Data Storytelling Skills to Increase Your Impact with Kat Greenbrook, Author of The Data Storyteller's Handbook

r. I think a lot of the data storytelling that seems to make it into the public domain is coming from these, these media outlets, news outlets.

Because that's generally has been  historically the domain. And now that some of these journalists are upskilling in data, they're combining what they know about people and narrative and story with data, and those are producing some really, really beautiful data storytelling pieces. I think a lot of more traditional.

People that work in bi, they work for companies and they're not necessarily allowed to share a lot of the work that they do. So their data storytelling doesn't necessarily make it into public domain. And so what we see and what we read about are, are being produced by these data journalists.

Richie Cotton: That's interesting that you've got data journalists and business intelligence analysts doing, I guess, similar things or maybe different styles of data storytelling, but you only kind of get to see one side of things. It's a bit more public. So you mentioned a term scrolly telling. I've not come across that before.

Tell me what this is and how you might make use of it.

Kat Greenbrook: Yeah, scroller telling is just making use of, of scrolling on websites, and so you start from the top and you scroll down, and as you scroll down, the story unfolds. Whether  that's. a visual sense. So you can have interactive things moving and popping up as you scroll along or just the narrative advances so it. 

Richie Cotton: that seems straightforward and very useful. 'cause I guess a lot of stories are consumed on the web and you wanna have an experience that. Goes alongside that rather than I guess flipping pages in a book. do you have any more sense of like, what makes these things great? You mentioned a few data journalists there. 

What's sort of the essence that makes the data stories great?

Kat Greenbrook: Yeah, there's, there's quite a few things that can make a good data story, and a lot of that depends on who your audience is, and so. What are they interested in? It's gotta be relevant. So you could write a really interesting data story for yourself, but if you're giving it to people who aren't interested in that topic or that data, it's not gonna beed by them is a good data story, right?

And so you've you've gotta think about how it's going to resonate with your audience. A lot of data stories that go viral are the ones that are quite topical for what's going on within the political  landscape or within the social landscape, whatever it is. But they generally are something that the audience can relate to and the audience care about, and also they have.

A good structure to how the information is presented so they're not just throwing a whole lot of data at people and expecting them to kind of piece it together. They've done all that hard work for the audience, and so the audience can actually just enjoy absorbing the information because it's already presented in a way that they can.

Richie Cotton: So I like the idea of doing a bit more in the sort of content creation side and then making it easier for the audience. So I'd love to get into some of the details around this. I know in your book you have this idea of it's, uh. problem goal action impact framework, which is to help you do this. 

Talk me through what is this framework and how do you use it?

Kat Greenbrook: Yeah. So this is the very, very basics of how a business should kind of operate. You have a problem that is trying to be solved, and problems and opportunity go  hand in hand. So you could also have an opportunity that you're trying to grasp from that you set a goal whether that goal is to fix the problem or take advantage of the opportunity.

In order to achieve that goal though, you have to do something. You have to do some sort of action. And from that action, you would hope that as well as achieving the goal, you create some sort of positive business impact, because that's what we're all trying to create in the jobs that we do. We want to leave a positive impact in the organization that we're working with.

So that's the basic structure of how you work. Everything you do in your job is an action of some sort that is hopefully going to achieve a goal. And to solve a problem or create an opportunity, and that action is gonna drive a positive impact. But unfortunately, most of the time we don't know anything more really than the action that we are doing because we've been told to do it.

So just taking the time to think about, okay, I'm doing this piece of work. What is my  goal for this piece of work? What problem within the business am I solving? What action am I creating further upstream that.

It can help when we share the work that we do. So we are able to influence better, we are able to share more of the context around the work that we do. So we're not just sharing, Hey, I'm building this model. We're sharing, this is a problem that the business have, that this model is going to solve, and the flow on effect from that is that I'm going to create this positive impact.

That's don't the.

And impact of the.

Richie Cotton: Yeah, I how I built this model. It's not gonna make a lot of sense to many of your colleagues or  certainly people you're not working with directly if you don't provide the extra context. So I like that idea of like, well figure out like what problem you're trying to solve even before you, before you're doing any work. 

