoperability.
- Skills: continuous training in tools (e.g., Excel, Power BI) and analytical thinking.
- Organizational culture: resistance to change and the need to engage all levels.
- Tool stabilization: dashboards and processes must move from pilot phase to systemic adoption.
- Process integration: initial efforts often focus on fragmented analyses before achieving full HR Analytics integration into broader business processes.
As organizations progress through each maturity stage, these challenges evolve. We refer to them as “transition challenges.”
A Simplified Maturity Model
| Theme | Basic Level | Intermediate Level | Advanced Level |
| Culture & Skills | Intro training in Excel and HR Analytics basics; limited or no analytical mindset | Advanced training (Power Pivot, insight communication); dedicated but unevenly skilled team | Analytical capabilities widely distributed across HR; continuous training in AI/ML and data storytelling |
| Processes & Organization | Initial ad hoc meetings; experimental team | Regular meetings (e.g., 6 per year); consolidated team with defined roles; improved insight interaction | Fully integrated processes with other functions (e.g., Finance); automation and data-driven decision-making |
| Data & Tools | Disconnected sources (e.g., ERP, unstructured Excel); no integration | Partial integrations (e.g., Payroll, Business); more stable tools (e.g., normalized TA file) | Unified platform with real-time data; predictive analytics and full interoperability |
| Analysis | Descriptive reports (e.g., headcount, turnover); isolated dashboards (e.g., HRD, D&I) | Diagnostic analyses (e.g., turnover causes); customized dashboards for HRBPs with business data | Predictive and prescriptive analytics (e.g., attrition risk); optimization models (e.g., L&D budget) |
| Dashboards & Visualizations | Static dashboards, unstable data (e.g., TA 2022); unimplemented mock-ups | Interactive dashboards (e.g., stabilized TA); insights accessible to HRBPs | Self-service access with drill-down and automated alerts; integration with decision systems (e.g., QLIK BI) |
Clarifying the Last Three Themes:
- Data & Tools = Infrastructure: How data is collected and integrated
- Analysis = Processing: How data is turned into insights (descriptive → predictive)
- Dashboard = Communication: How results are made accessible and understandable
Case Study: A Luxury Sector Client Journey
We supported a client in the luxury industry through multiple stages of maturity across all five themes. Starting from a basic level, the organization progressed to an advanced maturity level.
Project Structure
The project spanned the entirety of 2024 and tackled specific transition challenges:
- Ownership: Senior leaders gradually assumed ownership of HR Analytics as a core decision-making and business-alignment tool.
- Dashboard Development: As ownership increased, so did demand for more advanced, functional dashboards.
- Automation: For stabilized dashboard components, we introduced Business Intelligence tools.
- Skills Development: Junior profiles focused on strengthening their mindset and skills around HR Analytics.
We established several workstreams focused on sharing analysis results, transitioning from Excel to more advanced analytics and visualization tools (BI), training, and developing vertical analyses. A specially selected team of motivated professionals explored the use of predictive and prescriptive algorithms in HR.
Key Activities by Theme
| Theme | Project Activities |
| Culture & Skills | Empower senior profiles to fully own the HRA approach; strengthen juniors’ mindset and skills for HRA work |
| Processes & Organization | Support HRA meetings via a structured calendar; implement area-level routines to deepen data usage and decision-making |
| Data & Tools | Integrate Tagetik (linked to Continuous Improvement); SuccessFactors, Payroll; less-structured data from TA and Learning; business/financial data integration |
| Analysis | Apply predictive and prescriptive algorithms |
| Dashboards & Visualizations | HRD dashboard using BI; operational HR dashboards developed by perimeter (Operations, Staff, Global Distribution) |
Key Takeaways
This project revealed several key lessons:
- Teams and individuals differ widely in learning pace and readiness. A pragmatic approach must accept that some areas move faster than others. The goal is to bring slower teams to a baseline while enabling high performers to explore more sophisticated, high-value topics.
- Supporting the planning and execution of meetings to discuss data findings is essential. These routines must become part of the organization’s daily life.
- After extensive work on data preparation and analysis, presenting the results effectively is critical. Storytelling and contextualizing insights within the business are fundamental. Equally important is ensuring that everyone involved in the analytics stream receives feedback on their contribution.
As a result of the project, the client organization became largely autonomous in handling most analytics topics, methodologies, and presentations. Our ongoing role is now focused on high-complexity issues and the deep integration of data and analytics across other business areas.
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