My team used the image approach at our weekly standups! Thanks for reminding me of this. It was incredibly effective and lighthearted (e.g. for Halloween we could choose between 9 pictures of carved pumpkin faces).
I’m really intrigued by people analytics applied to music. Many professionals in the music industry would claim they have a special skill of spotting the “next big thing”. But to what extent is this uncovering hidden patterns in existing data (easy for algorithms to do), versus deciding that something completely new could be popular (difficult for algorithms to do). I hope we find out! My cousin is writing his high school capstone analyzing songs that go viral on TikTok, and I think there is much more research to come.
This is a great idea. Most companies “accidentally” collect a lot of employee data, and this would help the ones who have no intention of using it punitively to differentiate themselves with prospective employees. Also to consider: This should entail erecting firewalls within organizations that reflect what the data need is (e.g. HR has access, but not individual managers).
I think you’ve identified an important tension! Curious how we could structurally manage this in People Analytics. For example, in our LPA class we have a “best practice” to develop a hypothesis (aka narrative) before we run a regression, which pushes us in one direction.