And then at the end of it you gotta try and figure out like, well, what's the impact of my work? In fact, it sounds like this goes beyond data storytelling even to, it's like it's probably a good idea to think about a lot of these things for any kind of work you're doing. Do you have like a concrete example of how you might put this into action?

Kat Greenbrook: I mean, we could, we could go with that kind of model. Example, let's say you are an analyst and you've worked on a model that's. Or you've done some analysis around why customers are leaving the organization and you might think, okay, I'm just gonna share the results of that, the output of my model.

But you can frame it using the problem and the goal and your action and the impact you hope to create in a way that's actually going to engage people. Think of it as kind of like an elevator pitch for the work that you, so you could say something, our organization  has struggled.

18 months, but I'm analyzing these customers to understand why they're leaving us. And as a result of my work, I hope to add value to our organization through the use of better retention strategies. So just including some of those downstream and upstream things, problem and impact. You can just weave a little bit more context around.

Very, very sometimes technical and specific action that you're working on and more people will be able to understand and.

Richie Cotton: That makes a sense. So it's like what happens kind of upstream and downstream from it. It's just all, all around the problem. Just given that sort of. 

When do you bring it into your workflow? Is this something you think about, like after you've done your analysis, it's like, well, how do I now present this to a group? Or is it something you need to think about before you even start analyzing data? 

Kat Greenbrook:  it can be both. So you can think about this kind of thing before you do the analysis. So you can use data storytelling to help share the results of something that you've done, or you can use data storytelling to help influence an action that you are about to do. So data stories revolve around business actions as a way of sharing them or influencing them, and so having an understanding of your problem, go action.

Impact. 

Analysis or whatever action it is off the ground. And if you are sharing the results of something you've already done, you can use the problem. Go action impact to help weave that context into the way that you explain the importance of the work that you've done.

Richie Cotton: I like that. And so it's, it feels like this is fairly flexible in terms of when you make use of this. Like sometimes you're gonna want to like, think about the whole story beforehand, sometimes after. Towards, sometimes I guess it comes up in the middle, so it seems like  it's sort of like a parallel task to the actual analysis.

Kat Greenbrook: Yeah, I think this is just about understanding the world in which you are working in. So that could be a business world, that could be a scientific field, whatever world that you're finding yourself in, having an understanding of what problem you're solving and what influence and what impact you're trying to create is a good thing, regardless of whether you are doing data storytelling or not.

Richie Cotton: Now I'd like to talk a bit about tailoring your story to different audiences, because it seems like if you're speaking to a business person or speaking to a scientific person or or the technical person, it's gonna be, you're going with a very different story. So do you have any examples of how you might want to tailor your data stories to different audiences?

Kat Greenbrook: It can be very specific to some audiences, so. Piece of work that you've done, and you would have to produce two different data stories to meet the needs of two different audience groups. Going back to the example we talked about analyzing customer churn, you could say that  your initial audience might be your boss, might be your manager, and you could show them what you found in terms of this is what's happening.

But you need to. Motivations what they care about. And probably for someone in that position, they're gonna wanna know, did you go through very accurate data analysis process in terms of I don't want anything being shared from this team unless it's really accurate. 'cause it's gonna come back in and bite your boss.

Right? So your boss is going to be very, very concerned about if anything goes outside their team, it needs to be. Have gone through a very robust analysis process, so they're gonna be focused on that. But if you were sharing that insight with, say, the marketing team, they will wanna know what happened. Yes.

But they will also have a focus on the future, what's gonna happen in the future. So they probably don't care about how accurate your analysis is. They trust you, they trust the position that you have within the company. You're, you're employed. Because you're good at  that. that's not a concern for them.

They don't care about how you've done the analysis. They just care about what it means for them going forward and how they can use it, make jobs easier. So to. Audience motivations and what, they care about. Because you really have to think about that in terms of what you communicate. If you were to go and communicate and a very in depth playbook about how you've done the analysis to a marketing team, you are going to bore the hell out of them.

So it's just being very, very aware of what it is that your audience care about and also how much technical detail they need. So your boss is probably. Be very interested in the technical detail. Your colleagues are gonna be interested in the technical detail. If, if you're a data analyst but anybody probably outside the data team is not, gonna need those technical details.

You're gonna drown them in information that they're not interested in. So you have to be aware of what interests your audience.

