Tom Brash

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Narrative risks and the curse of the “10%” answer
Tom Brash
Posted on April 7, 2021 at 4:35 pm
Storylines and narratives are powerful tools to convey analytics results. However, they also risk analysts and audiences fixating on conclusions too early and disregarding new information to the contrary.

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On April 13, 2021, Tom Brash commented on Nestlé: People Analytics and Gender Equity :

Thanks for sharing, this is really insightful! I think the idea of using it for this kind of measurement and visualization helps avoid the risks that often come with using people analytics for modelling etc., and illustrate how much can be done just by making the data available to people in tangible, easily understood ways.

Also totally agree on the point of visualization and transparency to help combat this issue. Giving people the data not only helps the issue be understood, but also empowers people to have challenging conversations about salary without relying on anecdotal or private evidence.

On April 13, 2021, Tom Brash commented on Debugging the Pulse Check Survey :

Really enjoyed this – I also agree that they can still be valuable and have a place, but that they can absolutely be a box checking exercise that doesn’t add any value if not managed actively! Inclined to agree with Dio that there needs to be a balance between open and transparent individual results, but also some form of providing anonymous feedback in case there isn’t a culture that inspires people to be open.

One thing we found helpful in pulse checks was to get rid of the standard 1-10 scales for questions like “how are you feeling”, which started to feel like a box check. We got 15 images off the internet for each pulse check (without an obvious best/worst) and asked people which one they felt like and why, which seemed to provoke more honest, in depth answers

On April 13, 2021, Tom Brash commented on Keeping Performance Reviews Honest: Lessons from the Marines :

Incredibly interesting that NLP can effectively identify the coded language that corresponds to different levels of ‘real’ performance. I guess the only risk here is whether sharing that data and nudging people in specific directions contaminates the raw data, making the analysis itself less helpful (and eventually even restrictive of which words should belong in which category of review).

I don’t have an answer, but I think how you use the data and stop it being gamed is a consistently interesting challenge with this kind of analytics!