Hi Meghna! The connection between analytics and public health is such an interesting topic! All of the points you raise about how to share this data, if it is missing any crucial groups of respondents, etc. are really important. I was also surprised to learn that academics are some of the gatekeepers of this data, I had always assumed it was just large corporations. I totally agree with you that if this data continues to be collected, there should be a pipeline for making it available (as long as it’s anonymous). That’s the case with large scale census or other survey data. Good point re tracking vulnerable communities, though I also wonder if there are instances in which this type of data can actually reveal or spotlight how vulnerable communities are disproportionality harmed by certain health crises? Part of the issue seems to be concerns that there’s a lack of transparency when it comes to how this data is collected/used. If there was full transparency and the anonymous data was made widely available, would this help? Would really clear and transparent opt-out policies (similar to what we are seeing now for cookies) allow people to consent? I’m not sure there’s a clear answer, but I enjoyed thinking about these issues!
Hi Charles! I found this post really thought provoking – using big data to understand what predicts worker turnover, especially in low-wage contexts, is much needed. And what I particularly love about your post is that it indicates the need to have both big data AND people with expertise in the field interpreting and reacting to that data. It was fascinating to read how you reacted to the findings and what you thought the authors should have included or explored. I found the lack of relationship between turnover and retirement/time off also surprising. I wonder if part of it is about labor market options? Like maybe workers know they wouldn’t find these benefits in other similar jobs, so it doesn’t influence their turnover intent. And/or perhaps it’s because so few workers actually have access to these benefits, so there’s no way to analyze whether these factors predict turnover?
Hi Elena! I really love the examples you share about how an overreliance on analytics can lead to biased decision making. I totally agree with you. As you highlight, the problem seems to be a lack of clarity around what the tools are actually doing, and an assumption that they can overcome certain human biases (which as you also point out, is very much not the case!). I wonder if there are ways to grapple with these limitations? Do you think additional trainings on the tools is helpful? Or maybe it’s about thinking of people analytics as one of our many tools that should be used only when appropriate (like how a hammer and screwdriver have unique functions)?