Richie Cotton: I say the story hits home quite hard. I remember like earlier in my career, I'd moved  from working in a scientific organization, like talking to other scientists, like going into like a lot of technical detail to going to a commercial organization, speaking with customers. And yeah, I'd started presenting about data cleaning processes of executive.

Then it was like at the end it was like, oh yeah, and this is how you're gonna save 1.2 million pounds, and he's like. You could have led with that?

Kat Greenbrook: Yeah, totally.

Richie Cotton: Didn't so much care about the data cleaning. Okay. So, it sounds from what you were saying, that this sort of tailoring, it's not just about changing individual sentences or phrasings, you have to completely restructure your story or like the entire content depending on who you're speaking to.

Is that right? Do you have to start from scratch every time?

Kat Greenbrook: You don't have to, not every time. So if you have very different audiences, yes, I would recommend doing two different forms of communication. But sometimes it can just. Switching terminology. So you could be, if you're talking more public storytelling, instead of saying spending on our people, you could say something like investing in our  people.

And just that switch from spending to investing has a different way of framing whatever it is that you're talking about. So just sometimes thinking about how.

On your audience, some words will be very emotive, like spending can be a very emotive word to some audiences, and reframing that to investing 

Richie Cotton: I'm curious as to whether your approach to tailoring changes depending on whether you know the audience or not. So imagine like talking to your team or your boss or whatever, you can know them quite well, whereas like, on this podcast we don't know all the people who are listening. So what's your approach for tailoring stories depending on like whether or not you know, the audience?

Kat Greenbrook: I think it's definitely easier to create a story if you know your audience well, but you are right. In some cases that's just possible and.

What their preferences are going to be. Are they gonna like a lot of detail or are they gonna  prefer things in a summarized format? Are they going to make a decision more based on their guts or are they going to like to pour over all the data? You're gonna have to ask yourself some of those questions and just kind of think about, okay, my audience is kind of sitting over here.

I think they're gonna prefer a more summarized format. Generally, if you go away from the technical detail into a more public audience, they're gonna prefer a lot more context and far less jargon. So you have to think about how much technical detail does your audience prefer, and if they are working in a technical space, then maybe that's okay, but even some technical spaces don't understand certain jargon terms that maybe you are very familiar with.

And you. don't even think about anymore. Sometimes we fall under a what's knowledge where you forget it's.

With at your current level of knowledge and forget that other people don't have that. So  I like to, when I'm going to a general audience, whether it's if you're communicating to a wider group of people in your company, start with the person who has just joined the company. So today, they've just joined the company.

You are presenting to them and work your way into the detail rather than starting with the detail because.

Richie Cotton: You mentioned the idea of explaining technical details. I think this is something where a lot of people tend to get tripped up. Like you've got a lot of numbers technical methodology. How do you go about explaining those Depending for, well, for different audiences?

Kat Greenbrook: I think you have to, first of all, ask yourself whether your audience needs to understand those technical details, because most of the time they don't, especially when you're talking about methodology and technical details, how. That is not really important for the majority of people on the receiving end.

Generally, people care about the results and  what it means for them, rather than any of the detail in terms of how it's been done. So I think that's the first question you have to ask in terms of communicating technical information is whether or not your audience needs to understand it in the first place. 

And then just kind of what I said before about adding more context and removing that jargon. Just really, really being aware of. Knowledge, everybody has it. it's not necessarily, but it's just being aware that it exists and trying to think about reframing what you know, beginner.

Richie Cotton: Okay, so. Really just include, I guess the minimal viable amount of technical terminology that you can get away with. Does that sound

Kat Greenbrook: mean it, depending on how, what your communication is. So it could be a report it could be a presentation. You can always include that technical detail if people ask for it. So reports are set up so that technical detail can live in the appendix and presentations are done. And you can say at the end of it, if  you get any questions, I'll, I can send that.

So you can still have it there if people ask for, don't start.

Richie Cotton: I like the idea of using appendices. It's like probably no one's gonna read them, but if there's that one annoying person ask you the questions, then yeah, you, you've got the evidence to, to back up your arguments. Okay. That seems pretty cool. I. Alright. And just while we're on tailoring things, I know in your book you've got different sort of archetypes of stories.

