Thomas: G’day. Welcome to episode 13 of season two of the Presentation Boss podcast. Very nice to have you here with us. Hi, I am Thomas Kate. Uh, hello. How are you?

Kate: Hello. I’m good. I’m excited. I have news from the world.

Thomas: Oh yeah,

Kate: it’s an Excel update.

Thomas: Oh, it’s an Excel update. Okay, great. You’ve got like 15 seconds.

Kate: Okay. This is, this is an update that we needed 25 years ago. Mm-hmm. And if you use Excel, I know that you will appreciate this.

Thomas: Great.

Kate: The function, merge and centre. Mm-hmm. Do you know what I’m talking about? Yeah. Yeah. You select a few, it merges the cell and puts the text in the middle.

Thomas: Yep. Familiar?

Kate: Yep. Uh, the problem with it is it does mock up.

Your formulas can be an issue with formatting, all of that kind of thing. Generally it’s a pain to use, but it does look nice when you use it.

Thomas: Mm-hmm.

Kate: Now you can, in the same dropdown, do centre over selection.

Thomas: Ooh. Yep.

Kate: You used to be able to do that, but it was hidden a bit more of a, yeah, it was a bit more hidden.

You had to go into the formatting, um, menu and everything, but now it is there on your quick access toolbar. There you go.

Thomas: Amazing.

Kate: Are you mind blown?

Thomas: No. No. I think because strangely, I use Google Sheets probably as much as I use Excel. Okay. And it, I, I’m pretty sure it preserves the formula in those merge cells, so.

Kate: Hmm.

Thomas: It’s nice that she, uh, sorry, Excel now does that as well.

Kate: Yeah.

Thomas: Okay. Cool. Well, let’s kick on with the episode today. We’re talking a little bit more about the best practise when it comes to data visualisation and that scenario where you’ve looked at a data set, you’ve been playing with it for some time, you know exactly what it means.

You’ve made a chart, very nice chart, and you’re presenting it to stakeholders, decision makers, and you are thinking to yourself, it’s pretty obvious, it’s pretty good. And then they just don’t get it.

Kate: Yep. And before we kick on, I want to maybe explore this concept of they just don’t get it because what does that look like?

Thomas: Yep.

Kate: Because most people will not say, I don’t get it. I

Thomas: don’t get it. No, no, they don’t.

Kate: So what do they say? What does that actually look like? Um, what would you say it looks like? Like, have you seen this? Yeah.

Thomas: Yeah. I mean, you get it a few ways, right? And often this is a comment we get from managers, directors when we’re coming in to work with a team is they say, like, I just don’t kind of get what they’re saying.

And, um, I think what we do see, I remember you have the story about, um, if you are sitting in a presentation and the audience says, “Mm, thanks, that was interesting”. Interesting is not a, not a compliment.

Kate: Yeah. Yeah.

Thomas: Or if they may be kind of nodding along and then they just don’t have any questions at the end, they kind of just, yeah, okay, thanks.

And then nothing happens. It can be a longer term

Kate: thing, especially if you thought that there was going to be questions or you expected questions about certain things. Oh, yeah. And they don’t ask those questions. It can be an indication that they’re just not tracking with you.

Thomas: You get questions about like, weird stuff.

Kate: Yes. Or you’d get other questions. Yep.

Thomas: Yeah. So that’s not quite what we were talking about where we sitting in the same room here.

Kate: Yeah,

Thomas: yeah, yeah.

Kate: Yeah. So that’s what it can look like. And it’s not, yeah. It’s very rarely someone will say, I don’t get it.

Thomas: They will basically never say, I don’t get what you’re talking about.

Kate: Yeah.

Thomas: Yeah. Which is fair enough. So look, you know, have you ever presented a chart that seems perfectly clear to you only to discover your audience couldn’t understand it, or maybe on the reverse, which is, have you ever been on the receiving end of someone presenting some data visual or like they’re reporting to you and maybe you get that comment from them, which is, “as you can clearly see”, or “obviously is what it says”, and you just, you don’t, you can’t see it.

Kate: Mm.

