Hi Hanne, I agree with your blog post. We should not be blinded by data but we need to be educated in applied data science. One of the major dangers is to have an increased data-driven culture but not an increased data-educated population. The problem is that data science is inherently complex to learn and requires mathematical intelligence, at least in its purest form. Therefore I think LPA was extremely valuable, allowing us to gain insights into data science (including risks and opportunities) without being too theoretically too advanced.
Totally agree, there are many caveats in using people analytics the way I described it. How to use the data and how to interpret it will be a big part of the challenge and the debate! Nevertheless, I do believe there could be a consensus on how to use people analytics in clinical medicine. But the debate has, to my knowledge, not been introduced yet…
Totally agree, Sam. Big data, machine learning, and AI somehow became buzzwords and everyone wants to use them but there are caveats to using data. I almost want to say that high school programs have to be gradually adapted. They should start educating their students (and future workforce) in applied data science, even the less mathematically gifted students. We should educate our students on how to use I, how to interpret it, and the risks of using data.
Interesting idea, Mannix. I would also describe the “great resignation” as a cultural shift where less importance is accorded to employer loyalty, I am not sure if tracking employer engagement would actually change the culture shift but it could theoretically make a difference on an individual level, depending on the industry and country.
As mentioned above, some countries or cultures might be more reluctant to data-tracking involved with tools as you described.