LPA Blog Assignment

April 06, 2020

Read The Full Prompt

Organizations, from startups to multinationals to non-profits to sports teams, are using employee data in new and exciting ways. They are typically trying to improve efficiency, performance, or both, leading to a variety of intended and unintended consequences. Using employee data in new ways often has important implications for employee privacy, company transparency, ethics, and organizational culture.

Your assignment is to identify an article, blog post, or other public source of information about an issue related to People Analytics. This could be a description of an organization’s use of employee data in a particular way, commentary about the field as a whole, an opinion on a particular practice related to People Analytics, or anything else that is relevant to the field. For examples of articles related to the course, visit the PeopleAnalytics@Harvard website.

Write your own reaction to your chosen article in the form of a short blog post. What is your point of view about the issues in question? Are you a proponent? A skeptic? Does your article describe a particularly effective use of data that should be emulated by other organizations? Or do you think it describes an ineffective or troublesome practice? You should go beyond simply describing what is in the article and articulate your own point of view on the topic.

Create an approximately 600-word post to make your contribution on d3.harvard.edu by 6pm on Monday, April 6. Please include a link to the material you write about. Feel free to use graphics, data, videos, and links to other sites to corroborate your points.

After posting your own blog, please comment on three posts by other students by 6pm on Tuesday, April 14. Selected posts will be discussed in class.

If you have any problems with the digit.hbs.org platform, please email diplatform@hbs.edu for help, and visit the digital platform resources page for helpful advice.

Submitted (57)

Trusted Partners or the Machine?
Toni Campbell
Last modified on April 15, 2020 at 6:57 pm
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 [...]
Googlegeist – An inconvenient truth
Viet Nguyen
Last modified on April 14, 2020 at 12:56 am
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?
What Happens When “MoreyBall” Encountered Repeated Games
Guo Chen
Last modified on April 13, 2020 at 11:51 pm
MoreyBall analytics works for Houston Rockets and changes the way the NBA is played. However, does it really help them in the playoffs?
People Analytics In Compensation
Posted on April 14, 2020 at 12:40 am
Compensation Analytics: the opportunities and challenges of using data to determine pay
People Analytics: A Service vs. Platform-Driven Model
Jason Brown
Posted on April 14, 2020 at 2:34 am
How should companies structure their people analytics team? A thought-provoking product-centric model for building people analytics talent.
Video Interviews with AI: Better for gender equality? Or worst?
Posted on April 14, 2020 at 4:32 am
Using AI to asses interviews / The pros and cons.
A Trail of “Bread-Crumbs” Leading to Organizational Insights.
Posted on April 14, 2020 at 8:35 am
How email and calendar paper-trail is benefiting decision making around people analytics? What is being left out?
Do your employees laugh at your jokes? ?
Last modified on April 15, 2020 at 12:44 pm
How do you track if your employees think you're funny? And should you even be tracking that?
Closing the gap between People Analytics and Workforce Development
DSR Section I
Last modified on April 16, 2020 at 10:17 am
There’s an interesting phenomenon where adjacent fields or industries adopt their own vocabularies and ideas around solving essentially the same problems. A simple example is econometrics and data science – listening to an economist and computer scientist speak about these [...]