Thomas: So how do we turn a chart, nice, neat chart that we’ve got that makes excellent sense to us into one that makes sense to everyone else?

Kate: In our last discussion episode, we started to look at this process and we started with choosing your chart and making sure that you’ve got the right chart type. And this is the question that we often get is like, what chart should I use?

Thomas: Mm.

Kate: And people think that that is kind of the end

Thomas: Yeah.

Kate: Of, of the decisions that need to be made is which chart, but there is a whole lot more to it. So when we look at the chart type, we talked about the difference between explore charts that are for us for exploring data and explain charts which are used to explain the data.

Um, and so even, even though you may have gotten that bit, right, yeah. There’s a whole lot more that we can do to our chart to make sure that it is fitting the criteria of a good chart.

Thomas: Yeah.

Kate: And again, let’s just quickly recap what is the criteria for a good chart?

Thomas: Yeah, yeah. What makes a good chart? Three criteria fast needs to be understood in as little time as possible by the intended audience.

Number two, easy demands as little cognitive effort as possible. It should be as well easy as possible to understand what’s going on there to read it. And most importantly, arguably unmistakable. It should be really difficult for an audience to walk away having mistaken what the key message is and get something out of that chart that you did not intend for them to get.

Kate: Yeah.

Thomas: That’s it. Three criteria.

Kate: Yeah. Alright, so, uh, we said also that the process that we use, which is more of a concept than a process, but it has four steps to it.

Thomas: Yep.

Kate: There are four things that you need to go through to make sure that your chart is fast, easy, and unmistakable. Mm-hmm. And we’ve done the first one, choose your chart.

So today we are going to do the rest of them because choose your chart is probably the biggest one.

Thomas: It’s Yeah, the biggest, certainly the most, yeah. Uh, graphic visual one.

Kate: Yeah,

Thomas: yeah, yeah.

Kate: But we’ll get through the rest of the, uh, steps today.

Thomas: Yeah.

Kate: Keep it in mind. We are not trying to create a prettier chart. We are trying to make it easy, fast, and unmistakable for our audience so they understand what it means, why it matters, um, without them needing to do their own analysis of your chart.

Thomas: Yeah.

Kate: And so the first place that we can go wrong is asking our audience to sift through so much clutter chart, um, clutter chart junk.

Thomas: Hmm. Let’s fix that. Because we spend an enormous amount of time looking at data, our data sets and our graphing as anybody working with data. We just get quite used to it. Whether we’ve had training in it or we’re just, yeah, like I said, used to fiddling around with data and we get really quite comfortable with complexity and understanding what on the chart matters and what doesn’t.

If we say, got a default chart in, in front of us, and we can just forget that other people don’t, they don’t know how to do that. Mm-hmm. And so the second step that we’re the first step for this episode, but the second step after choose your chart is to declutter. To declutter. A cluttered chart will probably look quite reasonable and sensible to us because we’ve spent time with it and we know exactly what to look at, and we kind of already filter out what doesn’t matter.

Everything that’s on a chart, everything from the bars, the lines, the grid lines, the axes, the title, whatever it is, all of that takes cognitive effort to process, whether it is pushing the message of our chart forward, or if it’s distracting from the message, we have to look at it and think, does this help me to understand what’s going on?

Or is this something that’s distracting and like I need to filter it out effectively?

Kate: So that really feeds into those first two points about it’s gonna be fast and it’s gonna be easy. Yeah. We need to help people read it quickly and read it easily.

Thomas: Yeah. Yeah.

Kate: Mm.

Thomas: Let’s try and visualise a bit of an example on this one. And we’re gonna, uh, pretend that we’re looking at a column graph. A column graph. And it is the sales performance of 12 different salespeople. So down the X axis, we’ve got people’s names alphabetically arranged, and up the y axis is the total dollars sales amount for the period.

And like I said, it’s arranged alphabetically. And what you do is you start looking at this chart, and I’m curious, Kate, what is the first thing you do when you’re looking at this very basic chart?

Kate: I look for the biggest number. Who’s winning? I wanna say who’s winning.

Thomas: Winning. Yeah, exactly. You start mentally sorting them.

