Improving analytical practices & skills
Over the last couple of years, the amount of data produced in humanitarian emergencies has increased drastically. In 2010 an average of 215 reports were produced per disaster - by 2018 this number had reached 600, according to Reliefweb statistics, and the numbers continue to grow.
With the increasing number of methodologies and tools for information management and data collection in humanitarian crises, the sense-making component has unfortunately been largely overlooked, as if displaying graphs and maps on a dashboard for users to interpret themselves were all there is to humanitarian analysis. Never before have we had so much information and been left with so many questions.
In this section you will find materials and tools on how to improve analytical practices and skills.
Biases are normal processes designed to make decisions quickly. They are unconscious, automatic and non-controllable and there is no magical solution to overcome these reflexes.
However, knowing their effects, when and where they apply as well as some key structured techniques, can help mitigate their negative consequences. Systematically identifying their effects on your analysis is a habit that each analyst should possess.
Scientific Thinking in Humanitarian Analysis
Scientific thinking needs to be taught and cultivated so it becomes seemingly intuitive when humanitarians conduct analysis under pressure and tight deadlines.
Logical Reasoning in Humanitarian Analysis
The aim of logic is to develop a system of methods and principles that can be used as criteria for evaluating the arguments of others – and as guides in constructing arguments of our own. It is thus critically important for analysts to apply logical reasoning in order to provide good analytical products.
"Homo Analyticus" - Humanitarian Analyst Profile
Humanitarian analysts apply specific frameworks, structured techniques and analytical standards to review, evaluate and make sense of the often partial information available. They tailor their analysis to their end user’s specific questions (e.g., who are the affected groups most in need of assistance after the earthquake), contributing to the design and implementation of more efficient humanitarian programmes.
This document provides guidance for data analysts to find the right data cleaning strategy when dealing with needs assessment data. The guidance is applicable to both primary and secondary data. It complements the ACAPS technical note on How to approach a dataset which specifically details data cleaning operations for primary data entered into an Excel spreadsheet during rapid assessments.
Spotting Dubious Data
This technical brief provides practical guidance on how to interpret the context. It provides a list of common problems found in the numbers appearing in humanitarian reports and illustrates these problems with examples.
Below a list of infographics illustrating some of the key concepts.
The Analysis Workflow:
The Analysis Canvas:
The Analysis Spectrum:
Cognitive Biases in Humanitarian Analysis:
Sources of Errors in Humanitarian Assessments:
Spotting Dubious Data: