Tools · People analytics
Onboarding Plan
Describe a role — get a 30-60-90 onboarding plan.
How it works
Corpus-grounded (people-analytics cluster). Phased ramp (goals → activities → success signal per phase, learning to ownership), plus the stakeholders to meet, resources to set up before day one, and the metrics.
You bring
{ role, cluster? }
You get
{ role_summary, phases[]{phase, goals[], activities[], success_signal}, stakeholders_to_meet[], resources_needed[], success_metrics[], riskiest_assumptions[], grounded_in, provenance }
Use it for
- →PA-guide reader: a manager's ready-to-use 30-60-90 for a new hire
- →Front-load relationships + context before output
- →Set the success signals per phase
See it work
example outputRole: the first People Analytics Manager at a ~600-person SaaS company, reporting to the CHRO and building the function from scratch.
30-60-90 Onboarding Plan — People Analytics Manager
Role: The company's first People Analytics Manager at a ~600-person SaaS firm, reporting to the CHRO — building the function from scratch.
First week
- Goals: Get access, meet the immediate team, map the data landscape.
- Activities: IT/data access provisioning · 1:1s with the CHRO and HRBP leads · inventory the HRIS, ATS, and engagement-survey data.
- Success signal: Can name where every core people-data source lives and who owns it.
Day 30 — Learn
- Goals: Understand the business questions, not just the systems.
- Activities: Interview each HRBP and two or three business leaders on their top people questions · audit current reporting · assess data quality and joins.
- Success signal: A prioritized list of the five to seven questions the function should answer first, validated with the CHRO.
Day 60 — Contribute
- Goals: Ship one credible, end-to-end analysis.
- Activities: Build the first analysis (e.g. a regretted-attrition driver model) · stand up a basic reporting cadence · document data definitions.
- Success signal: One analysis presented to the leadership team and trusted enough to inform a decision.
Day 90 — Own
- Goals: Own the roadmap and set the operating cadence.
- Activities: Publish a 6-month people-analytics roadmap · agree on SLAs for recurring requests · propose the next hire or tooling investment.
- Success signal: Roadmap approved; stakeholders route people-data questions to the function by default.
Stakeholders to meet
CHRO (manager) · HRBP leads · Talent Acquisition lead · the Finance/FP&A partner · Data Engineering · two or three business-unit leaders.
Resources to set up before day one
HRIS + ATS access · a sanctioned analytics environment (warehouse / BI) · the data dictionary (if one exists) · the last two engagement-survey readouts · the org chart + headcount file.
Success metrics
Time-to-first-shipped-analysis · stakeholder-trust pulse · % of recurring reports automated · roadmap approved by day 90.
Riskiest assumptions
- That clean, joinable data exists on day one (the real first-90-days work is often plumbing).
- That leadership wants insight, not just more dashboards.
Grounded in: people-analytics onboarding canon — a phased learning-to-ownership ramp with stakeholder mapping.
Run it now
Build a 30-60-90 onboarding plan
Turn a role into a phased onboarding plan — goals, activities, and success signals per phase, plus the stakeholders to meet, resources to set up, and metrics.
Prefer code? Call it over the API or hand it to your AI agent via MCP — POST /api/bicycle/onboarding-plan · build_onboarding_plan. API & agent access →
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.