Migrating to a new country is challenging, marked by cultural shocks and language barriers. As an immigrant, I understand the daunting nature of this process. However, my journey can’t compare to that of asylum seekers and refugees, who are often displaced due to war, persecution, or other life-threatening circumstances. Compared to immigrants, they lack the luxury of preparation. Luckily, numerous charities assist them at every step, providing crucial support during a challenging time. This essay delves into the complexities of working with data from such charities, particularly those aiding refugees and asylum seekers.
Cleaning the data
The initial hurdle in this research was the need for clean data. Human errors compounded the issue, as charities typically fill out simple forms that are later input into a database by individuals. Such a process increases the chances of discrepancies; unfortunately, that’s precisely what happened in this case. The dataset was riddled with contradicting information, creating a formidable challenge for analysis. For example, some charities claimed their primary beneficiaries were females, yet their data consisted of many male beneficiaries, showcasing the prevalence of inconsistencies.
To address the problem of dirty data, I adopted a two-fold strategy. First, I narrowed my scope to the context of charities working with refugees and asylum seekers. I further refined my focus by examining the specific services offered. This approach aimed to understand the support sought by asylum seekers and refugees, leading to the identification of crucial services such as mental health, language, and employment training.
Additionally, I opted to select charities not only based on their main beneficiaries but also by extracting relevant keywords from the descriptions of their work. This meticulous approach allowed me to filter and choose the data necessary for building an insightful dashboard.
Creating the Dashboard
For the Tableau dashboard, simplicity was the guiding principle. The most intriguing aspect to explore was the funding sources for these charities. Remarkably, except for two charities, the top 10 organizations working with refugees and asylum seekers largely depended on government funding. This discovery became the focal point of my insights, prompting an investigation into why these two charities stood out despite not relying extensively on government support.
Through the dashboard, I aimed to convey the dependence on government funding and the unique factors contributing to the success of the two outliers. The visual representation provided a comprehensive view of the landscape, making it easier to grasp the dynamics of charity support for refugees and asylum seekers.
In conclusion, the journey of compiling and analyzing data on charities assisting refugees and asylum seekers underscored the challenges posed by dirty data. However, valuable insights emerged by adopting a strategic narrowing of scope and a meticulous selection process. The resulting Tableau dashboard highlighted the prevalent reliance on government funding and shed light on the exceptional cases that defied this trend. This exploration is a testament to the importance of clean and targeted data in understanding and addressing complex social issues.