Thanks for sharing—you picked a very interesting article! Trust is an essential characteristic of any high-functioning organization, and I agree that it will be key to realizing the full potential of people analytics. As you have mentioned, achieving trust in the data collection process is difficult. Oftentimes employees do not feel comfortable sharing personal information with the company through surveys or their digital behavior through surveillance mechanisms. During this step, companies must be sure to adhere to strict data privacy standards, as any accidental breach of sensitive employee data would result in a major loss of trust. As you have also mentioned, achieving trust in the implementation stage is challenging. We’ve discussed the importance of transparency a good bit in class—companies must be forthcoming about the technical details behind the people analytics models they are using and the end goals that these models will serve. If companies can achieve trust in these two important aspects of people analytics, I too feel that it can be used to further promote crucial DEI efforts in the workplace. One specific example that comes to mind in terms of using people analytics to improve DEI that we touched on briefly in class is organizational network analysis, a technique that would enable companies to better understand how interactions between employees differ across gender and race.
Hi Mary Helen,
Thanks for sharing—I had never heard of ConnectMe prior to your post! I appreciate your skepticism of the platform—it appears to be well-founded based on the two cons that you have outlined. In regard to your first con, data privacy is definitely a hot topic within the field of data science. I am currently taking a course with two of our other classmates, Nico and Jeff, on differential privacy—a rigorous mathematical definition of privacy that, when satisfied, provides a formal guarantee that individual-level information about the participants included in a dataset is not leaked with statistical releases. There is some very interesting research going on over at Harvard SEAS within this space if you are interested (https://privacytools.seas.harvard.edu/differential-privacy)! In regard to your second con, I too do not see ease of access to HR information as being a major driver of employee engagement. It seems to me that a well-implemented mentorship program would be worth more investment in resources than this HR delivery platform if increased employee engagement is the ultimate goal. Overall, I am on the same side as you—any example of predictive modeling is interesting to me, but perhaps there are more impactful examples than ConnectMe.
Thanks for sharing—I really enjoyed reading your post! As you’ve indicated, there certainly seems to be a line between gathering employee data to perform effective people analytics and implementing creepy worker surveillance systems. I had never heard of the example of individual sensors monitoring employees at The Daily Telegraph, but that seems to be a clear-cut case of a company crossing the line. Your post brought me back to our recent case study on Airbnb, specifically the portion that discusses the use of active versus passive data. Although the use of passive data is sometimes viewed as too intrusive, the benefits of passive data analysis are significant. It does not require employees to spend time filling out long surveys and involves continuous streams of data that can be used to carry out time-series analyses. However, one potential drawback of using passive data is that employees may begin altering their digital behavior if they know that it is being monitored, which would likely lead to less meaningful analysis results. Moving forward it will be interesting to see which approach—active or passive data analysis—becomes the more common practice in people analytics. As you’ve perhaps suggested, passive analysis can be a viable route as long as companies implement it in a manner that is not overly intrusive.