Tools · General business
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.