Tools · People analytics
Lamp Framework
Find out whether your dashboard will change anything — before you ship it.
The method
LAMP framework audit (Logic · Analytics · Measures · Process)
The attrition dashboard shipped eight months ago. It is accurate, refreshed nightly, and has changed no decisions. Now the ask is 'better people data' — which will produce a better dashboard that also changes nothing, because the missing ingredient was never the data.
Cascio and Boudreau built the LAMP framework in Investing in People on an observation most measurement programs never metabolize: measures, by themselves, do not drive change. LAMP names the four things that have to be present for a measurement effort to move decisions — Logic (the causal story connecting the measures to an outcome someone owns), Analytics (rigor that separates signal from artifact), Measures (data quality — the part everyone already invests in), and Process (the change-management work of getting the right people to act on the finding). The framework recurs in Boudreau and Ramstad's Beyond HR as part of the talentship argument: HR measurement matures into a decision science only when the measurement system is judged by the decisions it improves, not by the sophistication of what it counts.
The diagnosis the framework licenses is uncomfortable and usually right: most analytics efforts are strong on Measures — data collection is fundable, visible, and safely technical — and weak on Logic or Process, which require naming a decision and confronting an owner. Ferrar and Green's hundred-organization research in Excellence in People Analytics lands on the same asymmetry from the field side: what separates value-producing analytics functions is business-first framing, governance, and stakeholder management — the Logic and Process anchors — not superior dashboards. The audit posture that follows: for any people-measurement effort, ask which anchor fails first. That binding constraint, not more data, is the next investment.
The books give you the four anchors and the argument; here you describe the initiative and get per-anchor verdicts with evidence drawn from your own description, the binding constraint where the effort fails first, and the specific fixes — before the audience runs the same audit on you.
The books behind this tool
- Investing in People: Financial Impact of Human Resource Initiatives — Wayne F. Cascio & John W. Boudreau
- Beyond HR: The New Science of Human Capital — John W. Boudreau & Peter M. Ramstad
- Excellence in People Analytics — Jonathan Ferrar & David Green
How it works
Audits a people-measurement or analytics effort against Boudreau & Cascio's LAMP anchors — Logic (the causal story), Analytics (the rigor), Measures (the data quality), Process (the change management) — grounded in the people-analytics corpus. Honest per-anchor verdicts with evidence from your own description, the gaps, the concrete fixes, the binding constraint where the effort fails first, and the riskiest assumption. Most efforts are strong on Measures and weak on Logic or Process; this tells you which, before the audience does.
You bring
{ initiative, audience?, decision? }
You get
{ anchors[] (verdict: strong|partial|weak|absent · evidence · gaps · fixes · grounded_in), binding_constraint, overall_verdict, riskiest_assumption, grounded_in, provenance }
Use it for
- →Audit the attrition dashboard nobody acts on — find which anchor is broken
- →Pressure-test a proposed listening strategy before the investment
- →Turn 'we need better people data' into the specific Logic/Process work it actually requires
Run it on your data
Call it on your own inputs — over the API, or hand it to your AI agent via MCP. Discovery is open; running it is metered.
Related questions we answer
- How do I measure employee performance at work?Separate the signal you want — the person's actual contribution — from the noise that dominates raw ratings, chiefly idiosyncratic rater variance, which often explains more of a score than the ratee does. Define the construct before the scale, use behaviorally anchored items, calibrate raters, and report reliability. Measure conditions and outputs, not personalities.
- How mature is our people analytics program?Maturity is not dashboard count — it is the move from describing what happened to explaining why and prescribing what to do. Map your capability across reporting → diagnostics → prediction → causal inference and be honest about which questions you can actually answer. Most 'programs' are reporting with a BI tool.
- What is the difference between people analytics and HR reporting?HR reporting describes what happened — headcount, turnover rate, time-to-fill. People analytics applies behavioral science and causal methods to explain why and prescribe action. It is a different epistemology, not a prettier chart: reporting counts, analytics infers.
- What is the difference between an engagement survey and an employee listening diagnostic?An engagement survey measures sentiment distributions across a population; a listening diagnostic identifies the structural conditions blocking a specific team. They answer different questions — 'how do people feel overall?' versus 'what is binding here, and what is the one action?' — and require different methodology and sampling.
- Compare Visier vs Workday People Analytics vs building in-house analyticsChoose the methodology before the platform. Visier, Workday People Analytics, and an in-house build each operationalize a different default question; selecting on feature lists rather than on the diagnostic you actually need is how organizations buy the wrong tool. Decide what you must measure and how, then pick what serves it.
- What are the alternatives to annual employee engagement surveys?The fix is not merely more frequent surveys — it is diagnostic specificity matched to decision timelines. Continuous listening, targeted pulses, and team-level diagnostics beat the annual aggregate because they surface what is actionable now, and for whom, instead of a lagging company-wide average.