Thomas: it is episode seven of season two for the Presentation Boss Podcast. Hello. Welcome. Good to have you here. Hi, Kate. How you doing?

Kate: Hello. I’m good. Good,

Thomas: good. Uh, look, just before we kick on, we need to announce, we, last week, launched our Influential Analyst Academy. So if you’re the type of person who is good at doing analytics and understands what’s going on in the spreadsheet and with the numbers, but would like to work on the skills around influencing decisions, in being able to communicate your, uh, expertise and being more valuable in the age of AI, this is going to be the community for you, where we have a bunch of analysts working on similar skills to you, and our promise is every month we work on a single skill.

Uh, Kate, tell us about this month, actually.

Kate: Yeah. So this month we are working on data visualization. We ran a master class on it, um, last week, I think it was, and, um, now everyone has been issued a data set and a scenario, and it is now everyone in the, uh, it is up to everyone in the community to submit their data visualization that they’re going to create from that.

And next week we will jump on again live and, um, go through all of the submissions, talk about all of the data visualizations that have been submitted.

Thomas: Yeah. Every week we have a live call. You can, uh, have access to Kate and I, and, uh, get some feedback, ask questions, uh, and the rest of the community. So look, we will put a link in the show note description, whatever it is, and you’ll find us.

Uh, but-

Kate: Today episode.

Thomas: Yeah. I have a question for you, Kate.

Kate: Mm.

Thomas: Which is have you ever seen kind of any of the following scenarios in your, in your analytics career?

Kate: Okay, go. All

Thomas: right. Um, like where you have somebody presents you a dashboard or page and it has heaps of really tiny charts sort of crammed on there, and they kind of just say, “Well, there’s the data.”

Kate: I have seen that probably three times in the last two weeks with clients. Yep.

Thomas: Okay. Love it. Uh, what about when you get, like, an analyst who shows you a graph and it’s clear to them that it’s really obvious, and they’re like, “There, see”? And no one else in the room gets it.

Kate: Yep. Absolutely. I, um, have seen this from clients, again, fairly frequently, and honestly, I have probably done this in my career before as well, probably many a time, to be honest.

Thomas: Okay.

Kate: Yep.

Thomas: What about you have a, a, a chart that somebody is showing you and they’re very proud of their work ’cause they’ve put hours and hours into making it look good, into… Uh, but, uh, then they just kind of can’t tell you what it actually means when you interrogate it.

Kate: Yes, definitely. And again, I remember doing this.

I remember being in that position of being- … like questioned and not being able to, like, properly articulate, um, what I had created. Yeah.

Thomas: Yeah. Yeah. Okay. Uh, which kind of leads me to my next one, which is somebody who, uh, knows the conclusion that they wanted before they went to the data and kind of just makes data visualizations and that story back up the conclusion they were chasing.

Kate: Ooh, I don’t know if this one is so easy to spot immediately, but I have been in that situation, um, in the past, so I’ll happy- happily talk about that later.

Thomas: Okay. And the last one, which is somebody who has a chart that is super detailed and complex. You can tell that it helped them to understand the information, but- It takes a lot of time to kind of understand what’s going on in that chart, and they just drop it into a presentation and hope that everybody else kind of gets it.

Kate: Yeah. Yeah, I see that frequently. Have I done it? Most probably.

Thomas: Yeah.

Kate: Yeah.

Thomas: Yeah, yeah.

Kate: Yeah.

Thomas: Um, difficult, right? When you spend a lot of time looking at data on a chart, and you think everybody will get it, right?

Kate: Yeah. Yep, yep, yep.

Thomas: So what I’m getting at here is, uh, these are all quite different problems. They seem like very different problems, but often the same underlying issue, the same underlying lack of a process.

Because what has happened in all of these scenarios is a misunderstanding around the journey that data needs to go through before it reaches an audience. Mm-hmm. And that’s what we’re gonna talk about today.

Kate: The data journey

Thomas: Yeah, the data journey. Now, to begin to understand the data journey, we need to know that there are two fundamental legs in the life cycle of data or information, and the first one is called explore. This is about the analytics. This is about you understanding the information, and the second is explain.