So you've got I think you mentioned the one about like character data story and what about a time data story. Just talk me through what are these different types and when you might use them.

Kat Greenbrook: The two main types in which you would actually contrast data measurements. So data stories are built around how you can contrast your data measurements. So a time data story uses time as a way of contrasting these measurements. So it, it looks at, okay, last year we had this expenditure, but this year we have this  expenditure.

So we're using the difference in time to contrast that data measurement. Character data stories. Look at how these data measurements are different across different characters. And a character in a data story is not like a fictional character from a book. A character in a data story is generally whatever the data is describing.

And so it can be a company, it can be a product, be an animal it could be a person. So a character data story, character data, story contrast, looks at. Did this this year, but our competitor did this. And so we're using different characters to contrast our data measurements and contrast in, in storytelling in general is very important.

Stories are built around contrast and data. Contrast is just another way in which we can add that contrast into our storytelling.

Richie Cotton: So moving on from tailoring to structure, I think like there are a lot of kind of standard story structures, so there's like the  hero's journey and all these kind of things. I'm not sure which ones of these are applicable to like a business analytics scenario. What sort of structures do you think work for data stories?

Kat Greenbrook: Yeah, basically, I mean, any story structure can work for a data story. I, I'm familiar with the hero's journey. I think it's a bit too involved for a data story, especially the data stories that are used within business as just a way of general communication. They don't have to be that detailed. They don't have to be that hard to do.

I.

Which is of structure. Make sure. Include those three acts in terms of how you present information. So making sure that you have your act one there to set the scene, to provide that context. You have your Act two there. Generally it can go into what is the problem we're solving, what are we banging up against?

What are we finding hard? What's our struggle? And then act  three, this is what we're gonna do about it. This is our conclusion, our resolution, whatever it is. But those three acts hitting those points. Creates a, a full story. It's not just a case of sharing random numbers without a structure, having a story structure in place is the backbone of your data storytelling. 

So yeah, there are really complex structures out there. The hero's journey, one of them I just don't think that amount of detail is necessary for business communication.

Richie Cotton: So I like sort. So lemme see, there was scene setting and some sort of. Tension or problem, and then some sort of hopefully happy ending, I guess, in act three can you talk me through like a, an example of how the three act work for a particular use case?

Kat Greenbrook: I can give you an example of how it works just in general storytelling, and I can give you an example of how we in data storytelling. I've got an example here for general storytelling, and I'll just, read it off. ' it's an and, but therefore, so they use the words and, but, and therefore to define  those three acts.

And see if you can guess the movie. it's based on a movie plot line, so I've made it quite simple. It starts with Ariel is a young mermaid princess who dreams of exploring the human world. And after she rescues Prince Eric from a shipwreck, she falls deeply in love with him. Her father, king Triton forbids any contact with humans.

Desperate to be with Eric. Ariel makes a deal with the See Witch Ursula who grants her legs in exchange for her voice. Therefore, Ariel Embarks on a quest for love and her life in the human world.

Richie Cotton: Alright. Yes. This uh, Ariel, the, uh, little Mermaid is uh, I like that cartoon. When I was a kid, it was like uh, ginger protagonist. Uh, It's good stuff. Go on. 

Kat Greenbrook: So, so those are the three acts. And so the words, the word and helps join together statements in the first act to provide that context. So they're all the statement and is the most common agreement word. They're all these  statements that you join together in the first act. They're all in agreement. They're all helping to set the scene, introduce the character.

And but introduces the second act because it's a contrast word. So it's, providing that tension between act one and act two. So when King Triton forbids any contact with human, that's, the tension of the story and therefore helps resolve the tension that was introduced in Act two. So we're introducing act three with the word therefore, and continuing the story on in a way that's going to conclude it.

And we can do the same with our data stories, just. By using these three words, and I'll give you a, a very, very simple example, but you could say, last year we had 20,000 customers and how the large proportion of the market share, but this year customer numbers dropped 25% because of increased competitor.

Therefore, we need to think about how we're gonna win back our customer.  So that's an example of data story. I don't think it has to be as involved as the, the hero's journey structure. It can be simply these three acts, but making sure that we have something for our, for each act will help us to tell a more balanced story.

