Welcome to the Analytics Power Hour. Analytics topics covered conversationally and sometimes with explicit language. Hi everyone. Welcome to the Analytics Power Hour. This is episode number 260 and I want you to sit back, get comfortable by the fire, snuggle under a cozy blanket so I can tell you a tale. It starts back in ancient Mesopotamia over 4,000 years ago with the princess and priestess Enheduanna, who is often considered
the first known author. For the purposes of this introduFction, we'll say she is one of the first known and named storytellers while Enheduanna pinned her stories in Kannada form, the modern analyst uses digital technology, slides with words and images and data visualizations to craft data stories. And that's the topic of this episode. What the heck are data stories? What are they not? Why do they matter? And what are some of their dos and don'ts? I'm joined for
this particular podcast Narrative by Julie Hoyer from Further. Julie, what's one of your daughter's favorite stories at the moment? Hi there. She is really into If Animals Kissed Good night. Aw, that sounds really sweet. Aw, isn't that cute? It's really cute. And I'm also joined by Moe Kiss from Canva, a company that as stories go, actually is a unicorn. I think
that's right. Right? Sure. It's your unicorn company. Yeah. But it also provides a platform that can help analysts deliver impactful data stories. Moe what's a popular story with the Kiss Kids these days? Actually, we are really into AIs for analytics at the moment. It feels very fitting. Oh. Aw. Shout out to Jason Thompson and Hela. And I'm Tim Wilson from Facts and Feelings. I'm also the co author of
The Right Way. A business leader's guide to putting data to productive use, which is a non fiction narrative available for pre order now from Amazon, Barnes and Noble, Target and more. Apparently not in Australia, though. My kids are all well over a decade past relying on me to read stories to them, but I do have a nephew who will be getting a copy of Mo Willems' Don't Let the Pigeon Drive the Sleigh for Christmas. So we're excited about that.
But for today's episode, we wanted to get someone who's put a lot of thought into this topic. Duncan Clark is currently the CEO and Co founder of Flourish, and he's also the head of Europe at Canva, the latter of which is a position he took on when Flourish was acquired by Canva in 2023. Earlier in his career, Duncan was literally a storyteller in that he is a published author and among other storytelling roles, spent time as a data journalist at The Guardian. And today he
is our guest. So welcome to the show, Duncan. Thank you for having me. Great to be here. All right. So I think maybe a good place to kick things off is to actually nail down a good definition of data storytelling. Maybe, that may be the entire episode and we will get into, we'll come to blows on it. So we'll start with Duncan, if someone asks you to like, explain what data storytelling actually is, like what do you tell them? Well, I guess fundamentally data storytelling
is about using data to communicate something. And that's quite different from using data to understand something. It's the difference you might say between what sometimes gets called in the data viz world, explore versus explain. If you're explaining something, you're communicating something, you are articulating an idea and in some sense, therefore you are telling a story. But beyond that, I think it's one of those phrases that
people do use in very different ways. I mean, there are people like John Burn Murdoch who talk about storytelling being very much about how you use text in a chart and making sure a self contained chart can articulate what it's trying to say without supporting words. But there are, the way that we at flourish and before that kiln I've been thinking about data storytelling is really
a little bit more like a traditional concept of narrative. Like a traditional story has a start, a middle and an end. It goes through an arc and so it progresses through time. And I guess what I've been working on for quite a long time is visualization that can do that, that can transition through time to actually tell a story With a start, a middle and an end. How much of it do you think is the, as you mentioned, the visualization and how much is it the narrative that
goes with it? Or is it just like, it's a bit of a dance and it really depends on the particular data story that you're telling? I think it's fundamentally about the narrative and the visualization is a really crucial part of how you tell the story, how you tell the narrative. It's the reason that you can articulate a lot of information in a very succinct way. It's how you can make something visually interesting. It's how you can make something
that doesn't need you to justify every point you're making. 'Cause it's justified in the visualization. But I think ultimately, if you're trying to tell a story but you don't have a message, then however good your visualizations are, what you've really made is something almost a bit more like a dashboard. It's a collection of charts. So the communicate versus
understand. I love that. And the going to the narrative. Would you then say that like if data storytelling is about communication and at the core of doing that communication, you need the narrative that really you should always be figuring out the narrative first and then the data visualization is just one piece that gets dropped in along the narrative as opposed to... Well. I would put it, but in a way it's the other way around in as far as the narrative has to come
from the data and how do you understand the data? Well, you do that visually. So it's always a bit circular and a bit iterative. And I think data visualization often starts with let's visualize something just to see what this data is. Okay, let's change the visualization to understand it. And then once you've understood it and you've sort of picked it apart in different ways, it's at that point where you start thinking, okay, I've actually understood
what's going on here. I need to be able to articulate that otherwise, 'cause you can do all the data analysis in the world, but unless you can explain why it's relevant and get something changed as a result, then it's an academic exercise. So for the data storytelling bit is that bit that comes at the
end of that circle. Maybe there is no such thing as the end of a circle, but something that comes, you've got that slightly circular process of visualizing for understanding the common visualizing for articulation explanation. So the narrative layer has to come out of that. But it's almost like if you've got the story clear, you can actually tell the story without the visualizations. It's possible. Whereas if you just throw the visualizations that people, they're not gonna understand
what you are trying to get across. So it's really a unity of them both that's required. If you throw a visualization at them, you're expecting them to then figure out the story. I think like that feels like the big miss. If I am trying to understand and I put all the understanding in front of you, it's who's taking on the burden of figuring out what it actually means. Exactly. Yeah. And you could almost
see it as a spectrum, right? I mean, in theory you could just dump the raw data in front of them and of course, no one would expect them to be able to understand what happens. In theory, no that happens in practice. And it's a problem. So. No,
you're totally right. I mean you see those Excel files that are circulated and people have drawn a cover page on them, almost like it's a presentation and you put a big white box and put the title, and then you go to page two and it's just loads of numbers. And so that's the kind of dump the data and expect them to
do not just the interpretation, but the sort of analysis. Then there's the version where you've pulled out a few charts that make the data easier to digest, but you've still not explained what's interesting about it. Then there's the version where you get your charts to be sufficiently good at articulating their own message. And this is what I mean, go back to say John Burn Murdoch from the Financial Times.
