The art of data story telling – Kat Greenbrook

Apr 24, 2019 | AgileData Podcast, Podcast

Join Shane and Blair as they chat to Kat Greenbrook on her experience doing and training teams on Data Story telling.


Find out more about how Kat helps teams learn how to tell stories using data over at

Want to find our more about data story telling, Kat recommends these:

Recommended Books

Podcast Transcript

Read along you will

PODCAST INTRO: Welcome to the “AgileBI” podcast where we chat with guests or sometimes just to ourselves about being Agile with teams who are delivering data, analytics and visualization.

Shane Gibson: Hi, I’m Shane Gibson.

Blair Tempero: Hi, I’m Blair Tempero.

Kat Greenbrook: Hi, I’m Kat Greenbrook.

Shane Gibson: Hi Kat, welcome. Thanks for taking the time to join us today. I suppose what really piqued my interest was this idea of data storytelling. So we do a lot of work in the AgileBI space, we’re working with data. We’re working on analytics, we often talk about visualizations. But what we find is visualizations are synonymous with a dashboard. And I listen to a lot of Agile podcasts around the world. And when I hear podcasts about storytelling with Agile, what I tend to hear is about presentation skills about way of people presenting their story verbally at a conference or to the team, their coaching or to the stakeholders they’re working with. Whereas from what I see you do you tell about how you can tell the stories with data when you’re not actually there to verbalize it to wave your hands to give it the emphasis. So really keen to talk about that today. But before we get going, why don’t you tell us a little bit about your journey to how you got to where you are now with data storytelling and route penguin?

Kat Greenbrook: Sure. How far back do you want to go?

Shane Gibson: Back to the 20 years, so whatever you think’s interesting for the audience, or more probably for us?

Kat Greenbrook: Well, I graduated university with a Science degree. Back then you couldn’t go to university and get a Data Science degree or an Analytics degree or anything like that, that just wasn’t done. So I came out with a science degree in genetics, didn’t want to work in genetics. So fell into business, I started off in a customer service center, because it was the only role that I could get with a science degree, and worked my way very fluidly into analytics roles. And so my journey in this whole data space just kind of went from there by accident almost. So I’ve done a lot of different analytics roles from reporting through to insightly more predictive modeling, worked in New Zealand and overseas. And I kind of got to a point about 10 years into my analytics journey, where I was just getting really frustrated with the lack of action off the back of any of these outcomes or outputs. And I had in my head that data visualization was a solution to this. So for me, at the time, I thought data visualization would make analytics more engaging, which would lead to better decision making off the back of it. And so data visualization then became the next step on the pathway. And I went and studied digital design to help me with that kind of side of things. Some of my early data visualizations were absolute crap. And I can say that because I did them.

Shane Gibson: It’s simpler, even I can do today.

Kat Greenbrook: And study really helped in it to fill in my design gaps. But what I found in the last couple of years since starting row penguin is that data visualization only takes you so far. And you can make something that’s really pretty and really engaging, but actually really useless. And so for me, the whole data storytelling side of it is more around how do we effectively communicate our insights. And if you combine that with good data visualization, the deck can help make a difference. So that’s where I’ve ended up.

Shane Gibson: Excellent. So that combination of storytelling and data, so I remember when we’re kids, I used to read comics. I love comics. And I remember working with you quite a while ago, where we took this big idea of taking a user guide with something that was quite complex and turning into a comic, still sitting on the wall, I still love it. For me, the amount of time you took to distill something that was quite complex and make it into something that was so easy to consume as a comic was a lot longer than I would have expected. How do you find that with data storytelling? How do you find that in this modern Agile world, where we get taught that, I can grab a bit of data, I can work it through these magic tools, and I can just get a dashboard in 15 minutes or less, where what you’re trying to do is distill some complexity into something that can be consumed as if it was a comic. So you gonna think about it. How do you find that process? Is that as hard as it’s always seemed?