Richie Cotton: And this sounds like it's gonna feed naturally into that understanding what your problem is from the, the previous PGE AI framework. We, we've talked about,

Kat Greenbrook: Yeah, so having, having an idea of the problem that your action is actually solving will definitely help in terms of how you put that story together, because it can weave through some of that context for your audience.

Richie Cotton: do you always want these three acts or do you ever want to like break your story down into smaller building blocks?

Kat Greenbrook: So I always start with, the three acts, and the more general you can make it, the better, because then you can start to go deeper if your audience needs a little bit more detail. And so I call these  nested stories or stories within stories. And so if you have. A problem statement for your second act, and you find your audience is probably gonna need a little bit more information about that problem more than just maybe a sentence or two, then you can actually create another a BT structure, a story within a story that just supports or goes into more detail about that problem.

And so this is how you can flesh out your high level stories, but still keep them engaging so you're not just throwing a whole lot of information at people.

Richie Cotton: Okay. Stories within stories, I can imagine this could get quite complicated with some sort of inception type situation. Lots of layers but yeah, oh, I guess at some point, like three sentences worth of story isn't gonna be enough. For some people when they want more detail.

Kat Greenbrook: Yeah, you'll be surprised though, because I think it's, it's actually enough for a lot of people. be times when you obviously have to create more detail, but for the majority of situations in a business context, you can get away with just that high level story. 

Richie Cotton: Yeah, like presenting to the whole company about like whatever is. Most of the company only really wants to have a very, like, they only care a little bit. So just having a high level overview is enough. Alright. I guess the other thing with stories is how you make sure that the story is an honest representation of the data because it's very easy to kind of, massage the, the results to fit your story rather than the other way around.

So how do you stay honest?

Kat Greenbrook: Yeah, and this is a good question because storytelling I think, gets a bad name. In terms of people don't trust it because they do think of it as something that is made up, something that people use to manipulate other people, and that's absolutely true. people do make up stories and people do use the engagement of stories to manipulate other people.

There's, I'm denying that, but I think.

Present facts, present honest data,  then it can be just as powerful in resonating and influencing them. But there does come ethical decisions and things that you have to make along the way if you call yourself a data storyteller. So you have to make sure that your source is reputable. You have to check your own biases.

So are there conflicts of interest that maybe you have in terms of how you interpret the, the data and how you communicate that data? It to understand any assumption that you're making. So assumptions are make

under. Why you've made them. So looking at correlations versus causation data accuracy, have you made assumptions around that, that maybe just writing down will help you actually readdress and think, okay, is that right? Or even just asking another person, do you think this is right for me to have made this assumption?

And also when you're writing a story,  ask yourself what is the counter story and is. Because data can tell different stories based on the context in which it's examined. So you could be looking at a specific number, and if you compare that number to a higher number, it seems lower. But if you compare it to a lower number, it seems higher.

So you can kind of shift how people respond to that particular number that you're sharing, whether it's in a good or bad way sometimes. So you really have to. Yes, I'm pushing this particular narrative, but is another narrative also true that could contradict this one? And you have to be comfortable with data holding multiple truths and how you communicate.

That can sometimes be quite difficult because you have to communicate those multiple truths.

Richie Cotton: Oh man, it was, it was starting to sound quite simple, like you just need to make sure that you've got these checks in place to make sure your analysis is kind of, correct and that your conclusions are valid, and then just try and communicate as accurate as you can. But then the idea that you've got multiple truths within data, so you might have to  communicate things in a different way.

Actually, first of all, do you have an example of this and then how do you go about communicating like these different sort of truths?

Kat Greenbrook: Yeah, I think the different sort of truths I don't have a concrete example in my head right now, but it could be just in the perception your audience has of particular data. And so you could have let's say the dairy industry. You could have people from the vegan side of things and who see the truth of the dairy industry in a very negative way in terms of environmental damage.

You can see another camper, people looking at the dairy industry and thinking, thus supplying food to the world. This is a very positive thing. Both of those things can be true, and it's finding a balance between. Necessarily just show one side of that story because , that's not the whole truth.

