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- Exploratory
- Aggregation, disaggregation
- Chronologies, timelines
- SDR
- Describe
- Prioritization (sudden onset)
- Severity (sudden onset)
- Explain
- Cause and effect
- Interpret
- Ratings, rankings and uncertainty
- Anticipate
- Prediction, forecasting
- scenario building
Exploratory
Most of the focus is on finding whether the analyst has data to start the analysis, structure the existing data Focus: identify if data required is available (credible, reliable, timely) and structure it in a way that best suit the requirement and identify identifies information gaps. What data is available, which sources you have to your disposal, and whether it can be used.
- Familiarise yourself with the data and check its characteristics - How relevant, sufficient and reliable is the data?
- Clean & enrich your data to ensure it is as good as it gets - How clean and ready for analysis is the data? Do I have enough data?
- Are potential signals hidden in the data?
- Sort, aggregate and disaggregate and define suitable taxonomy of categories. Code & refine your data – Can the data be better prepared for queries?
- What are the main results so far?
Examples of analysis findings
- There is a variety of information sources on food insecurity in country X, primarily from IPC, Fewsnet, WFP and FAO.
- Some are purely observational, some are quantitative.
- Recent figures on food security are available after a comprehensive national survey by WFP
- Findings are mostly aligned.
- No recent information is available from the southern region where accessibility is limited
- There seem to be higher levels of food insecurity in rural areas.
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