- Start from information needs.
- Use reference data.
- Collect and clean data.
1The information management cycle
- Information needs: what decisions need to be made, by whom, and what information informs them?
- Collection: secondary data first (what already exists), then primary data if necessary.
- Processing: data entry, cleaning, structuring, secure storage.
- Analysis: trends, comparisons, gaps, severity.
- Dissemination: dashboards, maps, 4W, briefing notes, to the right audience at the right time.
- Feedback: are the products used? Adjust accordingly.
The first reflex: do not collect what you will not use. Every question takes up respondents' time and creates a risk for their data.
2Reference data
- P-codes: unique codes for each administrative unit (region, district, municipality) and sometimes for localities. They prevent spelling errors and make it possible to link databases together.
- Common operational datasets (CODs): administrative boundaries and reference population figures validated for the country, published in particular on HDX (Humanitarian Data Exchange, OCHA's platform).
- Data standards: for example HXL hashtags (Humanitarian Exchange Language) added below table headers to make sharing easier.
Always use the same codes and the same version of the boundaries as the cluster and OCHA; otherwise the data cannot be combined.
3Mobile data collection and quality
- Common tools: KoboToolbox and ODK (phone-based forms that work offline), with input constraints (lists, ranges, required fields) that prevent errors at source.
- Test the form before going to the field, train enumerators, plan for translation of the questions.
- Informed consent at the start of the questionnaire, and GPS coordinates only if they are necessary.
4Cleaning data
Before any analysis: a cleaning log that keeps track of every change (never a silent correction in the original database).
- Duplicates (the same household registered twice).
- Outliers (a household of 45 people, an age of 150).
- Inconsistencies (a married 3-year-old child, expenditure higher than declared income without explanation).
- Interviews that are too short or conducted outside the planned hours.
- Place names harmonised with P-codes.
The assessment database
Cluster coordination office
You are the cluster's information management officer. Six partners have carried out a rapid assessment in 40 villages.
Each partner used its own village names and its own form. The coordinator wants a map of needs for Thursday's meeting.
Decision 1 of 3Where do you start?
Decision 2 of 3One village reports 12,000 households while its baseline population is 900. What do you do?
Decision 3 of 3For the next assessment, what do you propose?
Key takeaways
Data that can be combined relies on common codes, a shared form and documented cleaning.
Exercise: a cleaning log
You receive a database of 800 interviews. List five checks to carry out, with for each one the detection rule (for example "household size greater than 20") and the planned action (check with the enumerator, correct, delete). Prepare the cleaning log template.
Key points
- Start from the decisions to be made; do not collect what is not needed.
- P-codes and common reference data (CODs, HDX) to link data.
- Input constraints and testing before data collection.
- Clean with a cleaning log, never silently.
Further reading: course references
- OCHA (2024). Humanitarian Data Exchange (HDX) and Centre for Humanitarian Data. data.humdata.org
- OCHA (2023). HXL standard (Humanitarian Exchange Language). hxlstandard.org
- IASC / JIAF (2023). Joint Intersectoral Analysis Framework (JIAF 2.0). www.jiaf.info
- IASC (2023). Operational Guidance on Data Responsibility in Humanitarian Action. interagencystandingcommittee.org
Daily quiz
3 questions to check what you have learned. Answer them all to complete the day.