Path
Humanitarian Information Manager
For the IM officer holding a cluster's shared data — assessment analysis, inter-agency reporting, and the responsibility questions that come with holding data about people in crisis.
Competencies
Assessment analysis
IntermediateTurn a multi-sector needs assessment into severity and coverage figures a cluster can plan against.
Inter-agency reporting
IntermediateProduce figures multiple agencies will report against, and reconcile them when two members count the same beneficiary differently.
Classification against standards
AdvancedApply Sphere, IPC and JMP thresholds correctly, including the conditions people routinely drop.
Data responsibility
AdvancedDecide what should not be collected, what must not be shared, and at what aggregation a figure stops being safe.
Before you start
- Field or coordination experience in a response — you have watched a 4W, a cluster report or an MSNA arrive late, partial and inconsistent.
- No programming background required.
The route through
Stage 1
Ground the numbers
Define the indicators a cluster reports against, and say what a partner's submission does and does not establish before it reaches a dashboard.
Stage 2
Make the data trustworthy
Turn a raw export into a table you would defend line by line, and hand over a cleaning log that answers the auditor's question before it is asked.
Stage 3
Put the files together
Assemble several exports into one analysis table you would defend column by column, with every join proved, every grain stated and every denominator sourced.
Stage 4
Check it like an auditor
Assess your own data before a donor does — five dimensions with measures, a recount against the source, and a report where every finding has an owner and a date.
Stage 5
Define what you report
Write indicator definitions two analysts compute the same way, defend the denominator, and set a baseline and target that survive a mid-term review.
Stage 6
Put an interval on it
Analyse a cluster survey the way its design requires — weights from the frame, a measured design effect, and an interval that says what the sample can and cannot settle.
Stage 7
Know where it came from
Read a routine reporting system as the database it is, pull an extract you can point at months later, and answer what was counted for any figure it produces.
Stage 8
Apply it to nutrition
Take the methods into one sector — WHO growth standards, SMART plausibility, the IPC phases and the Sphere performance thresholds, on a survey and a treatment register.
Stage 9
Apply it to public health
Rates with person-time denominators, treatment cascades, coverage three ways, and an outbreak line list turned into a curve, attack rates and a case fatality you can defend.
Stage 10
Apply it to WASH
The JMP service ladders and the Sphere minimums on a household survey, then a monitoring register with repeat visits that turns one functionality rate into three and bounds each against the rounds nobody drove.
Stage 11
Apply it to food security
FCS, HHS, rCSI and the livelihood coping module built from raw components and found to disagree by a factor of seven, then a price series whose seasonality dwarfs the programme effect and the evidence table that reports both.
Stage 12
Apply it to protection
The analyses you must decline to publish, alongside the ones that matter — a consent-gated referral pathway, a nineteen-point equity gap located at a specific gate, and a caseload that explains two other tables.
Stage 13
Apply it to education
Gross against net enrolment on a projected denominator, two attendance numbers twenty-six points apart, a cohort through promotion and repetition, and two assessment rounds whose instruments differ.
Stage 14
Say how sure you are
An interval on every proportion, the right test for a comparison, an effect size beside every p-value, and the count of comparisons that turns two striking schools back into noise.
Stage 15
Model more than one thing at once
A coefficient is a comparison — which one, between which units, adjusted for what. An odds ratio your reader will misread, a covariate that removes 42% of the effect, and a model that explains three per cent and settles a targeting decision.
Stage 16
Say what caused it
A seven-point gain that is entirely the school year, a comparison group imbalanced on every characteristic measured, and the minimum detectable effect that decided the answer before any data existed.
Stage 17
Put it in front of them
The mark the comparison implies, an interval that stops a ranking, a palette that already means something to this audience, and a figure generated from the dataset so the chart and the sentence cannot drift.
Stage 18
Make it rerunnable
Raw data read-only and code the only thing edited, an environment pinned so a colleague's laptop gives your numbers, checks that stop the pipeline rather than producing a plausible wrong one, and a handover a successor can act on.
Stage 19
Put it in front of the decision
A page that answers one question rather than twenty, the definition panel that stops the monthly argument, and the report whose findings, limitations and recommendation survive being read separately.
Recommended projects
How the skill is assessed
Can place households on a JMP service ladder correctly, including the collection-time condition.
EvidenceA coverage analysis that reports both the source-based and the service-based figure and explains the gap.
Can state a suppression rule for a sensitive dataset and justify the threshold.
EvidenceA data protection note naming the threshold and the party it was agreed with.
Can produce a district ranking whose conclusion survives dropping any single input.
EvidenceA stability check reported alongside the ranking.
An information manager is trusted with data that several organisations depend on and one of which collected. That is a technical job and a custodial one at the same time, and the custodial half is what this path takes seriously.
The protection project here leads with what is not collected — no names, no free text, no incident date, no location below admin2 — and treats that restraint as the deliverable rather than as paperwork. The small-cell suppression threshold it uses is recorded as a protection decision taken with the case management agency, not as a formatting preference, precisely so the next person to edit the analysis cannot quietly change it.
What this path still needs
Three courses are published: Data Analysis Foundations, Python for Programme Data and R and the Tidyverse for Programme Data. Assessment and classification work sits in Sector Analysis on the programme roadmap, and survey design in Indicators and Measurement; neither is written yet. See the programme.