You're just picking a sliver and I mean, that's a pretty general problem. But you could have more  detailed situations where if you just picked one side, it's not a true representation of that particular thing, whatever it's that you're looking at. So I just think the world is as and white as people think.

It's a lot gray in there. as data storytellers, we have to get comfortable in.

Richie Cotton: Okay. Yeah, I can certainly see how anything that's gonna be like remotely political that's gonna require some sensitivity in terms of like how you balance your story beyond, like just pure throwing in numbers. And I suppose that even applies to like business context. There's always some kind of business politicking going on.

It's like. Marketing attributions always something people, like different teams argue about, like who, who gets responsibility for bringing in customers? Okay. So yeah, I can see how, if the story sounds like it's gonna cause conflict between teams, yeah, you've gotta be a bit sensitive there in terms of your framing.

Does that sound about right?

Kat Greenbrook: Yeah, and I think it's, not necessarily about trying to be political in the way that you.  You communicate and you're not trying to please everyone, but you don't wanna sensationalize necessarily things when you only share one side of the story.

Richie Cotton: Okay. Alright, so, I guess just watch out for your phrasing and maybe, I guess get someone to check in before, before you start

Kat Greenbrook: Yeah. I mean, running, running, whatever it is that you're communicating with by other people. Just, just to get a sense check is always a.

Richie Cotton: Yeah, that, that seems like a, a wonderful idea. I guess more generally, do you have advice on how to make use of data stories at work? I guess like, in meetings that's like a common place of sharing information around data. How do you tell a data story in a meeting? I.

Kat Greenbrook: Generally when you have a team meeting, maybe it's a weekly team meeting and you kind of all around table, and you may have, you may not, but. The manager of the team and sometimes people will kind of talk about what they're doing.  Sometimes they won't. Sometimes a lot of people will sit there and be silent for the whole meeting.

I used to hate those team meetings, but they're a great opportunity for people to start hearing data stories within a team. So I do encourage a lot of teams to have a people in each meeting. For different weeks, but they each bring along a really simple data story and they just read it out to the team.

And it can be a couple of sentences and it can be about the work that they're doing. It could be about a situation that they've come across within the business. It could be anything. And this is just to. Give people the opportunity to practice because sometimes we get so caught up with the work that we're doing, and if that work is very technical, you don't necessarily get a chance to practice your narrative writing and your narrative storytelling.

And so giving people that opportunity, and it really doesn't have to take long, especially when you're writing these and, but therefore it's, they are just a couple of  sentences. And if you're not comfortable presenting in a meeting. You just have to stand up or read out a few sentences, and it can be really daunting to start with, but it's giving everyone in that meeting, not just the people doing the storytelling, but everyone an opportunity to build their narrative intuition.

Because the more you start writing stories, the more you start hearing stories and you start to recognize patterns and the framework that's being used it'll just make you better and better at it yourself.

Richie Cotton: Yeah, so you mentioned team meetings. I have to say I find it the boring bit of team meetings where you go through like all the kind of quarterly targets and it's like, Hey, we're. 53% of the way to target is like, great, is that good or bad? You're not quite sure. Having a bit of narrative around that has gotta spice things up, I think a little bit.

So it's gonna make your, your weekly team meeting a bit more informative and a bit more engaging, hopefully. Okay. So how about other forms of communication? Because I think a lot of business communication tends to be written, so things like  emails, slack messages, all that kind of thing. Can you do data stories with these sort of short form written communications?

Kat Greenbrook: Yeah, absolutely. So whenever I'm putting together a data story, it always starts out in the written form before it gets any design added to. If you start adding the design before you understand the message, it becomes very difficult to make that design good. So all of my data stories start off in their written form first, and they can be sent just like that.

So you can write a data story, email it to someone, or put it on slack, whatever it is. But that's the most basic form of a data story. Start thinking about how you're going to tell that data story. , sometimes that's just written form and gets emailed, but most of the time that incorporates some sort of data visualization. 

Whether that's in a form of a presentation or a report, whatever is best suited for the audience, because that's really what you've gotta consider. How does my audience  prefer to receive this information? And if that's in an email, that's great, but if that's in a presentation, then you have to think about, okay.

How I presentation together, what visuals am I gonna design that any design.

