Forced displacement is a very important topic of discussion in our current society, especially in relation to climate change. Reflecting on my intensive class on forced displacement opened me up to the different challenges faced by forcibly displaced people. Among these is poor treatment, as treating them as data objects is quite dehumanising. My observations also included that these victims were subjected to denial of some services due to their inability to present identification, some as a result of statelessness. Such services include healthcare and immigration, among others. More dehumanising were also the strategies used by governments, which at thought to help manage immigration influxes but made victims of the affected people. These strategies included the use of inhumane tools for the management of refugee influxes in those countries. Beyond the present, the effects of this can be damaging to the mental health of displaced people. Then it begs the question of whether the concerns surrounding displaced people are being handled. Well, they may be, partly thanks to the United Nations High Commissioner for Refugees (UNHCR).

I was increasingly appreciative of the efforts of the UNHCR aimed at addressing this intricate global problem while working on a dataset from the UNHCR. I recall analysing the displacement dataset from the UNHCR from 1951 to 2022 during an intensive on Data Science for Society course. One outstanding aspect of this work is the profound impact of a troubled world on the people living within it. Analysing the extensive data related to forced displacement not only broadened my understanding but also instilled in me a deeper sense of empathy toward those enduring such challenging circumstances.  I could recall, looking at the statistic, that 1.4 billion people have been affected by forced displacement over the last 72 years, and just so little has been done to alleviate their pain.

Despite not having an advanced skill set in Python for data analysis, my commitment to resiliently identify factors that could contribute to policy formation took precedence in my approach, a decision that significantly influenced my perception of myself as a problem solver and solution provider. To bridge the gap in my analytical skills, I proactively used online tools and sought assistance from video tutorials. I thought the lasting impacts of the trauma and the experiences underscored the urgency of addressing the inadequacies in current alleviation efforts. While I entertained thoughts questioning the efficiency of the UNHCR, I remained mindful of my position as a non-authoritative figure and acknowledged potential discrepancies in the data.

This experience motivated a personal desire to contribute more meaningfully to such endeavours addressing issues affecting underserved communities. Engaging in further data analysis, aiming not just to inform decisions through my findings but, more crucially, to devise and implement solutions to societal challenges, makes me feel more persuaded. This newfound commitment has triggered a cascade of questions within my mind—can these displacement events be predicted? Can we forecast the number of affected individuals to prompt proactive preparations? How can we effectively address the vulnerabilities of specific groups within the broader affected populations?

In my exploration for answers, it became evident that data holds the key and using data becomes imperative not only for offering solutions but also for guiding the planning and execution of interventions and for enforcing policies that address the complex and multifaceted challenges posed by forced displacement. Hence, i could say I am observing a leaning towards addressing issues of inequality for disadvantaged communities. While the specific topic is not yet know, I hope to continue questioning.

Thoughts: Inequality and Disadvantage / Onyebuchi Enechukwu / Knowledge Integration and Project Planning: Data, Inequality and Society (2023-24) by is licensed under a