Data science and the art of listening to your clients

Note: This article isn’t just about data science consulting – I use the terms ‘client’ and ‘end-users’ interchangeably, as in either case someone is entrusting you to find insights in their data.

One trend that I’ve started noticing recently is that you can roughly divide up data scientists into two categories: those who really, really care about what their client wants – and those who don’t.

As you can probably guess by my tone, I think it’s pretty important to listen to your clients – and apparently, way more important than many data scientists seem to think it is.


Data scientists are getting caught up in their own hype

I think we’re at peak hype for data science.  Not that the hype can’t increase from here; more that it’s probably never been higher.

We’re seeing all-time highs in big promises, rosy predictions, fancy buzzwords, substanceless (but fancy) graphs, self-congratulatory conferences, frothy startup & VC activity in the data science space…

Is this hype legit?  I don’t know.  What I do know is that it seems many more people are starting to think that data science will magically solve all their problems.  And if you happen to be a data scientist – well then you’re God’s gift to business!

The problem is, when you think you’re awesome, you stop listening to feedback and constructive criticism.  After all, you’re already pretty much perfect – why should you make a change?

Of course, no data scientist would say they think like this.  However, if you look at how some of them act, sometimes blatantly ignoring feedback and suggestions from clients…it seems they don’t really care what the client has to say.


Data scientists are starting to listen less to their users

It’s not that the data scientists don’t care what the user has to say.  It’s that, data scientists seem to be becoming increasingly likely to brush off a user’s concern as “yeah, well they just don’t understand.”

With all the cool new analytics tools and methods available to data scientists today, it’s easy for them (and especially me) to get caught up in all the flash – and lose sight of what actually matters to the client.

What users are not asking for: fancier graphs, flashier ML algos, bigger buzzwords.

What users are asking for: help finding insights in their data.


You’re probably not spending enough time listening to your users

This will sneak up on you – as it has for me, multiple times.  Most of us would like to think that we do a good job listening to users.

It can show up in multiple subtle ways, at least initially: brushing off a user’s concern that about a data issue as “just an edge case,” leaving errors in a graph because “they can still understand it without me fixing it,” not taking the time to sanity check output because “they’ll still get the big picture.”

And then, one day, you get taken off the project – quietly.  You’ve been replaced by somebody who does actually care about the “little things” – because when you add them up, they start to be really big things.

Some of us get so caught up in our own hype, we’re forgetting why users were asking us to do this work in the first place.


A note for newer data scientists

Especially for newer data scientists, it can be hard to appreciate these points.  It’s not that they would say “don’t listen to your users.”  Instead, it’s more like they’ve never really been in a situation where they might have to distinguish between their own personal preferences – vs the client’s.

Many newer data scientists have simply never had a client.  Even if they’re done a ton of analytics on their own, the whole time they were essentially their own client.

Once you have to start keeping a client happy, it’s a completely different ballgame.  They don’t get to see all the sweet analytics work you’re doing in the background to try to find them insights – all they see is the end product.  And if your end product isn’t that great, it doesn’t really matter the effort you put into it.



Many people, including myself, would like to believe that all the little “imperfections” in our analysis aren’t a big deal – and often, they aren’t.

However, user’s look at this stuff in a completely different way than we do – and eventually, the extra mental willpower needed for a user to power through these cumulative “little” issues…can just be too much for them.  And you get taken off the project.


The views expressed on this site are my own and do not represent the views of any current or former employer or client. 

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