Introducing operating models into the debate around people analytics allows us to discuss infrastructure necessary to set up ethical and effective practices.
Onboarding faces persistent challenges. But its current importance is unquestioned, and organizations should recognize it if they are to build a balanced, healthy, and equitable workforce.
In this article, I describe the possible perils of human analytics.
How data analytics and people analytics could potentially change the landscape of K-pop music industry.
Googlegeist is a great initiative to gather feedback and seek the truth, but two questions remain: (a) can we trust the truth from this survey?; and (b) does this truth lead to meaningful resolutions?
In our first class this semester, back in the halcyon days of our pre-quarantine innocence, Professor Polzer posed a question: Are employees trusted partners in a mission or cogs in a machine? Though we have discussed and debated, pondered and premised for almost a full semester now, these authors feel no less conflicted as to this central tension of people analytics than we did almost three months ago.
Reactions to the Indian government’s decision to make government attendance data public.
In this post, I share my thoughts on the article, “Employee mood measurement trends” by Tom Haak, which amongst other things, describes three main means of measuring employee mood: traditional surveys, simple daily feedback tools, and passive data mining of employee online communications (emails, Slack, Yammer etc.).
I share my assessment of each method. Furthermore, I discuss why passive communications mining is likely to generate data that is unrepresentative of employee mood. Instead, it is more suited for analyzing supervisor effectiveness, which is a leading indicator and arguably the most important determinant of employee mood (the symptom).
Finally, I opine that analyzing supervisor effectiveness through communications data mining could be combined with traditional employee mood surveys to generate actionable insights to improve overall employee performance.
The DoD investigates uses for data/workforce analytics to improve training efficiency and enhance force readiness.
There's notoriously high human error in medicine, but algorithms can be imperfect, too. How should we handle this?