Asking “So what?”: Transforming data into action with the Nigerian Red Cross
In a previous blog — 3 steps to data readiness with the Nigerian Red Cross — I wrote about how the British Red Cross is working with the Nigerian Red Cross to enhance their ability to respond to and manage emergencies through data-driven decision making.
One way that we were going about this was to introduce the concepts around Humanitarian Information Analysis for Emergency Needs Assessments.
But wait: what exactly are emergency needs assessments, and humanitarian information analysis?
After a disaster has occurred, Emergency Needs Assessments are undertaken — these are surveys conducted by the local Red Cross or Red Crescent. They are fundamental to understand the impact of a crisis and its humanitarian consequences.
Humanitarian Information Analysis looks to combine data from those Emergency Needs Assessments and other reliable secondary sources, to identify the priority geographical locations, population groups, and sectors. They are analysed in a systematic way and form the basis of, for example, an appeal or a response plan.
Back in October 2021, I had the opportunity to run a training with the Nigerian Red Cross to introduce tools and methodologies to assist effective analysis of data collected during these Emergency Needs Assessments. It aimed to enable data-driven decisions from needs assessments to assist the affected populations, by removing the “noise” from the critical information and identifying gaps in response information.
What did we cover?
This Introduction to Humanitarian Information Analysis training took place over a couple of days and was based on an existing week-long IFRC training. We were lucky to have a wide range of participants from the Nigerian Red Cross, IFRC and ICRC present who could all share their perspectives and experiences!
We focused on answering these questions:
- What is going on?
- How and why is this important?
- What will happen next?
This focus meant we would know who needs the most humanitarian assistance post-disaster, and how the Red Cross Red Crescent movement can best support communities to recover, and what the possible outcomes are if there was no intervention.
The training introduced topics such as:
- How to transform what the data says to what it means — such as “it is going to rain” to “we need an umbrella”;
- the different types of analysis from descriptive analysis to explanatory and interpretive analysis;
- and continuing to ask, “so what?” — what does an observation imply, why does it matter, and what can we conclude?
Using this introduction as a basis, we firstly identified what biases we as individuals hold and how that can impact our analysis, and ultimately the humanitarian response. By recognising these we noted that we can introduce frameworks and methodologies. These positively slow us down and make us challenge our own assumptions and hypotheses.
Secondly, we identified what the most important information needs are after a disaster has taken place– the priority geographical areas, population groups and sectors, and ensuring to compare these against pre-disaster data and information.
We then combined this theory together and put it into practice using a simulation. We used the IFRC analytical framework and categorised pieces of data and information from a disaster into different sectors (just like organising a wardrobe!). From this exercise, we could then also visually see any gaps in disaster information or any information that we would need to confirm.
In the photo below you can see these pieces of data and information slowly filling up a framework!
We then explored how we could apply severity and reliability tags to these pieces of information to identify what pieces of data or information should gain more weight than others.
We also delved into analysis and data collection plans. Analysis and data collection are tightly linked, meaning that the data collected is relevant and in a format for analysis that we wish to undertake. We looked at a couple of frameworks to assist us and ensure that we do not miss any data or information required for our analysis!
Once we have collected our data, we need to analyse it. First, we focussed on descriptive analysis: ways to group and reduce our data and information, combining like with like, whether this be based on location, population group, or sector; summarising our observations, clustering data and information into higher level statements; and finally, searching for stories in our data, what are the large- or small-scale patterns, or outliers which exist in our data.
Secondly, we focussed on interpretive analysis, drawing conclusions from our observations. To interpret, we identified that our analysis must be done in context, such as pre-crisis information, societal norms, existing coping mechanisms or how the disaster will evolve over time. We are therefore now moving away from summarising and explaining, to identifying what the data means — asking “so what?” and “why is it important and what should be done?”. We explored together how we could rank our data to support interpretation, draw conclusions as well as recognise that this type of analysis is shared, working with others in a team to come to conclusions.
We rounded up our Introduction to Humanitarian Information Analysis training with a session on how to deliver compelling documents and communicate our findings.
To conclude
It was an action-packed training, with presentations, simulations, and exercises. Being able to collect, categorise, describe, explain, and interpret data and information for Emergency Needs Assessments is critical in reaching those most in need when they need it. Information analysis can enhance existing methods and tools to support this.
The staff and volunteers of the Nigerian Red Cross Disaster Management programme now have access to these tools and methods and the understanding of how to apply them to support and enhance their own needs assessments in the future.
Thanks to all the participants from the Nigerian Red Cross, IFRC and ICRC for their engagement and enthusiasm — it was a great training!
