After all the time you’ve spent diving into the data and progressing towards finding out what’s actually going on, it’s now time for you make a huge mental shift and figure out how you can actually present your findings and conclusions.  

Not only that, but you usually have a very finite amount of time to present, and the audience will mostly likely be non-technical.  

It’s a high-stakes position to be in, regardless of whether it’s your 1st time or 100th.  And as a lot of us have seen, being a great data scientist doesn’t always translate into automatically being an effective presenter.  

If all you want to do is create a file of facts and figures, then cancel the meeting and send in a report. -Seth Godin

The single biggest thing I’ve learned from those situations basically boils down to you have less presenting time than you think – especially if you’re taking questions.  

Generally, no matter how often you’ve practiced and gone through your presentation notes, you probably can count on at least one significant aspect of your presentation being far more technical than you thought – at least from your audience’s perspective.  

And for that one item, it usually ends up being very important for your audience to have at least some semblance of comfort with what you’re talking about, and this usually results in some back and forth discussion.

 

How Helpful (really) Are Your Graphs?

Not only that, but also as you’re presenting you might come to realize that one of your graphs is nowhere near as explanatory as you had initially thought.  You realize on the fly that one of the axes, which makes perfect sense to you, only makes perfect sense because you’re the one who freshly derived the y-axis proprietary variable less than a week ago.  

Maybe to your immediate manager your graph and axes make fluent sense, but to anyone not intimately involved with your line of thought (of why you had to derive that axis in the first place) will now be looking at a graph with little to no actual informational value.

All this to say, almost regardless of how big-picture you think you’re including in your presentation, it’s probably not enough.  At least for me, I’ve found that presentations have gone much more smoothly when I start with the almost basic high-level concepts that I know we’re all familiar with, quickly going through those, and then establishing a very simple link between those concepts and each graph that I’m going to show.  

I’m sure many of us have sat through at least a few painful presentations where the presenter makes a couple high-level remarks about their topic and then starts showing chart after chart after chart – but at most 5% of the audience actually understands what the presenter is talking about.  

 

Speaking Their Language

The sad part is that, with maybe two more minutes of highly-targeted remarks of linking the graphs to concepts the whole audience is familiar with, that 5% number would probably be much more closer to 90%.

At the C-suite level, they don’t really care that you have some fancy graphs and highly technical words.  What they do care about is revenue growth, churn, yield, client retention – the list goes on.  

Just taking the extra minute or two to make sure the audience is able to make the fundamental connection between (a) what they care about and (b) your graphs –  it’s something that might end up having the highest per-minute ROI of your entire data scientist career.