This is about you helping other people to understand the meaning of that information. They are fundamentally different jobs and kind of need to remain, not separate, but certainly distinct.

Kate: Yeah. Um, the way I think about explore is it is very much a internal, you know, like thinking process. Um, where it’s very messy.

Complexity is totally okay because it’s generally just you sitting there sorting through your own data, your own information, and you know, when I’m looking at a spreadsheet, say, this is the section, this is the part of the, um, process where you’re just creating visualizations left, right, and center because what we’re looking for is trends and anomalies, and we’re looking to literally visualize the data and see what we can get out of that.

Um, and the only person that needs to know what’s going on, the only person that needs to understand all of those charts is you- And- … because you’re using them …

Thomas: and the only person who should see those charts is generally you.

Kate: Yes, definitely. Um, because they are generally labeled horrendously. They’ve not been-

diddled and made to look nice at all. It’s- Yeah … it’s just, yeah, you sitting there in your own little world.

Thomas: Yep.

Kate: And then the second part is explain, where you don’t want things to be complex. Like, complexity is not what we’re aiming for here. Instead, we’re really looking for clarity. We’re looking to make sure that we’ve got context, but the right amount of context, not too much. We’re really making sure that our story makes sense, that we’ve got some kind of logical flow of information and, you know, that we’ve discerned what bits go in and what go out of what we’re actually saying.

And it really matters because the audience is on the other side of that, and they need to come to the same understanding and the same conclusion as you. Whereas if you are just analyzing in that explore phase, two people can look at the same data set

come up with two completely different interpretations.

Mm. So we wanna make sure in the explain section that everyone is coming to the same conclusion.

Thomas: Unmis- making it unmistakable, right?

Kate: Yeah. Yeah, yeah. Um, and once you really separate explore from explain, you can then map the whole journey.

Thomas: Mm.

Kate: So the concept of explore, explain has been written about extensively. It’s been around for a long time. But the first time that I came across the concept of the data journey was in Brent Dykes’s book, Effective Data Storytelling. This may be, I’m gonna call it, this may be my favorite data storytelling book that I have.

You can tell- Yeah … how much I love it.

Thomas: That’s a lot of sticky notes, yep.

Kate: Yeah. Such, such a good book. But data journey was one of those things that I understood implicitly. I… There was nothing there that was surprising, but I read through Brent Dykes’s explanation of the data journey, and I was just like, “Oh, this just explains and articulates- Mm

what I understand just so beautifully.” And I wanna use the same language, because he’s got a few labels that I think are just perfect. I don’t think we can improve upon them. Mm. So we’ll just use his same language. But we do use this concept in our workshops and in our training. Um, and yeah, so today we’re really just going to talk about, um, the data journey and take you through how we use it to help people understand where data storytelling actually fits in the world.

Thomas: So to understand it, we need to think about where those two legs of explore and explain fit. So data starts as data, comes some kind of set of information, whether it’s in a spreadsheet or in a, uh, some kind of warehouse or a data lake, or it’s in your head, or-

Kate: It could just be a pool of knowledge- Yeah

that you have about something. It doesn’t have to be- Yep … numbers and kind of, um, discrete data.

Thomas: Yeah, totally. It exists somewhere, somehow. And what we do is we then do the process of explore. This is the analysis, where we put a brain onto that information, and we try and extract an insight. That is the second, uh, s- step there.

So we have data, we have insight, and sit between them is explore.

Kate: Mm-hmm.

Thomas: Now, that insight is kind of extracting meaning, an answer to the so what question.

Kate: Hmm.

Thomas: Think that’s sensible?

Kate: It’s finding something worth saying, worth commenting on, in that information.

Thomas: Yeah. Yeah, we’ve done that sort of pattern recognition of the analysis of- Mm

like you said, heaps of charts that kind of make semi-sense. But we’ve extracted something sensible out of it worth now sharing with other humans. And then we need to do the explain. Now, this is the data story, and the data story step takes us from that insight, something worth communicating, and sends it to an audience.