Richie Cotton: I guess the other extreme of that is something like a dashboard where it's, it's predominantly visuals. Can you tell us data story with a dashboard or it need to map adaptation Then.

Kat Greenbrook: There's different views on this. I don't think you can use a dashboard to tell stories. I think dashboards are great tools to find stories in or find the beginning of stories in dashboards are tools that. They present information in a very, very way, so they make it very easy for people to see the latest measurements of the.

They don't give you that context. They don't necessarily  change the way in which they show that data to certain audiences to make sure it resonates with them. So they're very, very much designed for people who already understand the meaning and significance of whether a number is high or low. the audience already has.

Dashboards are a form of data communication, but they're not a form of data storytelling, just like data storytelling is a form of data communication as well. It's just designed for a different audience. So I think dashboards definitely a place to find the beginnings of stories, but they're not a place to tell those stories.

Richie Cotton: Okay, that kind of makes sense. If you've got a load of charts there, then there's no sort of implicit story like I was thinking. Like if you've got I dunno, your company's like monthly revenue, hopefully the person who's looking at is gonna understand whether that's a good number or a bad number.

You don't necessarily want like, text within the dashboard to explain it. And does it make a difference if your story is being  told like directly to a person or if it's asynchronous? So I mean, we talk about like email or whatever that's asynchronous communication rather than the face-to-face chat.

Does that affect how you create your story?

Kat Greenbrook: Yes, I think so. So if you know that your audience is gonna be there, so say you're doing a presentation, you can design your visuals to. Not necessarily tell your whole story because you are there, you're going to be presenting, you are gonna be doing that part of the process. Whereas if you know your audience is not gonna, is, you know, you are not going to be there with your audience when they read your story.

For instance, a report, you're going to have to include a lot more. that meaning and context into the narrative in which you write down so your audience can have that understanding. When I design visuals in a presentation, they include very little in the way of words because I'm there to explain it and I want to walk them through the visual or  walk them through the information as I.

Obviously very different to the way in which I would design visuals if I'm not there to explanation.

Richie Cotton: Okay, so I'm kind of convinced data stories are a good idea. You might, you might realize that, but if you are convinced that data stories is a good idea, you want all your colleagues to try this as well, how do you get them to make use of data stories?

Kat Greenbrook: Understanding what it means to tell a story. So make sure you're all on the same page in terms of what you think data storytelling is. And there are different definitions of data storytelling. I've talked about what I believe data storytelling is, but you'll find other people have have different ideas about that.

And I think this is such a new field that it's okay there multiple definitions of what it means.

In terms where it's going, and it's  okay to think differently, but understand what, you agree data storytelling is first, and then practice on each other just talking about, sharing in a team meeting. you don't have to go to a team meeting to do this. You can each pick a movie and come, come in the next day and write a story about the plot line of that movie that is practicing using this story structure and practicing building that, that narrative intuition.

And over time it will become more intuitive for you to do this. And so start with something really easy. it doesn't have to involve data, but same storytelling skills. For a data story exists for a story, so it doesn't really matter what context it's in. Just practice using that framework and getting comfortable thinking in story.

Richie Cotton: I did, like, I did take a movie. You've seen try and convert it to that and, but therefore structure and yeah. it's good practice for telling your data stories later on. You know, I was trying to avoid  talking about joint, I just episode, but I'm getting withdrawal symptoms, so I've gotta ask um, how has ative AI affected people's abilities to tell data stories?

Like, how can you use it to help you here?

Kat Greenbrook: I think it, it's very powerful and it's only gonna continue to be powerful, especially in this space. I'm not naive to think that it's not gonna one day be able to do what I do. I do think. Some sort of AI will come along and be able to tell the stories of our data. It's a question of , whether we let it.

But I think that, we'll, I think it's probably gonna happen after it automates or takes over the analytics space first. I think that's kind of. The easy pickings almost for, AI to come along and, and start to help us out in terms of how we uncover those insights. I think that's already happening.

So there's no reason to think that it couldn't flow on from that. And then help  us to tell our stories. I don't know if it's gonna be able to incorporate all the context we need it to, because obviously what we.

Ourselves and being able to.

That involves having, I think, a little bit more knowledge on how our work is contributing to the business, what problem we're solving, really going back to those basics so we can direct AI in terms of how it can help us communicate the results of what we found or what we've jointly found with analytics.