You start looking like, oh, who’s the best? And like, they’re three quarters of the way along the sort of X axis, but somebody over really near the beginning is maybe second. You can see how this is slow and takes a lot of cognitive effort.

Kate: And I think, so the answer here is obviously to sort it into some sort of meaningful way.

And I think this, to me, this is so obvious. This is something that I have always done and would always do. And often when we’re talking to people, some people are like, yeah, obviously you’d sort it. And then some people, it just doesn’t occur to them.

Thomas: Mm-hmm.

Kate: Like it, it just, they have no issue with it being in whatever random order.

Usually it is in alphabetical order.

Thomas: Alphabetical. Yeah. And alphabetical does not tell a story.

Kate: Yeah. Yeah.

Thomas: Mm-hmm. Alright, so why do we end up leaving unnecessary detail on charts? And I mentioned a few of the extra bits of what we would call chart junk on charts. Why do we leave that on there?

Kate: Because when you take something away, it feels like you are going backwards.

Like you are removing, um, understanding like you’re removing context. It’s uncomfortable to take things off.

Thomas: Yeah. And I think what happens is we can click the insert chart button on Excel Power bi Tableau, and it gives us a chart and it’s got the stuff on there, the grid lines and the legend, and it’s sorted.

However the data was sorted, we look at it and go, oh yeah, that makes sense. Because it’s just kind of given to us and we know that maybe I don’t have to look at the legend particularly strongly. Like it just, it makes sense to us, which is back at the, the core of the problem, which we’re just very used to that

Kate: we’re used to the defaults.

Yeah.

Thomas: Mm. Okay. Let’s get just a little bit practical. What are the sort of the high level broad stroke tips that we talk about when it comes to decluttering a chart? The first one we’ve already talked about sort the data meaningfully. It might not be biggest as a smallest, it might be chronological, it might be something Yeah.

But sort it meaningfully for the message you’re trying to communicate.

Kate: Yep. And then I would say remove as much as you possibly can. Mm-hmm. And get really honest with yourself. Do you need grid lines?

Thomas: Yep.

Kate: Do you need your legend?

Thomas: Yep.

Kate: Do you need um,

Thomas: axis labels?

Kate: Axis labels?

Thomas: Yeah. Yeah. For the big one?

Kate: Yep. Or little bits on your axis.

Thomas: A background. A background on charts.

Kate: Background?

Thomas: Mm-hmm.

Kate: Yep. Oh my God. If you’ve got a gradient, just throw the whole thing out, man. I hate a gradient Background.

Thomas: Gradient background Or a 3D exploded pie chart, which is worse. Oh

Kate: god. Don’t make me choose.

Thomas: And then I wanna talk about the last step that we encourage because it fee, this one feels like a backward step.

Kate: It does.

Thomas: When everything is all a nice bright colour or the like default excel blue, or there’s the blue and the orange and the like, and everything’s highlighted and nothing is highlighted and it’s really hard to tell what’s going on. So all of the colour on a chart make it grey like a 30% grey. Or if you have a company, grey, make it grey.

What it does, it pushes all that data kind of to the background as context and we’ll, we’ll do stuff from there, but

Kate: it gives us a canvas to work with.

Thomas: Yeah. Does

Kate: like a blank canvas. Yep.

Thomas: Yeah. Yeah. And sometimes your audience genuinely might need one of the things we’ve just mentioned, but what we’re doing is we’re getting rid of stuff so that maybe later on we can make an informed decision of like, you know what?

I do need that axis label.

Kate: Like keeping stuff intentionally.

Thomas: Yeah. Rather than it’s all there just in case, Excel put it there. Yeah. We don’t want to increase their workload. Yeah. But what about, and I’ve seen this happen, Kate, , where somebody says, ah, but my boss, my audience keep asking for putting those things on there that are now missing.

Kate: Mm. And I think this is the reality of working with stakeholders and trying to go to what I would call a perfect chart.

Like you’re looking for best practise. Yep. Um, but the point is, can you get one step closer?

Thomas: Mm-hmm.

