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From People Analytics maturity assessment to executive reporting design: a practical guide
People Analytics maturity assessment and executive reporting design are the two core tasks that connect HR data to business decision-making. This guide walks step by step through assessing your organization's current level of data usage and designing the metric framework and reporting format executives trust.
This guide is written for the following readers. HR practitioners introducing People Analytics for the first time, organizations that have the data but get stuck at every executive report, and HR leaders who want to assess their current analytical level and design the next step.
Executives are not dissatisfied with HR reporting because there is too little data. With 83% of companies worldwide acknowledging a shortfall in workforce analytics capability (source: Deloitte, Human Capital Trends 2024), the problem is not the volume of data but the ability to translate it into the language of the business.
People Analytics is not the exclusive domain of large data teams or specialist analysts. It is the entire process of starting from the data you already hold and deriving insight executives can use in decisions. This guide contains the criteria and tools to design that process stage by stage.
People Analytics is the set of activities that collect and analyze an organization's workforce data to provide evidence for HR decisions and business strategy. The goal is less "analyzing data" than "making better decisions with data."
According to McKinsey & Company's 2023 research, organizations using People Analytics extensively showed outcomes 2.3x better in talent performance and 1.8x better in financial performance than those that did not (source: McKinsey & Company, The State of Organizations 2023).
In Gartner's 2026 survey of 426 CHROs, "realizing HR-AI value" was selected as one of HR's top priorities. Executives now expect HR to move beyond operational reporting and contribute to business decisions (source: Gartner, Top Priorities for HR Leaders in 2026).
The reality differs. In the same Deloitte survey, 76% of all companies had HR analytics, yet only 6% had reached predictive-level maturity. The remaining 94% still sit at the level of reporting past aggregate data.
If even one of the patterns below applies, this guide will help directly. The reaction after each pattern is what executives most often send back.
Before starting People Analytics, you first have to know what level your organization is at. Setting goals that do not fit your level either stalls execution or makes you treat as difficult something you can already do. Use the four-stage maturity model defined by Bersin/Deloitte to place your organization.
Meeting 60% or more of the items in a stage's checklist puts you at that level. Work through from stage one in order, confirm your current stage, then use the next stage's checklist as a reference for setting your improvement direction.
| Check | Item |
|---|---|
| Headcount status : total, by department, and by level can be aggregated monthly | |
| Turnover aggregation : voluntary and involuntary turnover are calculated separately by month | |
| Recruiting status : time-to-fill is measured per posting | |
| Labor cost aggregation : labor cost by department is reported monthly and quarterly | |
| Leave and attendance : leave utilization and overtime by department are tracked |
| Check | Item |
|---|---|
| Trend analysis : metric trends over 6–12 months can be visualized as charts | |
| Cross-department comparison : the same metric can be compared across departments to spot outliers | |
| Executive dashboard : an HR metrics summary is reported to executives at least monthly | |
| Benchmark use : turnover, time-to-fill and similar are compared against industry averages | |
| eNPS measurement : employee net promoter score is measured quarterly or semi-annually |
| Check | Item |
|---|---|
| Cause analysis : the causes behind problem metrics such as turnover and absence can be explained with data | |
| Cross analysis : correlations between metrics (performance and turnover, training and performance) are analyzed | |
| Cohort analysis : data is segmented and compared by tenure, level, and department | |
| Impact measurement : metric changes before and after HR programs are reported with data | |
| Decision linkage : there are cases where analysis results led to actual changes in HR policy or process |
Stage 4 usually comes with dedicated analytical staff or external expert support. If you are running stage 3 stably, set the items below as medium-to-long-term goals.
| Check | Item |
|---|---|
| Attrition risk model : past turnover data, performance, and engagement are combined to produce an individual attrition risk score | |
| Workforce demand forecast : headcount needed 6–12 months out is forecast in line with business growth plans | |
| Scenario analysis : labor cost and productivity impact are modeled in advance across hiring, transition, and attrition scenarios | |
| Real-time dashboard : an executive dashboard with automatically updating HR metrics is in operation | |
| Preemptive intervention process : procedures such as retention conversations and compensation review are defined on the basis of forecast results |
Predictive analytics on low-quality data actively causes poor decisions. Introducing predictive models without meeting 60% or more of the stage 3 checklist is not recommended. Accurate data → reliable insight → prediction: keep that order.
