The capabilities that are now available when it comes to People Analytics show great promise and are quite exciting. However, with powerful insights and behavior mapping come a higher need for responsibility and ethical considerations. In your opinion, what are some of the common areas that we need to be looking out for and what are your suggestions around how our organizations can use these tools responsibly?
Hi, Craig! Thanks for your question. I’m going to take the last part of your question and work backwards a bit, and I’ll go right to the point in the interest of time. Tools that capture and analyze people-related data are now commonplace. I use the term “people-related data” because not all data that generated by individuals is consciously known by them. Yes, in most cases they sign a piece of paper or click a terms of use that are rarely read, yet these agreements allow the data to move at the discretion of the enterprise and/or platform owner, often times will little to no communication back to the individual. And, to be real, if communication, emails for example, went back to the individual each time data was captured and analyzed they’d have a few lifetimes of work to just absorb it. This, therefore, is not realistic. So what must be done? Organizational leaders, platform providers, and data generation and analysis firms must have an Ethics Charter or some guiding principles that outlives how they handle people-related data analysis. What are their rules? What are their boundaries? What constitutes a “no-go” decision? How does the analysis help the individuals who generated data? These questions and others must not only be answered, they must be supported by examples over time. This is what happened. This is what we did. This was the result. And, again, this must done again and again over time. In the end, those analyzing people-related data must strive to build trust. Especially now that the collection and analysis of passive behavioral data (“data exhaust”) meant to shed light on how people are actually spending their time. Especially now that the potential to perpetuate or exacerbate bias exists. Especially now that language (NLP) data is being used more widely. All these can be perceived as invasive if not handled proactively, virtuously, and continuously. Hope this helps and, of course, much more to share and explore on this topic.
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