Today’s post is somewhat of a precursor to the thought process behind the analysis. Recalling that the goal is to establish a set of recommendations for not-for-profit to help facilitate advertising their fundraising event and simultaneously recruiting potential donors. Keeping in mind the needs of the client is key for a) acquiring or utilizing the relevant information to address them, and more importantly b) making sure that whatever the results end up being are presented in a way that the client will be receptive to. In the case of this scenario, the not-for-profit has the mission statement of helping further the careers of women in STEM who come from traditionally underrepresented groups in the industry. If I was to consider a pure results-based approach of working to obtain the most donations possible, I think that would not translate well into long-term sustainability. So, the next step is to consider who the ideal person my client is looking for which from my understanding, would be someone who would be enthusiastic about supporting diverse women in STEM and helping financially.
Having the perfect donor in mind is for sure the path we want to go down, but actually locating and persuading them demonstrates that the way there is fraught with thorns and unexpected issues. There does also come into play a component of ethics in data science that was especially highlighted throughout my time at Metis. There are a multitude of tools that can and will be used to develop “customer profiles” on people that have overreaching impacts on everyday folks, so it is our duty to fight back against that in a way to keep the privacy and dignity of people intact so as not to reduce people to potential consumers of said product. Finding the right balance is crucial to do so for anyone working in spaces that collect data on its customers. I do my absolute best to live up that promise and I hope to continue that in the future! Short post today, but I wanted to highlight some of the thoughts I had going into this first project. Tomorrow, I will conclude with a brief about the additional datasets I used and the visualizations that went along with it!