Organizational & People Analytics

Validated measurement + modern data science. Decisions you can defend across hiring, performance, engagement, retention, and org design.

Start a People Analytics Project

Decision-grade rigor

Confidence intervals, effect sizes, and lift estimates tied to business thresholds—so leaders can act with clarity.

Fairness by design

DIF/bias checks, model cards, and auditable pipelines. Human + AI workflows with explicit guardrails.

Weeks to signal

Lightweight ingestion, templated dashboards, and incremental rollouts—value in weeks, not quarters.

What this service delivers

Validated measurement

Construct clarity, factor structure (EFA/CFA), reliability (α/ω), IRT scaling, and interpretable score bands.

Human + AI workflows

Explainable models, parity audits, versioned features, and transparent governance embedded end-to-end.

Operator-ready artifacts

Executive briefs, tech appendices, action guides, and runbooks aligned to your cadence and ownership.

Business linkage

Metrics wired to revenue, quality, cycle time, and risk—so change management earns fast internal buy-in.

Core modules

Hiring & Selection Analytics

Score calibration, pass-rate analysis, adverse-impact review, utility modeling, interview/screener validation.

Implementation process →

Performance & Productivity

Role KPIs, manager/peer calibration drift, leading indicators tied to output, quality, and customer outcomes.

Methods →

Engagement & Culture

Validated survey design, driver analysis, and team-level action guides grounded in practical effect sizes.

Case snapshots →

Retention & Mobility

Survival analysis, risk cohorts, opportunity mapping for internal moves, mentorship, and career pathways.

Toolstack →

Org Design & Headcount

Span-of-control health, scenario planning, budget-sensitive staffing models linked to demand and SLAs.

Implementation process →

Methods & measurement

Psychometrics

Construct mapping, EFA/CFA, reliability (α/ω), IRT, DIF/fairness checks, score norms and interpretability.

Causal & Predictive

Uplift modeling, panel/DiD, multilevel/hierarchical, survival/hazard, interpretable ML with SHAP/ICE.

Experimental Design

A/B and multivariate tests, power analysis, sequential monitoring, guardrail metrics and risk bands.

Ethics & Governance

Bias audits, privacy by design, model cards, versioned data dictionaries, decision logs and approvals.

Toolstack & delivery

Data ingestion

ATS/HRIS/CRM exports, survey platforms, product and finance signals; lightweight ELT + schema harmonization.

Analysis & modeling

Python/R pipelines, reproducible notebooks, validation reports, scheduled jobs where beneficial.

Dashboards

KPI views with uncertainty bands, driver drill-downs, cohort trends, and role-based access patterns.

Artifacts

Executive brief, technical appendix, survey/assessment tech-pack, operational runbooks and handoffs.

Example outcomes

Selection utility

Score-based thresholding increased quality-of-hire index by 14–22% at constant pass rates, with zero flagged DIF.

Engagement drivers

Manager clarity + task autonomy explained 31% of variance; action plans reduced regrettable attrition by 6 pts.

Capacity planning

Span-of-control tuning lowered cycle time 9–12% while holding budget neutral through redeployments.

Implementation process

01. Scope & metrics

Clarify decisions, success criteria, constraints, and required signals; define a minimal viable metric set.

02. Data & validation

Ingest sources, map entities, run quality checks, validate measures for reliability, fairness, and stability.

03. Models & insights

Fit interpretable models, quantify effect sizes, and link findings to practical actions and thresholds.

04. Activation & governance

Ship dashboards, action guides, runbooks; institute review cadence, decision logging, and model stewardship.

Discuss scope & data sources

Common questions

Data requirements

Typical starts: HRIS/ATS exports, survey CSVs, plus performance or ticketing data. Minimal viable inputs are fine to begin.

Bias & fairness

Every relevant metric/model is parity-checked; you receive documentation and mitigation recommendations.

IT & security

File-based transfers or secure connections supported. No production changes required to start discovery work.

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