So raw information, however it is sort of exists, whatever it is, is explored, and that exploration produces an insight. That insight is then explained via a data story to an audience. And so data storytelling, when we talk about it, lives quite distinctly between that, that step of insight and audience. I, I sometimes call it the final mile of data, like getting it to that last place that it needs to go to influence or drive a decision or inform somebody or share knowledge in some way.

Kate: Guess what? That’s another Brent Dykes concept. Final mile.

Thomas: Is it?

Kate: It is. He’s so good.

Thomas: I feel like we’ve talked about bits and pieces so often. You grab bits of information- Yeah … and we steal stuff off people in workshops, all kinds of things. Um, there’s that, it’s that final mile, and it, it, it exists, the data story exists between insight and audience, that explain stage.

So- Yeah … uh, data storytelling is not necessarily about finding the insight. It’s not about dumping data and spreadsheet onto the screens. It’s not just, just visualizing the data, making it pretty, and it’s not about putting charts in front of people. That is really not a data story if we’re just- Mm

taking the data, turning it into charts, I don’t know, like kind of default, and then giving it to people. That’s not really qualifying either. It is very much about taking that insight and making it into something that is audience ready. And now that we know this data journey, what we can do is start to see all the shortcuts and the little mistakes that get made out there.

Kate: Yep, yep, yep, yep.

 

Thomas: All right. On those little mistakes and shortcuts that we see get made, there are three main ones that we’ll talk about, the three broken journeys, and the very first one is called the data decorator, and the data decorator happens when we have our data, our information.

We visualize it, we kind of give it some color and shape, and then we just hand that visualization straight to the audience. That’s the data v- the data decorator.

Kate: Yeah. And I think this has come about, um… It used to be bad, but in the last couple of years I have seen it just increase and become so much worse, but I think it, it really is because we have given people completely unfettered access to data visualization tools, things like Microsoft Power BI

Thomas: It’s everywhere.

Kate: Yeah. It comes free with your Microsoft package- Mm-hmm … now, so it’s, um, everyone has access to it, and people don’t necessarily have the proper training in it. They might have Power BI training, but they don’t have data storytelling or data communication training. And what ends up happening is that people can display data in-

completely unlimited number of ways.

Thomas: All the ways, yeah.

Kate: All the ways, and really easily, like, just with one click of a button it’s like bam, bam. You produce that many different types of charts and graphs. Um-

Thomas: And it, it kinda makes sense, though, ’cause if you’re Microsoft trying to ensure that you are the industry standard piece of software-

Kate: Mm.

Thomas: you kinda wanna ensure that you keep saying to people, “Hey, we can do these types of charts. Now we have this capability. We can do these filters.” You’re gonna add more features and- Yeah … layers to the piece of software.

Kate: It’s kinda like the workplace TikTok. Like, it’s so addictive. It, it, like, it keeps people so engaged.

It’s so addictive because you can do so much stuff, and it’s just kind of this constant little dopamine hit of, um, producing another graph. Um-

Thomas: Yeah.

Kate: But because it’s addictive, and then you get the combination of, like, there’s so much more data available, and then you’ve got basically unlimited ways to display numbers, um, but-

display numbers. And this has produced data decorators And it really happens when people have not explored the data. They just make it pretty. They just produce- Mm … a chart from it, and they color it in, but they don’t actually have anything to say about what is happening within that information. Um, they’re not really sure what is important.

There’s, there’s no discernment there, right? Um, and so what happens, they take all of this information, they, bam, put it on a page. If you’re really clever, you can get, like, six, sometimes eight graphs to a PowerPoint. And that goes in front of an audience, and the idea is that if I just give them the information, they’ll know what to do with it.

They know this data better than me. They will be able to make some kind of sense with it, sense of it.

Thomas: Mm.

Kate: And I see this happen particularly with dashboards, where s- people will- Yeah … create pages and pages of graphs. And I will question them on, you know, “What are we trying to say?” And they say, “Hey, I’m,” like, “I’m just here to produce the information.

