Hi ,

​

"Spend less time with (your) kids. They just waste your time."

Last week, I asked readers if they would follow this advice, if provided by a trusted advisor.

Reassuringly, no one said "yes". However, respondents were split approximately evenly between those who would quietly ignore the advice and those who would ask further questions to clarify.

Some comments I received as feedback (all quoted with permission) include:

• "4. Find a different advisor. Actually I’d probably also replace ‘quietly’ with ‘loudly’ in option 2" - Chris Dolman, Data and Algorithmic Ethics Expert

• "I choose c [Ask questions to clarify] as there’s got to be some nuance to make such a draconian statement. Would like to hear the other side’s argument fully threshed out first before rendering a decision of agreeing or disagreeing. Maybe I won’t show that I disagree but I’d like to hear the other side." - Leo Lim, Domain Architect

• "(c)[Ask questions to clarify]... but I would also point out that if this is how they really perceive my time spent with my children, then it's obvious we don't value the same things. It would be time to move forward with an advisor that has similar values." - Eric Carroll, Visual Notion Founder

All of the above indicating that advisors who make seemingly insane suggestions are likely to find themselves our of a job.

With this in mind, watch the following short video (originally brought to my attention by friend-of-the-list Rod Aparicio):

Here's the thing...

As everyone who's ever played "Broken Telephone"/"Chinese Whispers" knows, what you're told and what you hear are often two completely different things.

This may seem hilarious when you're a kid, but it can lead to all sorts of problems in a business setting.

To non-technical people, data talk can be harder to understand than a challenging accent. And as the above results show, you can't always assume people will clarify the advice they're given - even if what they hear may seem absurd.

It's the responsibility of the data scientist to ensure any advice they provide is accurately received.

Data scientists constantly argue over which is the most important language for data science - R or Python. 

But when it comes down to it, there's only one language that truly matters - the language you use for speaking to stakeholders.

Talk again soon and spend less time with d*ckheads,

Dr Genevieve Hayes.

All past emails are available in the archive.