He's a brilliant data journalist. For him it's always very much around how do you make a chart a self contained piece of, almost like an encapsulated piece of content where the title is an absolutely key surface area where it's kind of, this is explaining what the chart is saying. The annotation is then like the glue that binds the user's attention between the title and the supporting evidence in the chart. And so that's like the next level up where you've made charts that almost you could
drop in front of people and they'll get them. And that's why, apart from him being a brilliant analyst, it's why John Burn Murdoch's charts often go really viral on social because they tell a story in an encapsulated way. But then there's the version above that where you actually sequence charts together and you construct a narrative. And actually to continue with the John Burn Murdoch example, what he does brilliantly on social is he actually strings a bunch of charts together with tweets
and tells a story. And it's kind of, each chart is a self encapsulated piece of information design. And it's a kind of scene in a story, but actually it's when you string them together and draw a conclusion and tell the narrative that it becomes really, really powerful. It's time to step away from the show for a quick word about Piwik PRO. Tim, tell us about it. Well, Piwik PRO has really exploded
in popularity and keeps adding new functionality. They sure have, they've got an easy to use interface, a full set of features with capabilities like custom reports, enhanced e commerce tracking and a customer data platform. We love running Piwik PRO's free plan on the podcast website, but they also have a paid plan that adds scale and some additional features. Yeah, head over to Piwik.Pro and check them out for yourself. You can get started with their free plan. That's Piwik.Pro. And now let's
get back to the show. I feel like we're gonna get into this explain versus Explore concept a lot and we're definitely gonna focus on the explain side. I've never heard it framed that way and I feel like I've had like a light bulb go off in my head because I have honestly, like Tim and I have both thought about this topic quite a lot to be honest, because it's something we are really passionate
about. When you think of the explore category though, like is it just like dashboards that comes to mind or analysis or are there like other areas that maybe I'm not considering that also fall under that. Well, I think it's a really good question. So I think, the archetypal example of just explore, I think is the dashboard. You've got some filters, you've got a bunch of visual representations of the data and you sort of explore that way. But I do think there's one
in the middle actually. So one of the things, I mean, just to tell a bit of prehistory about where Flourish came from. So I was a data journalist at The Guardian and that obviously is very much, it's all about the story. Like what are you trying to explain. How do you get the user to care about it? And coming out of that, I co founded a little company called Kiln, which in the early days was just doing, it was really bespoke visualizations to order. It wasn't a tool
at that point. We were kind of experimenting with how you tell a story with interactive content. That was really what it came down to. And so to answer your question, like what we found ourselves doing is we would often make a chart. Let's say you've got a scatter plot and you're exploring the correlation between two things, but that scatter plot might also have a time slider. So the things are moving, hands rustling style over time, but you might also have a
filter. And so in a way that's a dashboard. It's a chart with a bunch of controls. It's very interactive. You can use it to explore that data set. Every data point's available. You can move things through time. You've probably got animation as you change the filters, which will help understand the relationship between the two views.
But what you would generally find, let's say you've got 50 slider positions for the 50 years you're looking over and then you've got four different categories and you've got four different color schemes or whatever. That becomes quite a rich powerful, it's like a machine with lots of knobs. And the analogy that we used to use when we were working
on this stuff was a play a piano. And you've got a piano where you can play all the notes, but you can also feed in a piece of paper and it will play the notes for you so that it's a playable instrument, but it can also play itself. And that's where we got to with Kiln is we would make things 196 00:12:59,800 --> 00:13:0