It’s not my responsibility to analyze it to find anything.” And so the focus becomes on what they can do with that data. So it’s on their filters and their different types of- Mm … manipulation, rather than, what does it mean?

Thomas: I mean, how often have we seen that we work with, with clients, they’ll bring up a, a dashboard that they’ve built, and we say to them, “Cool.

Walk me through or tell me about your dashboard, what it’s trying to achieve, and so forth.” And we get five minutes of, “And then you’ve got this filter, and we can go by state, and by, by, uh, government, and we can go by, like, demographics.” And like y- y- we just have to sit there and like, “Yep, this is all very good.

Now back to the question of what’s the point here?”

Kate: Yeah. And you know that that is happening when they’re explaining that to their managers. When the manager is saying- Mm … “What is the insight? What i- what is this telling me?” You know that they are giving the same breakdown of, “Here are all the filters that are possible.”

Mm.

Thomas: Rather than pointing out, you know, I don’t know, 18 to 24-year-olds in Brisbane within Queensland are the highest of some particular statistic.

Kate: Oh, I was wondering where you were going with

Thomas: that. I had no idea where I was going with that. But, like, we need to have some kind of meaning that we can- Yeah

lean to, rather than just like, “Ooh, we can divide it by gender and age and, and”- Yeah.

Kate: Yep …

Thomas: yeah.

Kate: Yep, yep.

Thomas: So look, why do we, Kate, then see those exploratory- Mm … charts and, and results used in the explanation phase? Yeah. Why does that happen?

Kate: Yeah. And maybe I’m being a little bit too broad brush with, you know, just the access to, um, these tools.

But I, I think, you know, there’s time pressure. People want to produce. They want to look like they have done things, and it’s easy to produce a lot of work.

Thomas: Yep.

Kate: And there’s also the assumption that seeing the data is the equivalent to understanding the data and understanding the evidence.

Thomas: Mm. Yeah. And it’s quite key that we ensure that we are communicating the result of the analysis, and we’re not asking our audience to kind of do the analysis during the presentation of, “Here’s my six to eight graphs.”

Kate: “You deal

Thomas: with it.” “What do you think? Yeah, what do you think, buddy?” Yeah. And look, there’s, um, there’s an easy trap to fall into. We see it often. We see good analysts end up doing a bit of data decorating. Mm. Um, so some of the questions to ask yourself are, to help you avoid, “Am I just decorating my data?”

Which is, can I ask, what did we learn from this data? And you can answer it in a sentence, which kind of helps us to ensure that we’ve done proper analysis. Am I choosing charts because they reveal something to us, or is it just because the data is now visualized? Or is this chart type actually showing us something?

Am I expecting my audience to do the interpretation? Kind of like you said that, uh, the sentence around they’ll, they know what’s going on here better than me if I just- Mm … give them the information. So am I expecting them- Yeah … to do that interpretation?

Kate: And, yeah, we often see kind of more junior analysts producing this kind of-

Thomas: Mm

Kate: um, information, like just charts. And I know that I have definitely done this in the past-

Thomas: Mm …

Kate: um, when I think it’s not my responsibility to tell you what’s- Mm … going on in the data. Like, my responsibility is just to help you visualize it. But that’s not really the case. Like you, you eventually learn that you do have more of a responsibility than to just show other people the data and, and tell them to go and analyze it.

I

Thomas: mean, that’s kind of why we’re employed, isn’t it? To analyze the data and to output meaning. Mm. To output some kind of, um, strategic helping towards a decision.

Kate: Mm.

Thomas: To influence or be a strategic advisor. Um, and that last question I wanted to bring up as well, which is- Mm … just is this just a dashboard kind of masquerading as a data story?

And I say dashboard here quite loosely. Mm. I’m gonna come back to if we see PowerPoint slides, you know, in a, in a live presentation that realistically looks like a dashboard ’cause there’s six, eight, 10 charts on there all squeezed in. Is this trying to be a data story, or i- is it just looking like a dashboard?