Many HR practitioners make the same mistake in metric selection: they pick the metrics HR finds easy to measure. Metrics that persuade executives are chosen on different criteria. From HCG's observation in the field, HR metrics that win executive attention satisfy the three axes below.
The HR metrics executives care about connect to one of revenue, cost, productivity, or risk. "12% turnover" is an HR metric; "estimated opportunity cost of KRW X hundred million from turnover among core sales staff" is business language. The same data, connected differently.
In practice you can convert like this. Translate turnover into replacement hiring cost (0.5–2x annual salary, source: SHRM 2023) and productivity loss period (3–6 months on average). Express time-to-fill as the work gap the team absorbed while the seat was vacant. Convert training investment into human capital ROI (HCROI) via performance change rate, and use eNPS movement as a leading indicator of long-term turnover.
A metric has to be able to influence an actual executive decision. Choose metrics that serve as "grounds for judgment" rather than "status reporting." If executives respond with "and so?", that metric has no link to a decision. Such metrics are better classified for internal operational monitoring and excluded from executive reporting.
Metrics that show a time series earn more executive trust than one-off analysis. A metric updated consistently every quarter establishes itself as a decision tool more than a figure reported once and dropped. When settling on a metric, also review whether you can measure it the same way six months from now.
The table below separates, by maturity stage, the core metrics to prioritize for executive reporting from those for internal operations.
| Stage | Core metrics for executive reporting | Internal monitoring metrics | Data collection method |
|---|---|---|---|
| Stage 1 | Headcount / turnover / time-to-fill / labor cost | Absence rate by department / leave utilization | HRIS standard reports / spreadsheets |
| Stage 2 | Voluntary turnover / eNPS / cost per hire / labor cost ratio | Turnover by level / six-month attrition after joining | HRIS dashboard / quarterly survey |
| Stage 3 | Key-talent attrition / performance-reward linkage rate / internal mobility rate | Attrition predictors / training-performance correlation | Analytics tools + survey + performance data cross-referenced |
| Stage 4 | Attrition risk score / workforce demand forecast / HCROI | Labor cost simulation by scenario | Predictive models + BI tools + HRIS integration |
Once the maturity assessment and metric selection are done, set the execution order. People Analytics fails when you try to build everything at once. The roadmap below lays out a realistic sequence for climbing one stage at a time from where you are.
The first task is building a foundation of data you can trust. Accurate base data comes before impressive analysis. What matters at this stage is not perfect data but a structure that can measure consistently.
Using the stage 1 metric matrix from Section 3, first pick the metrics your organization can measure right now. Five is enough to begin with. Building few metrics and measuring them accurately matters more than building many.
Define in a document "who collects this, when, and from what source" for each metric. When owner and cadence are unclear, data goes missing or different people bring different numbers.
Build an aggregation format in your HRIS or a spreadsheet and actually run it for three months. The first three months are about gathering data and, at the same time, chasing down the cause whenever a figure looks off. That process itself raises data quality.
For example: "turnover rate = departures in the month ÷ total headcount at month start × 100." Collecting data without definitions produces different figures from different departments. Definition first, collection second. Turnover alone changes depending on whether it counts only voluntary departures or involuntary ones as well.
Once three months of data have accumulated, you are ready to start reporting to executives. At first, a monthly one- to two-page HR summary is enough. Do not delay reporting itself in an attempt to produce a perfect report.
Include charts (line, bar) instead of tables listing numbers alone. Executives want to see the trend. How things moved over three months carries more meaning than this month's figure.
