The core idea
There is no single universally accepted people analytics maturity model. Our capability map distinguishes operational reporting, descriptive, diagnostic, predictive and prescriptive work, with impact evaluation throughout. Reporting establishes dependable totals; description identifies patterns; diagnosis investigates explanations; prediction estimates future outcomes; prescription compares possible actions. Evaluation asks what changed because of an intervention. These capabilities overlap. A well-designed comparison of two onboarding approaches can be more useful than an elaborate individual flight-risk score.
Source and attribution [1]Using it in practice
Start with a recurring decision and assess whether the current data can support it. Check definitions, completeness, ownership and how the result reaches a decision-maker. Improve one weak link before buying more complex technology. Build evaluation into the plan from the beginning: a team should not wait until it reaches a supposed final maturity stage to ask whether its work helped. Include qualitative evidence and employees’ explanations of their experience.
An example, not a reported case
Worked example · illustrative
A fictional employer initially reports monthly leavers. It then separates voluntary departures from contract endings and compares eligible new-starter cohorts after 90 days. Interviews suggest that unpredictable shift availability deserves investigation. The team pilots clearer scheduling communication and compares subsequent retention with a suitable reference group. It has moved towards a better decision without needing to predict which named employee will leave.
What to watch for
Do not present this six-part teaching route as an industry-certified scale or attribute it to a single author. More data, dashboards or machine learning do not automatically mean greater capability. An organisation may be strong in one analytical area and weak in another. Governance, trust, data quality and the ability to act matter at every stage.
Reporting and description
Create a metric dictionary before debating a trend. Define who counts as an employee, how casual-worker inactivity is treated and which date records a departure. A dashboard should show the relevant population and period. Reconcile totals with source systems and explain corrections rather than quietly changing historical values.
Diagnosis and prediction
A diagnostic question seeks plausible reasons and evidence that distinguishes them. A predictive question asks how well an outcome can be anticipated for new cases. The most predictive variable may not be a useful intervention target. Keep those purposes separate when discussing what an analysis can support.
Prescription and impact
A recommendation depends on costs, feasibility, fairness and evidence about what an action changes. Prediction alone does not establish that offering a particular intervention will help the predicted group. Plan a proportionate evaluation and define an outcome that matters beyond whether staff opened a dashboard.
A practical development plan
Choose one business question, one data-quality improvement, one skill to develop and one decision owner. Set a date to review what changed. Record unresolved limitations openly. This creates an achievable capability plan without making advanced modelling a status symbol.
Take it into your next conversation
Three useful questions.
- Which decision will this analysis change?
- Are the population, denominator, time period and assumptions explicit?
- What alternative explanation or unintended effect must we check?
Related terms
Go to the evidence
Sources & attribution
The core idea is an original summary of the cited work. Application notes, examples and sketches are our interpretations, not quotations or reproductions of the authors’ figures. Publisher records may require access to read the full original work.
Published 2026-09-20 · Reviewed 2026-09-20. Editorial approach