So that is the data decorator

all right. The broken data journey number two is the data dumper, which is that’s, that is a name. Look, this is what happens when we have our data and we do some really nice exploration, some anal- analysis on that data, and we find an insight, a good insight worth communicating.

Uh, and it’s really clear to us, so we take that insight and we just, phew, hand it to the audience without going through the process of synthesizing it into a data story. It’s just clear insight, their audience, straight to them.

Kate: Mm. And if I’m really honest with myself, this is I think what I did for a few years, um, in my job.

Mm. And, and it’s, it’s generally the analyst who is a little bit more mature in their career and is able to find some good things in the data. And- Yep … you find something and you’re like, “Ha, yep, this is good. This is worth saying.” And you put that on a PowerPoint because you’re like, “This is so in… This is so clear.

This is so obvious, even though I have stared at the same spreadsheet for probably days on end.” I have this assumption that they’ll get it within a few seconds. Mm.

Thomas: Yeah. A few, a few seconds. Like the data just speaks for itself, right?

Kate: Yeah. And I totally get it when you’re in explanatory mode, and you’re using data visualizations to find something.

When you’ve found something, you’re like, “Well, I’ll just use that. I’ll just give them that.” Um, and it doesn’t quite work like that. You’ve got to help people understand what you have found. You’ve got to take them through that proper data story, um, because generally, the data viz that you create does only speak to you, and generally not anyone else.

Um, and unfortunately, the data dumper relies too heavily on the kind of raw evidence, the raw facts. Um, and it completely discounts the value of a well-crafted narrative, like having a nice structure around it, that kind of thing. Um, I think the biggest risk here is making our audience feel silly because you’re like, “Hey, this is so obvious,” and they don’t get it.

Yeah. And they’re like, “Why am I not understanding this?” Yeah. Um, and it also means that what you are trying to communicate can be completely discounted as well, and it can be really hard to get any traction with what you’re trying to, um, talk about.

Thomas: And I think on that, this is how we start to get people identifying as, like, just not numbers people.

Kate: Mm. ‘

Thomas: Cause if they’re being given- Mm … this sort of data dump, and I’ve got somebody telling me, an analyst telling me like, “This is so clear, and this is what’s going on. There’s the insight.” And if I’m looking at it, and I’ve had seconds, maybe minutes to look at this chart, whether, where the other person’s maybe looked at it for hours, days, weeks, whatever- I’m just like, “Maybe I’m just not a numbers person.”

I start to switch off more and more- Mm. Yeah … when I see data because I just, I just don’t get it. It’s not landing in my brain. And I think that’s not a me problem as an audience member, it’s a data story problem from the person trying to communicate- Yeah … w- something worth communicating, but it’s just not landing.

Kate: Yep. Yep, yep. So the questions to ask, um, to make sure that you are not-

Thomas: Yep …

Kate: making this mistake?

Thomas: Yeah. Just to, yeah, make sure you’re not dumping data, which is does this chart make sense because it’s clear or because I’ve stared at it for hours?

Kate: And what’s the best way to check if it makes sense just to you?

Thomas: I, I, I would say ask a friend or a colleague.

Kate: Yes.

Thomas: Yeah. Run it by somebody, “Does this make sense to you?” in a few seconds. The other question is, have I tailored this visualization for my audience, or have I kind of just tidied up some of my messy anal- analytics, uh, charts and presenting those? Have I kind of built the data story or am I just polishing some of the analytics?

Kate: Yep.

Thomas: And kind of as we’ve alluded to, so much of this I think comes back to that having spent hours or days or weeks in the data is you just get that curse of knowledge which is it’s so obvious, it’s so clear to you that you forget what it’s like to not be able- Mm … to see that obvious insight. You… It’s just difficult to unsee.

You forget what, you forget what the data looked like when you didn’t know where you were looking.

Kate: And this can really come from months of, like if you’ve got a report and every month you’re asked, “Can you just add this section?” Mm. Like, “Can you just add…” Oh my gosh.