So I definitely think it's going to play a massive part in the whole data space, and that is quite exciting. But it's much.

Let it do by itself.

Richie Cotton: Okay. Yeah. So, there's a very good point that you are always gonna understand, like the context of your audience and, and the problem better than the ai.  But beyond that, it's very good at writing sentences, so it's gonna have some benefit for, creating the narrative. cool. 

so, do you have any final advice for how to tell a best data story?

Kat Greenbrook: I. So a lot of the time when we communicate our work, we're super proud of it, and there's nothing wrong with that. But we have to, it's less about, it shouldn't be all about us, it should be about the wider context, the wider problem, the wider impact that we are creating or our work is creating.

so having it be less about us and more about the value. That we hope it will create. That's, that's how it's going to resonate more. people don't care how we've analyzed the data. They care about what we found. And so we really have to lose the idea of I have to communicate all the important things that I know in order to be seen as valuable because.

That's just not the case. You have to communicate what you have found from using your  skills, obviously, but how that is impact other people and having that understanding of the business or the working with just helps to ground that.

Richie Cotton: It's interesting that knowing when to shut up is an important part of uh, story. Also a problem for podcast host as. Alright, wonderful. So, just to finish, I'm always on the lookout for people to follow. So whose work are you most excited about at.

Kat Greenbrook: I actually, I've just enrolled in a university paper. I, I have taken a psychology not psychology, a sociology paper this year because I want to, I kind of, I'm a bit worried about the future of data, I think. With what is happening in the world right now, there are massive decisions going on, massive decisions being made that have used data in a manipulated sense, or they've just  completely disregarded data, and data hasn't been considered in how that decision has been made.

And so I'm just a little bit worried in terms of how important data is going to be moving forward and I think the trust that we once had of data it's starting to erode a little bit and it's losing some of its social license so. I'm most excited about learning more from the sociology point of view in terms of how societies work, how people allow things like misinformation to spread through societies. 

I think having an understanding of that as well as the data side of things, because I still think data communication is a very important.

But I also think it can't be the only part moving forward. We have to have this understanding of people and how we work as a society and how power works and how inequality  comes to play. So I think for me, that's a real focus for me this year, and I'm super excited about learning more about that.

Richie Cotton: Okay, I was gonna wrap up there, but you've just terrified. With yeah, everyone's losing trust in data. Okay. So, do you have any insights yet from, from this new sociological learnings? Like what do you need to do to bring back that confidence in data?

Kat Greenbrook: I am, I'm still trying to work that out. So I had my, my first lecture yesterday and the first paper I'm doing is about social inequality and just learning about these different ways in which we look at society and how some people in society have been Left out in terms of left behind all these different terms that I've never been exposed to before.

So don't ask me to summarize it in any way. Um, It's very, very early days, but I'm just, I'm very excited to be learning more about that.

Richie Cotton: There's definitely something to be said for just learning about a complete new field  and getting a different perspective on reality, and that's gonna just broaden your ability to. Critically about things.

Kat Greenbrook: Yeah, I think so. one of the things that we did talk about in the lecture was actually figuring out your social position. So where you are in society, you're probably going to be surrounded by a lot of people who are very similar to you. so there's this such thing called social distance, which is.

Not the social distancing we talked about during covid, but social distancing. If the bigger the social distance between you and someone else, you know, you're probably not going to have a lot of things in common because you, you tend to hang out with people who are very similar to you. So just having a realization of where am I sitting?

Why am I not you talking or interacting with this person over here?

Think about empathy differently. It helps you consider somebody else's perspective or point of view based on their experiences because they're so different to yourself. And this is really important with storytelling in any aspect. Because we have to  understand our audience, we have to respect the people that are represented in our data.

so having and building empathy in some way for different audiences is very important.

Richie Cotton: That seems like incredibly useful advice. Just think about like how different people are from you and start talking to people who are very different from you. Mute. Okay. So that's homework for everyone in the audience. Then go and sit with someone new at lunch tomorrow. Yeah. It's gonna benefit you and the other person I think.

Alright, wonderful. With that we're gonna finish. So thank you so much for your time, Kat.