Kate: Can you remove one or two things, even if it’s not absolutely everything possible? What can you remove?

Thomas: Mm. The gradients, hopefully.

Kate: Yeah. If they absolutely need a legend Sure. Keep the legend. If they absolutely need, um, axes lines, keep them.

Thomas: Mm.

Kate: There’s, there’s no like hard and fast rule, but the idea is how much can you remove?

Thomas: So, I wanna ask, I’ve got, I’ve got my chart and I’ve got a legend over there with the different colours for those different categories. Um, best practise might be to directly label. Is it the type of thing you would transition where you might leave the legend and directly label and then later get rid of the legend?

Do, would you transition it?

Kate: Yeah, generally I would, yeah. You always kind of give them like the intermediary, like so they don’t notice as much.

Thomas: Yeah. Like sneak it through one step at a time. Yeah. Yeah.

Kate: Okay.

Thomas: All right. Look, the point of declutter, this second step of the story chart process is to remove every unnecessary element that is causing your audience to spend attention and cognitive effort staring at something that does not push the message of the chart forward.

Mm-hmm. And distracts. And we want it to be fast and easy.

Kate: Yep.

Thomas: So we’ve cleaned up and we’ve decluttered the chart. We’ve got that blank canvas. It’s easier to read now. Um, so I guess we move into the next two steps.

Kate: Yeah.

Thomas: Step number three here, I standby. It is probably the shortest, quickest step, but I think it might be the most powerful. I think it makes the biggest difference, and

Kate: I completely agree. Yeah.

Thomas: Okay, so look, we’ve got our chart, we’ve got the right chart. We’ve chosen, we’ve removed all the distractions, and it’s a blank canvas.

And the audience quite reasonably would look at a chart like that and think, Hmm, I can see the data, but what do you want me to take away from this? The classic chart title, if we go back to that example I described before with our 12 salespeople, the chart title would be something like year end sales by sales person.

It tells us what the data is and it tells us what’s in the chart, but it doesn’t tell us what we, as the, the data analyst have discovered. If you don’t provide a message, different people will look for different things in that same chart. This is what, like where unmistakable comes in, there is no guarantee they’ll find the same insight you did.

So let’s make sure they find the same example that we did.

Kate: Let me give you an example of if you are probably under 30, you will not maybe remember this, but for anyone over 30, I, I think you know the gorilla video. It was, oh gosh. Oh, the

Thomas: basketball gorilla video?

Kate: Mm-hmm.

Thomas: Okay.

Kate: It was no close to 20 years ago now.

Thomas: It was early internet.

Kate: It was, um, there was this video going around and I did it in a workshop that I did.

It was actually in a safety workshop and there was two teams. They had four people on each team. One team was wearing white shirts, one team was wearing black shirts and they were on a basketball court. And each team had a ball and the instruction was, you have to count how many times the white team passed the ball between them.

And the black team was also going to be passing the ball between them. And I remember watching this and I was like, I’m gonna win. Like I’m gonna get this number exactly right. And I watched like a hawk and at the end I was like, nailed it. 14.

Thomas: You’re just staring at the white team to the exclusion of all else.

Kate: Absolutely. Yep. Like I was, I was winning this thing. And then at the end, uh, that everyone else that he asked for the number and annoyingly like heaps of people got 14, which was

Thomas: Oh no.

Kate: Which was the correct answer.

Thomas: That was what you were asked to do.

Kate: Yeah. And he said, did anyone see the gorilla? And I was like, what?

What are you talking about? And they said, A gorilla walks past, like walks through the court in video.

Thomas: And dances

halfway through from memory as well.

Kate: Yeah. And I was like, no, there definitely was no gorilla. And so they played the video back and halfway through this video of these people passing this ball, this gorilla, this man in a gorilla suit

Thomas: Yeah.

Kate: Walks through, does a dance and walks off. You could not convince me that that was the same video. I still remember that feeling of just being absolutely mind boggled. And I would not believe that that was the same video. But what had happened, and, and this was the whole point Yeah. Was they were saying, you need to focus on this thing.