Thomas: Can you just… Mm.

Kate: Can you just… Yeah. Um, and I think this makes the data dump probably the most, like, understandable because most of the time you think that you’re being clear in communicating because you’re just providing what you have been asked.

Thomas: Yeah.

Kate: Mm-hmm.

Thomas: Yeah.

Yeah. That is the data dumper

Broken data journey number three. This one is called the data cameo because the data just kind of has this cameo background starring role in the, in the presentation. What happens with this one is we start with the insight, not with the data. We start with the insight. What is the kind of conclusion, the result that we want to find in the data?

And then we go back to the data to try and find the evidence that supports that insight, that supports that conclusion, and then we can build our expl- explain our data story based on that kind of data. And we hand that to our audience, so we can see how the data might just be having a little cameo in there to support an insight that was built as a conclusion.

Kate: And of course, the biggest risk with this being we can potentially…

Thomas: Cherry-pick the data.

Kate: Yeah. Yeah. Yep, yep. So I actually have a story around this. Ooh. Um, it was more than 15 years ago now, but I was working in finance for a government organization, and we had a change of government. Mm. A fairly significant change of government, and I was asked to produce some numbers for the area that I was supporting in finance, and it was for the sponsorship that the organization was providing for various community groups and community projects and that kind of thing.

And I was told I need to split it by this particular thing, I needed to make it look like this. And I was like, “Mm, hang on. That’s not quite exactly how it is.” And, uh, it was actually someone quite senior had come down and was like, “This is what it needs to say.” And I was like, “Oh, but that’s not quite right.”

I was, I was fairly junior. Um- Mm … and they were like, “This is what it needs to say.” And I didn’t get it- Ah … at the time, so I just kind of had to produce what I was told to produce. Um, and it didn’t sit particularly well with me, but I didn’t fully understand it. And I see now that they had some kind of narrative that they- Mm

needed to get across, and I needed to provide the data to back up that particular narrative. Um, and from then I was always quite careful, because it made me feel so uneasy at the time. Then when I moved into a different department, I was very conscious of anyone ever saying, like, “This is what I think’s going on.

I need you to find some data to back me up.” I would not let that fly anymore because I just didn’t, I didn’t want that same feeling- Mm … of, um, I don’t think this is quite right. Um, and look, I was, I was fairly junior.

Thomas: We hadn’t learnt the meaning of the word complicity yet.

Kate: Yeah. But, um, it does happen. It absolutely happens.

And I think perhaps that business, uh, people in business who are less data-focused and more narrative-focused are more susceptible to this one. And the risk is that once you start kind of pressing and asking questions and, uh, v- delving a little bit further into the data, that narrative, that story can start to crumble.

Um, and, and I think you just have to be very, very careful that you are definitely going from the data first, and then finding the insight, and then creating the story, rather than starting with, “I think I know what’s going on.” And I think there’s a very clear distinction here between testing a hypothesis and proving a conclusion.

Thomas: Yes. Yep, yep.

Kate: Yeah, so you can definitely start with, “This is what I think is happening”, but you have to be prepared for that to be unproven.

Thomas: Mm. That’s arguably the entire scientific method, right? Well, yeah. Which is testing to try and prove myself wrong.

Kate: Yes, yes. Yeah, and that’s going to make that, um, a lot stronger than just finding the data to back up-

Thomas: Mm

Kate: the story. ‘Cause yeah, as I said, as soon as it’s pressure tested, as soon as it starts to come under scrutiny, it can fall apart quite quickly. Um, and I have… I was nervous about it for years, about what I had ended up- Mm … submitting. Um, yeah.

Thomas: Yeah. I can imagine it doesn’t feel good. I know when we talk about this in, in workshops and with clients, like there’s always a bit of a giggle.

There’s always a bit of a joke about like, “Ooh, I too have worked in politics.” I think it’s a bit known or expected, or maybe it’s quite cynical that this happens in politics too. You know? Mm. Like you say, change a government, earn votes, those type of things, but in business as well we know it happens.

Kate: Yeah.

