Wes Kao, Maven co-founder, explains why data professionals struggle to communicate findings. Raw numbers don't sell themselves; you need a "so what" narrative. He shares two frameworks: concentric circles to tailor context by audience, and altitude to balance detail versus abstraction.
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There's this common thing where it's supposed to take 10,000 hours to become an expert, but I've been working for several decades now. Well, more than the 10,000 hours. But I don't feel like an expert at my career. So why is it so hard to get career skills?
Ooh, that's a banger to start with. Yep. I think it's actually because career skills are a combination of a bunch of separate skills. It's not just one skill. Being good at your career involves being great at execution, thinking about strategy, being a good communicator, project management, dealing with conflict negotiating. There's so many small negotiations that happen on a daily or weekly basis at work. And so managing up, managing down, managing expectations, pushing back, being able to advocate for your ideas in a way that's effective and productive. So there's so much nuance that goes into every one of those things, which is why I think it's a lifelong endeavor to get good at work, essentially.
Okay, yeah, that makes me feel better. There's so many different possible skills that you could learn, so many different contexts. So there's a lot to talk about. So we're probably getting to them all now. Otherwise it's going to be a 24 hour podcast. But maybe we'll start with something I think a lot of data people struggle with, just like talking to other people, so we go with communication. So maybe communicating technical ideas is one of the most common things data people have to do and they struggle with. So do you have any advice for this?
I think that data people tend to be logical and rational and they like numbers. And I think when you have this mindset, it's easy to assume that the numbers will speak for themselves, the data will speak for itself. In my experience, very few things actually speak for themselves. And even if they could, your audience would probably appreciate a bit of narrative around what is your interpretation of what these numbers mean? So I think the biggest thing for data folks is to not just offer a fact or data point as a standalone thing, but to offer your interpretation around what does this mean? What's the so what there? How can you help this make sense for your audience?
Okay, yeah, I like the idea of you can't just spout a load of numbers and expect people to care. You've got to give them the reason for why something is important.
Oh, I was going to say, I think the other thing is that very few things are very obviously good or obviously bad. So in our own minds, with full context, we might think, oh, I mean, this is clearly bad or this is clearly good. But when you share something with someone else, it's pretty common that they're not sure how to react. Right. Like most of us have had people say, wait, is that good or bad? You know, and I've definitely said that, you know, to folks who shared something with me that I could tell in their eyes was so obvious, but for me it wasn't. And so offering that interpretation of, you know, here's what this means, here's why it matters, here's how it relates to the work that we're talking about. That all helps to facilitate meaning, which helps to create a more thoughtful conversation and discussion that leads to eventually a better decision.
Yeah, that makes a lot of sense. And I suppose, I think that buying or selling stocks, there's only two possible things you want to do, like, this is a good thing, this is a bad thing. And trying to reduce a complex situation down to those two states, this is good or this is bad, it's going to help your audience. Okay, so one of the sort of common bits of advice for communication is you've got to tailor what you say to your audience. Do you have any advice on how to go about doing that?
Yes. So I have a couple frameworks here. One is, if you think about concentric circles, you are in the bullseye of the concentric circle. You have full context about all of your work. Anyone outside of your own head is in the next rung. So these are people who you work with on a daily basis. They might be fellow data analysts, data scientists, other people who do something similar, maybe for a different team, and then on the next rung might be your manager. Depending on how closely they are in the data world, some managers are more hands on, others are a little bit further away. And then in the outer rungs, you might have cross functional partners, you might have product managers, marketers that you work with, ops people, and then you might have leadership team members who have even less insight and visibility into the day to day workings of what your work might entail. And then if you go even in the very further outer rungs, you have people who are outside your company. So if you're talking to a journalist about a product launch, or if you're on a podcast, whatever, how do you tailor what you're saying to those different audiences? So I like thinking about the concentric circles because it helped remind me that people can't read my mind and they are not immersed in the world that I'm immersed in. And so it's up to me, to share context and to educate my audience on the things that I need them to know in order to be able to have whatever conversation that we're having. So that's one is the concentric circles. The other is to think about the altitude that would make the most sense when answering a question. So there's altitude that's too high. If you speak at too high of an altitude, you might be too vague, too high level. You're not giving people enough to chew on or enough detail. But if you swing the pendulum and you're five inches above the ground, then you're too low altitude. So that might be describing the step by step process of how you did something behind the scenes. So you want to be somewhere in between those two. And for an executive audience, for example, you want to give them enough detail for them to be able to chime in and be able to understand what the situation was, what the problem was. But not so much nitty gritty detail that you're getting lost in the weeds and they're not sure what the plot line really is anymore.
That last point is very interesting. Trying to talk about the altitude. And do you need to start at high altitude and go lower or is it the other way around?
So I have an example here. So let's say that there is a project delay and you're explaining in, you know, an all hands meeting why, why this big project was delayed. And, and one of the execs asks, you know, was the delay our fault or the vendor's fault? So an answer that is too high altitude would be something like it was both of our fault. Okay. And then just ending there something that is too low altitude would be getting to the nitty gritty of. Well, in January we started this project. By March we knew we were a bit delayed. Then in June we re scoped the project. And then later that month the vendor also missed a deadline. And so really it was a bit of their fault and a bit of our fault. Right. And then going into a lot more detail there. Right. So that would be too low altitude. And something that is, I would say a right amount of altitude would be to say it was probably a bit of both of our faults. The vendor missed a deadline, which pushed the timing back, but we also expanded the scope, which meant that we needed to add a couple more weeks to be able to finish the project.
