Data AnalysisHumanitarian WorkDecision Making
The Data-to-Decision Journey: A Practical Guide for Humanitarian Work
Shahzad ASGHAR3 min read
In today's world, data has become a cornerstone of effective decision-making, especially in humanitarian work where lives are impacted by each choice. The process from collecting raw data to making actionable decisions is not always straightforward, but when broken down into steps, it becomes easier to manage and understand. Let's explore the key stages of the data journey with examples relevant to the work I do.
This is where it all begins. Data collection involves gathering information from various sources to create a foundation for further analysis. In humanitarian operations, this could include:
- Data Collection
Collecting survey responses from refugees during needs assessments.
Recording calls received through a helpline to understand pressing concerns.
Monitoring data from registration systems like ProGres v4 to track demographics.
Example: During a recent needs assessment project, we collected survey responses from refugees across different regions to identify their priorities, such as healthcare access and livelihood opportunities. This step ensured that decisions were rooted in the realities faced by affected populations.
Raw data is rarely perfect. This step ensures the information is accurate, consistent, and ready for analysis. Tasks like removing duplicates, handling missing data, and structuring datasets fall under this stage.
- Data Cleaning & Preparation
Example: For a refugee skill-mapping initiative, we had to clean data imported into ProGres v4. This involved cross-referencing entries to avoid duplicate registrations and ensuring all skills were categorized correctly. Without proper preparation, the analysis could have led to inaccurate conclusions about skill availability.
This is where the cleaned data is explored to uncover patterns and trends. Various tools and statistical methods are applied depending on the complexity of the data and the questions being asked.
- Data Analysis
Example: During the analysis of polio immunization data, we identified gaps in vaccine coverage by comparing regional immunization rates. This allowed us to target campaigns more effectively, ensuring no child was left unprotected.
Insights are the "aha moments" that come from analyzing the data. These are the key takeaways that directly inform strategic decisions.
- Insight Generation
Example: While analyzing calls received through a refugee helpline, we found a significant number of requests related to food insecurity in certain camps. This insight led us to prioritize food distribution efforts in those areas, addressing the most urgent needs.
The ultimate goal of the entire process is to support informed decisions. At this stage, stakeholders use the insights to take action, whether it's allocating resources, designing new programs, or adjusting ongoing efforts.
- Decision-Making
Example: In one project, the analysis of registration data showed an increase in elderly refugees with specific healthcare needs. Based on this finding, we coordinated with partners to establish mobile clinics, improving access to care for this vulnerable group.
Why This Process Matters
Each step in the data journey is interconnected, and skipping one can lead to flawed outcomes. For instance, poor data cleaning can result in misleading analysis, while inadequate analysis might fail to uncover critical insights.
By following this structured process, we ensure that decisions are backed by evidence, helping us make a meaningful impact on the communities we serve.
Conclusion
The path from data to decision-making may seem daunting, but it becomes manageable when approached step by step. Whether it's identifying gaps in vaccination coverage or responding to urgent needs highlighted through helplines, this journey enables us to use data as a tool for actionable and impactful change.