And I was so focused on the white team. That’s all I wanted to see. And you don’t see the the distraction, the the thing

Thomas: Yeah.

Kate: The extra thing. And it was, it was a safety example, but it has always stuck to me that what you tell people to focus on Yeah. They will focus on Yeah. And they will miss everything else.

And if you don’t tell them what to focus on, then they’ll choose. They’ll make up their own.

Thomas: Yeah.

Kate: Mind on what they’re going to focus on. Yeah.

Thomas: Yeah.

Kate: And this is exactly the same with charts. If you do not tell people what to focus on, they will choose their own thing to focus on.

Thomas: Yeah. We effectively provide them a filter, which is we are priming them what to look for.

And if you’re not familiar with the video, I appreciate, we’ve now maybe spoiled. It, but think in the show notes and you can go and have your mind blown. So why then I think we’ve got, you know, sort of two types of titles here. The descriptive title; “year end sales by salesperson”, and an insightful one, which tells us what to look for.

I’ll give you some examples in a moment, but why do we default Kate to those descriptive titles rather than insightful ones, kind of by default?

Kate: Because that’s what has always been done. I think it is, honestly, as simple as that is that is what we’ve always seen. That is what happens is you get your title from your graph, which is just the description of what the data set is.

Thomas: Yeah, yeah, yeah. Okay. So let me go for an example here. What’s the difference then between a descriptive title, which is “Sales by Region”, or one that says, I’m gonna say “Queensland generated the strongest sales growth for the year”.

Kate: Yeah, so the first one, sales by Region tells us what the data is.

Thomas: Yeah.

Kate: Whereas, uh, what’d you say? Queensland generated the generated

Thomas: strongest sales for the year.

Kate: Yep. Tells us how we are meant to read the data, which is we are meant to take away the understanding that Queensland had the strongest sales

Thomas: to focus sort of on Queensland with the context of the other data there.

Kate: Yeah. Yep. Yep.

Thomas: And that, that feels to me like what I’m now doing is I’m making a statement, a message, an assertion, and then I have the data there that kind of proves the point for me.

Kate: Yep.

Thomas: Rather than like, here’s the data, find what you will.

Kate: Yep.

Thomas: Yeah. Okay. So, so then does an insight title, an insightful title, does that just not risk telling people what to think?

Kate: Yes. That’s the point.

Thomas: Yeah,

Kate: that is exactly the point. You’re the one who has done the analysis. You have taken away this understanding from this data, and now you are showing other people what that understanding is.

Thomas: Mm-hmm. And I should imagine the line between telling them what the data says and being, I guess manipulative is just you have the data to back up that assertion to back up that insight it.

All right. So go another example. Here’s what I, um, uh, we had for a workshop with, um, tertiary education institutions, which was the chart that we started with was “admission inquiries by source”.

Kate: Mm-hmm.

Thomas: And I remember they had things like, oh golly, it was like paid ads, referrals,

Kate: email. Yep.

Thomas: Yeah. Like, um, uh, families, like an alumni was the older brother type.

That’s right. Yep. That type of thing. It was, it was a lot going on. And when we worked through it, we ended up with a much more insightful title. So have a think about how these two titles feel different. The first, the descriptive title was “Admission Inquiries by Source”. Our insight title was “55% of inquiries come through existing school relationships”.

Kate: Huge difference hey

Thomas: Yeah.

Kate: Because the second one feels like something that I can work with.

Thomas: Yeah.

Kate: And I can go, wow, this is where most of our inquiries are coming from. So then that feels like I can do something with that, which is maybe I need to focus on existing relationships. Maybe I need to increase other sources.

If that’s what you wanna do, whatever it is. But it gives you actually something to go, somewhere to go, rather,

Thomas: it gives us an insight. A “so what?” Of the data, rather than just there’s all the, there’s all the sources of inquiries. Yeah. Yeah. So, um, the important thing though is we keep, keep the descriptive title.

We just move it to the subtitle. So that whole title would be “55% of inquiries come through existing school relationships.” Subtitle; what this data is “admission inquiries by source”.

Kate: Yep. And that is literally, you make it slightly smaller and you put it underneath that insight. Yeah. So you are not removing that entirely.

