People-Analytics on Workforce Data Lab

Absence and PTO Analytics: Skewed Distributions, Calendar Artefacts, and the Burnout-Proxy Problem

Tue, 04 Aug 2026 09:00:00 +0000

Absence data looks like the simplest feed in the HR stack — days off, per person, per month — and is one of the easiest to misread. The distributions are not the shape analysts assume, the calendar contaminates every monthly comparison, and the field’s favourite use of the data, as a burnout early-warning signal, is weaker than its reputation. This piece covers all three, with the statistical posture we recommend for each.

Workforce Planning Scenarios: Driver-Based Models vs Budget Arithmetic, and the Assumptions Register

Tue, 14 Jul 2026 09:00:00 +0000

Most workforce plans are budget arithmetic wearing a strategy costume: last year’s headcount, plus or minus a negotiated percentage, spread across cost centres. That process produces a number. It does not produce a plan, because it contains no model of how headcount relates to anything the business actually does. Driver-based planning is the alternative, and its track record is mixed for an instructive reason — teams build models with dozens of drivers when the variance is carried by five. This piece covers the structural difference, the small driver set that matters, and the publishing discipline that separates a scenario from a guess.

GenAI in People Analytics: Where LLMs Help, Where They Fabricate, and a Validation Protocol

Tue, 19 May 2026 09:00:00 +0000

Large language models have entered the people-analytics workflow faster than the validation practices around them. Eighteen months of watching teams deploy them has produced a clear pattern: the failures concentrate in specific task types, not in “AI usage” generally. This piece separates where LLMs genuinely add capability from where they manufacture confident error, and closes with the validation protocol we now consider the floor for AI-assisted people analytics.

Where LLMs genuinely help

The reliable use cases share a structure: the model transforms text into structured, checkable categories, and a human can verify any given output.

Pay Equity Analysis: The Specification Choices That Change the Answer

Tue, 07 Apr 2026 09:00:00 +0000

Two competent analysts, given the same payroll extract and asked the same question — “is there a pay gap?” — can return materially different answers, and both can be defensible. The divergence is rarely in the statistics. It is in three specification choices made before the model is fit: the model form, the control set, and how the adjusted and unadjusted gaps are presented alongside each other. This piece walks through each choice and the reporting risks that follow.

Internal Mobility Metrics That Hold Up: Definitions, Denominators, and the Gaming Problem

Tue, 17 Feb 2026 09:00:00 +0000

Internal mobility is the metric every organisation wants to report and few organisations define tightly enough to survive an audit. The numbers travel upward — to CHRO decks, board packs, ESG narratives — and each hop strips away a denominator. This piece lays out definitions that hold up for the three core metrics (internal fill rate, time-to-fill for internal moves, and the post-move outcome window), then catalogues the four places teams quietly game them.

Skills Data Quality: Inferred vs Attested Skills, and How to Decide with Bad Mirrors

Tue, 02 Dec 2025 09:00:00 +0000

Every skills-based initiative — talent marketplaces, workforce planning, succession analytics — inherits the error structure of whatever produced the skills data. That provenance is almost always one of two processes: inference (resume and ATS parsing, profile extraction) or attestation (self-report, manager endorsement). These two sources fail differently, predictably, and in ways that matter for which decisions each can support. This piece maps the error profiles and proposes a decision-bounding framework for the common situation: no ground truth available.

Engagement Survey Methodology: Why the Instrument Moves More Than the Intervention

Tue, 16 Sep 2025 09:00:00 +0000

The engagement survey is the most analysed and least scrutinised instrument in people analytics. Organisations invest in interventions to move scores by a few points, then change the questionnaire in the same cycle and attribute the resulting shift to the programme. This piece covers three methodological problems we consider structural rather than incidental — common-method bias, the anonymity-perception effect, and item-wording effects — and closes with survey-design guidance we now treat as baseline.

Attrition Risk Models: What a Defensible Model Actually Needs

Tue, 10 Jun 2025 09:00:00 +0000

Most attrition models in production today would not survive a methods review. Not because the algorithms are wrong, but because the surrounding apparatus is missing: the features leak, the probabilities are uncalibrated, and nobody is watching the model after deployment. This piece lays out the four requirements we now treat as the bar for a defensible attrition model — feature families that generalise, calibration alongside discrimination, a disciplined leakage audit, and drift monitoring treated as the actual product.