For example: "Sales team turnover up 3%p versus last quarter : second-half hiring plan needs review." Do not leave executives to interpret figures themselves. HR presents the meaning in one sentence first.
Do not report all metrics as equals. Place the metrics executives mention or ask about frequently at the top of the report. Asking "what would be most useful to you?" before the first report works well.
Close the report with one or two items to watch next month. This section is what starts executives seeing HR reporting as a forward-looking tool rather than a simple settlement of accounts.
Once six months or more of data have accumulated, trends and patterns start to appear. From this point you can shift from "status reporting" to "providing insight." Insight is not "we see this pattern" but going as far as "here is what this pattern means and what we should do."
Cross-analyze the departments, levels, and joining cohorts with high turnover to find patterns. For example, a finding that "turnover concentrates at the associate level around the 18-month mark" connects to recruiting targets, onboarding design, and compensation review.
Review the correlation between eNPS movement and turnover on a six-month basis. If eNPS functions as a leading indicator of turnover, you can report it to executives as a metric that forecasts next quarter's turnover.
Analyze performance grade distribution together with turnover data to define regrettable attrition. Not all turnover is a problem. Set the criteria that separate the attrition the organization must prevent from the attrition it should simply manage.
Propose HR policy improvements based on the analysis, and track their effect with data. Being able to report six months later how turnover changed after introducing a policy makes HR's business contribution visible.
Having the data and persuading executives are different capabilities. HBR (Harvard Business Review) has identified "a mismatch between HR language and business language" as the core reason HR fails to win executive recognition. How you frame the same data determines the executive response.
Not "15% turnover" but "15% turnover : up 3%p year on year, 5%p above the industry average of 10%". Provide the context. HR sets the reference point first, so executives never have to ask "is that good or bad?"
Applying the per-departure replacement cost estimate discussed earlier (0.5–2x annual salary), you can express it as "five key talent departures = estimated replacement cost of KRW X hundred million + six months of lost productivity." Converted this way, executives feel the scale of the problem.
Status reporting alone leaves executives unclear on what HR is proposing. Structure it as "analysis identifies X as the primary driver of rising turnover → we propose improving Y in the second half." Problem, cause, and proposal have to run as one flow.
Connect HR data to the business agenda executives currently raise most often, such as growth, cost, risk, and M&A. Setting the report title in business language, such as "workforce readiness for second-half new business expansion," works well.
Summarize three to five key messages within roughly one page for executive reporting, and put detailed data in an appendix. If executives ask, answer with the appendix. The longer the report, the more the key message is buried.
Below is the recommended structure for the HR report submitted to executives once a month.
| Section | Content |
|---|---|
| Executive Summary | Three key HR messages for the month (one each: status, issue, proposal). Within one page. |
| Core metrics | This month's figures for the 5–7 selected metrics + change versus prior month + industry average comparison |
| Issues & causes | One or two notable metric movements this month and the causes analyzed. In the form "analysis suggests X is the primary driver." |
| HR proposals | One or two data-based improvement proposals, specified to an actionable level. Items requiring approval stated explicitly as decision requests. |
| What to watch next month | One or two metrics or issues to monitor next month. The section that makes executives see HR reporting as a forward-looking tool. |
The maturity assessment, metric selection, three-phase roadmap, and executive reporting design covered in this guide look like separate tasks, but in practice they only work when connected as one. Designing and executing this entire flow from scratch with internal HR staff alone takes considerable time and trial and error. HCG Consulting assesses your organization's current maturity on the basis of HR practice data from more than 950 companies, and designs the metric framework and execution roadmap that match that assessment with you. Which metrics executives actually care about, and how those metrics must be reported to lead to the next approval or investment discussion, is the territory consulting supports, designed to your organization's situation. The gap between having the data and persuading executives with it ultimately comes down to whether you have designed this flow end to end before.