’cause that’s what people, um, when I work with people, that’s their biggest fear is like, oh, but I’ve gotta put the description in. Yeah.

Thomas: Yeah. Put the description

Kate: in. Yep.

Thomas: You don’t want people then staring at the chart and going, what is, what is this data? Yeah. Oh, it’s the mission inquiries by source. I totally get it.

Yeah. So the important thing is that your audience understands what you’ve found instead of having to like, sift through it and discover it themselves. Fast, easy, unmistakable. Don’t tell people what the data is or what the data set is. Tell them what the data tells us. And so now that we have titled exactly what our chart is, we probably should help our audience find that in the chart.

Okay, so quick recap. We’ve chosen our chart. We have an explain type chart. We have decluttered it to give ourselves a blank canvas. It’s looking pretty plain and, but we’ve added an insight title. We know what the message is. We’re trying to get our audience to see in this. The audience though, still has to look through that data to try and find the evidence that we’re referring to.

So now we can basically say, look, look here. Look. This is where that is. But we can actually do more than tell people. We can visually just direct their attention and exactly where their eyes are going. Yeah. Using Highlighting.

Highlighting, yeah.

Kate: Which is the fourth and final step of story chart. Yep.

So I think of this as, and stay with me here. Where’s Wally? Or if you’re in the US where’s Waldo? I? They’re Waldo. Yeah. It’s a weird,

Thomas: yeah, it’s, it’s Wally. But anyway, yeah.

Kate: Um, it is like giving someone like this page and being like, and you know, as we can see here, we’ve got Wally.

Thomas: See, there he is.

Kate: Yeah. And, and people are like, what?

And you, you can sometimes make people hunt for the thing that you’re telling them they should be looking at.

Thomas: Yeah.

Kate: But if you point and go there, there he is right there.

Thomas: This isn’t Where’s Wally? It’s There’s Wally.

Kate: Yes. Then you can’t not see him. Yeah. It’s really obvious now.

Thomas: And it it, there was a TV show associated with those books too.

Wasn’t there?

Kate: There was, my sister and I used to watch it and of course we would compete to see who could find Wally first.

Thomas: Yeah.

Kate: Um, they would pause the show for like 10 seconds and we would run up to the TV and have to try and find him first.

Thomas: Yeah. But if you didn’t find him,

Kate: it would zoom in. It would highlight, it would put a little circle around Zoom.

I put a circle and zoom in. Yeah.

Thomas: Yeah. Which is the equivalent of. There’s Wally. Like that’s what to look at, right? Yeah. When it’s, when it’s highlighted, zoomed in with a circle around it, you are drawing attention to it. It’s not just that you and your sister can easily see it, it’s, it kind of becomes impossible to not see, and you really, it’s difficult to look away from.

Kate: Mm.

Thomas: Okay. So how do we apply that? Right? So where’s Wally? Everything is coloured in, but on our chart, nothing’s coloured in. We made it all grey. But why do we tend to think in a bar chart, for example, a column chart than making every bar a different colour is better or more attractive?

Kate: I have the same answer as I did to the descriptive title.

That is the way that you see it done.

Thomas: Mm.

Kate: That is standard. No one is trained on data visualisation. We all start with insert chart and just going with the default. That is what we see. And so that is what we do.

Thomas: And I think that leaves us to the bit to then say verbally and kind of take up that bit extra cognitive load for everyone, which is what we’re looking at here is the orange bar.

It’s like, oh, there’s all these colours looking for the orange rather than grey. And there’s one coloured bar, maybe it’s orange. Right. Um, and then what hap, you know, I, I guess that that comes back to what happens when everything is highlighted.

Kate: Nothing is

Thomas: highlighted. Nothing is highlighted. Yeah. The orange doesn’t stand out against a red, a blue, a green, A purple.

Yeah.

Kate: Yeah.

Thomas: So let’s talk practicality. We’ve kind of hinted at a little bit. How do we draw attention? How do we highlight one of those is to add colour meaningfully. So from our grey blank canvas, we find that relevant category or that relevant group or that relevant dot point. And we use just a considered colour, often a branded colour, or sometimes it’s green for good, red for bad, add a colour for that’s meaningful

Kate: or, and um, the New York Times does this really well, grey and black.