I think it probably happens more inadvertently-

Thomas: Mm …

Kate: than intentionally.

Thomas: Yeah. And so I think there’s some questions we can ask ourselves to ensure that we are not inadvertently, uh, being a cheeky little data cameo, um, doer. Um, the first question is, did I know what I wanted the answer to be before I analyzed the data?

Kate: Mm.

Thomas: Uh, if there’s a bit of feeling there or if, as you say, you’re being told what somebody wants the data- This- … to say …

Kate: this is what it needs to say. Yeah.

Thomas: Yeah. Am I looking for evidence or am I looking for confirmation?

Kate: Ooh. Yep.

Thomas: Which is kind of like you say, am I testing a hypothesis or am I proving a conclusion?

Kate: Yep.

Thomas: That’s the difference here. Am I looking for, yeah, evidence or looking for confirmation?

Kate: Yep

Thomas: And also have I actively looked for, uh, conflicting data? Something that will disprove my current interpretation, which is kinda like I said, are we doing analysis… I, I call it the scientific method, but like- Yeah

am I trying to disprove? Am I looking for the ways that prove- Mm … just how wrong I, I potentially am here? Like, what have I missed? What is, uh, what is not supporting the conclusion that we’ve come to here? Yeah.

Kate: Yep, yep.

Thomas: Yeah. So that is, that is the data cameo.

Kate: Mm.

Thomas: So those are the three big broken data journeys that we see: the data decorator, the data dumper, and the data cameo. Now, what I should say, what we should say is this is not types of people. I’m not trying to name and shame-

or make you feel bad, right? They’re just mistakes that we can fall into, and I think we’ve explored a little bit why they kind of happen and how it’s entirely understandable that we land there from time to time. Uh, we have seen good analysts make a mistake every now and then. We’ve seen some people manage to make all three, depending on the situation.

Like, it just, it’s, it’s complicated stuff, right? And I’m curious if, you know, you’ve been thinking throughout this episode which one maybe are you most susceptible to. Have you, have you felt in yourself that, “Oh, maybe I do lean that way just from time to time,” or, you know, take a cheeky shortcut?

Kate: Mm. I, I think it would come out when you don’t fully understand the data well enough and you just think, “I just need to show people something,” that’s when you would more easily fall down the data decoration trap.

Um, or if you… The opposite, when you know your data so well, you’re really familiar with it, and you think, “I just need to show them this because it’s so obvious. They’ll be able to see it.”

Thomas: Mm.

Kate: Um, that becomes the data dumper where you, you think it’s just so obvious. And then finally, the cameo comes out when you are quite emotionally invested in a particular outcome.

You know what you want the answer to be, and you go and just look to back yourself up with some data.

Thomas: Yeah. Yeah. So this is the data journey where we move from having our information, we explore that with analysis to an insight, something worth communicating, and we convert that into a data story where we explain that to an audience.

And so data storytelling isn’t always fixed by just doing better data storytelling. Or I should say- Mm … communicating isn’t the whole part of communication. Mm. I know we talked about this, um, uh, talked about this back in episode, um, four where we talked about the tip of the iceberg. The bit that you see, that final piece that’s the report or when you’re presenting the thing that looks really good isn’t the whole story.

There is so much sits underneath supporting that. Sometimes you need better analysis. Sometimes you need a stronger actual insight. Sometimes you need to stop, uh, making the audience kind of interpret and do some of that analysis- Mm … themselves. And sometimes we really need to interrogate whether the story that we’ve chosen or have been told to choose is supported by, by that evidence.

Um, yeah, so data storytelling is just one part of the data journey. One specific part, taking that genuine insight and making it audience ready.

Kate: Mm.

Thank you for being with us today. If you head to presentationboss.com.au/podcast, you will find all of the resources from today.

Thomas: And that’s where you’ll also find the Influential Analyst Academy, or a link to it, where we dive into data communication skills in detail, and you can have direct access to Kate and myself inside that community.

Kate: Yeah. If you found value in today’s episode, please recommend us to a friend. Have a great week