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On April 20, 2022, Graciela-Brewer commented on Balancing robots and humans in talent decisions :

I completely agree with your sentiments overall presented here, as I also worry about the role technology can/should play in “fixing” the hiring process. One thing in particular that stood out to me however, is the role of current HR managers in this equation. Aside from not typically having formal people analytics training, I think there’s a deeper disconnect as well, which is that many people in HR were drawn to the field due to the person to person interaction. It is very common for HR professionals to not be as generally tech aware as other departments within the same company. I experienced a similar trend when working in behavioral health technology. We often worked with social workers, which is another field where much of the workforce is interested in personal relationships rather than technical skills. We struggled to get this cohort of our customers to adopt the technology due to lack of know how/interest, and I could foresee a similar set of obstacles as the HR field becomes more demanding in regards to the people analytics skills required.

On April 19, 2022, Graciela-Brewer commented on Improving Uber User Experience Through Causal Inference Analysis :

I appreciated your very easy to understand explanation regarding causal inference analysis as compared to the regression analyses we’ve been discussing in class! I find this application very exciting as a user experience designer. This type of data could be so influential in how information and interactions are laid out within a product. This application of people analytics is also a bit different than much of what we’ve discussed in class because it is performing the analysis externally on customers, rather than internally in an HR capacity.

Additionally, I wonder if this is the same type of analysis that Netflix uses for their cover images within the app. Netflix personalizes the image of each show/movie based on your specific profile and what it feels you’ll be most drawn to. I know Netflix is very big into experimentation and data analysis, so I’m curious if this specific type of analysis is occurring to identify if, at a statistically significant level, cover image selections are causing a change in customer viewing selections.

On April 19, 2022, Graciela-Brewer commented on Trust Issues: Obstacles to Using People Analytics for DEI Efforts :

I find it really interesting to consider the role of people analytics within DEI efforts, so thanks for sharing this article! I think a crucial part of building that trust is having representation at each stage of the process. This also addresses BRolan’s previous comment. The way an organization can demonstrate that they truly empathize with the various populations within their company is to have those voices incorporated along the pipeline. I feel that having BIPOC, women, LGBTQ+, etc. input across the life cycle of a DEI project is vital to garnering employee trust. Including diverse voices from the start can ensure projects are being structured in ways that don’t have blindspots that could affect the results. Discovering the “why” of the findings within the data will also be easier and more accurate if you have representation during the analysis phase as well. Finally, the lived perspective of various groups can make the communicating of results to leadership all the more impactful. The deeper consideration here though is to not automatically burden all underrepresented groups with this type of work where they feel obligated to be the “mouthpiece” for all who have a similar identity to them. This type of work definitely requires a thoughtful approach to collaboration.