If you need grey scale, if you need something that’s super high contrast, black, black and grey. Yep. Works really nicely. Yeah. New York Times does some of the best data visualisation and most of it is black and white.

Thomas: Um, so that is one way to add colour. The second is to directly annotate, which means you literally will put like a text box or something on your chart that says, this is what we’re looking at. Look there. This right here. Yeah, yeah, yeah. And then another one can be to like add a benchmark, an average, um, a goal, a benchmark I guess is that it target.

Target. You’re putting in that single grid line. Remember we got rid of all the grid lines and now we’re gonna thoughtfully add one in because it tells something, it helps us to interpret the message. Yeah. A target is probably an easy one.

Kate: I would argue that it’s not a grid line, it is a data point. If it’s a grid line.

Yeah. Sorry to just kind of pull you up on a technicality there.

Thomas: Technicality is, is from the Excel top tips person.

Kate: Thank you.

Thomas: Alright. Okay. Can we communicate two different messages using the same data?

Kate: Yeah, absolutely. And that is kind of the point there is so many potential messages in any data set. I don’t care what sort of data set you give me, we can find multiple messages in that.

Yeah. And that is the point. We need to communicate the message that we have found and that we want people to understand to take away.

Thomas: Yeah. It’s on us to pick that message and make that message fast, easy, unmistakable to the audience.

Kate: Yeah.

Thomas: And we know using just that, changing the insight title, you can imagine like year end sales by salesperson to maybe something like, um, bonus eligibility by salesperson could be one message.

There’s probably other messages in there. Um, but changing what that insight title is and how we highlight that chart. Mm-hmm. Without saying anything, we can communicate a very different message. Yeah. Yeah. Okay. So we’ve told the audience what matters and now we’ve pointed at it and made them point their eyes at the actual piece of evidence that supports that message.

So now we need to probably combine these four steps to make sure that we’re not just making our charts look pretty or clever, but we’re hunting for clarity. We’re hunting for clarity over cleverness. We don’t want something that’s complex and takes time to sift through and think about because we think it makes us look smart.

We are looking for clarity. Fast, easy, unmistakable. Mm-hmm.

Kate: All right. So now we are back to that opening scenario of you’ve got someone, maybe yourself, who has spent hours hunting through data, finding some kind of insight and you understand what those charts say.

And you’ve got an audience who literally has a few seconds to get to that same understanding. So how do we, as the person responsible for creating that data visualisation, how do we make that understanding accessible and possible within those couple of seconds? And the answer of course is StoryChart.

Thomas: So there it is, just four steps. Maybe that sounds a bit simplistic. I hope it is quite simple to do. It is, we know it is, um, to take a chart from something that looks complex, overstuffed, uh, all the colours, maybe just hasn’t got that audience focus into something that looks loved considered. And I’ll say it again, fast, easy and unmistakable for our audience to understand.

Do I need to say that again? I’ve said it a lot of times. I’m gonna put a link to the resources, to the checklist, especially, uh, what you can do for decluttering is a few extra steps we didn’t mention here and examples of highlighting all linked in the show notes. And if you wanna see us workshop some actual charts with this process, we did a masterclass on StoryChart

Uh, and the replay for that one is inside the Influential Analyst Academy. So I’ll put access details for that in the show notes as well. But this has been StoryChart. Best practise for data visualisation.

Kate: Yeah. Good, good. I love StoryChart. I love data visualisation.

Thomas: Yeah,

Kate: it’s always fun to talk about.

Thomas: And thank you for listening in and sharing this little bit of time with us today. If you have any questions about Story Chart or have some particular topics when it comes to communicating data that you would like us to address in this office, please feel free to email us podcast@presentationboss.com.au

We love hearing from you.

Kate: Yeah, and if you found value in today’s episode, of course, please subscribe to the podcast and share this episode with a friend.

Thomas: Lovely. Thank you very much for listening. Have a great week.