Kat Greenbrook: Yeah, and we don’t spend enough time on it. And the longer the time you spend on it, the more effective I think it ends up being. And if you’re designing something for yourself, there’s no need to worry about it, you already know that it’s effective for you. But as soon as you start designing or communicating to somebody else, that’s when you’ve got to put in the time. And I actually was having a chat to someone earlier last week, and she said something and it really left me feeling like oh, it really had an impact. So she said that we put in the effort now. And even if it’s 10 minutes extra of our time, what if we combine all the time, it takes all of the people that receive what we do, and say they have to put in 10 minutes extra of their time, and then adds up and adds up. So just a little bit extra of the storyteller’s time actually is quite impactful if you think of how many other people will then have to consume that information.

Blair Tempero: So it’s wondering how you work with your customer on that. Do you spend a lot of time upfront collecting requirements, and in kind of just fleshing it out, what do you bring them along for the journey as a kind of an Agile type setup, where you’re alongside them for the whole thing?

Kat Greenbrook: It works differently with different people. And I think it depends on the scope and the purpose of the output. And so sometimes, if it’s a data visualization, it’s got a completely different purpose, maybe then a report, or something which combines more storytelling. But I’d like to split data storytelling into the writing part of it, and then the visualization part of it. So if you can get the rushing the actual narrative written down first, before you even start to think about any of the visuals and how you will display the data. Because everyone can agree on that first, then the visualization side of it is very easy to do. And I’d like to go back and forth a lot when I’m dealing with clients, just because you don’t want to give them something at the very end of the process and have them take it to pieces because they haven’t been involved.

Shane Gibson: In the beginning think you understand their expectation and then come back at the end and be surprised where they go. That’s not what I wanted. So what I really liked about that you said to me was, you almost do a draft of the key points of the story. So this is the kind of story we’re going to tell. And then you have to really flesh out that story. And as you do that, you go back and talk to them. Do you find though as you say you have your narrative which is telling the story, you want a story to tell and then you go grab the single data. And no doubt at some stage, you find that either the data doesn’t support that story or worse, you don’t have data that can support it or not support it, because the data is just not there for that piece of the sentence or that paragraph. Do you find that happens a lot a little?

Kat Greenbrook: I find people think that that happens a lot. And you’ve pretty much described watch what people are scared of I think around data storytelling is that hold that you are you making up the narrative before you look at the data. But data storytelling, if you’re thinking of the whole analytics process comes at the very end. So you’ve already done all the analysis, you’ve got all that insight. It’s just about arranging it in a way that makes it concise and compelling to consume.

Shane Gibson: So I suppose one things I’ve spent a lot of time working through when I’m teaching or coaching teams around Agile and data analysis this idea of natural language, you’ve been through the beam stuff. And it’s that thing of beauty of when you say who does what, it’s a natural language. I still struggle with what a data story would be? Because I’m still used to dashboards or an A3 roadmap that tells us where we might be in two years. Have you got an example I’m putting on spot here and example in your head of what a data story what a narrative might be just so we have something to frame it with?

Kat Greenbrook: So the difference between a data story with a narrative and a dashboard has got to do a lot with the kind of visualization that it’s communicated through. So dashboard visualizations are very exploratory. They’re more operational and they’re designed for people to explore. So to find the information that they’re looking for, whether we’re an A3 or a narrative data story, it’s not designed for someone to explore, it’s designed for someone to consume. So you’re designing the story for them, rather than them having to explore it to find it themselves.

Shane Gibson: So I think that we’re telling them the answer, but taking them through the beginning, the middle and the end?

Kat Greenbrook: And that’s why it’s important. So a lot of the time when we communicate insights, we might put it in a report, there’ll be a whole bunch of graphs on a page. And people are kind of left to their own devices to choose what’s important to them. And if they don’t understand something in a wider business sense, they may not get the full story or the big picture. So the point of data stories is helped to communicate insight that that does show that big picture, and it pulls in all those aspects of why it’s important. And that why these particular data points or things that we’re finding in the data is important.

Shane Gibson: So if I still think of a data story is like a comic, beginning middle names, like any good story, have you got an example that you’ve done where you could tell us that really quickly, and that narrative is an example of what a data story would read like?

Kat Greenbrook: So it doesn’t have to have a beginning, middle and end. I like to think of it. It’s not a story in the sense that we think story, it’s more just narrative structure. And so we’re applying narrative structure into the way that we communicate insight then becomes more engaging. So there’s a great narrative structure by someone called Randy Olson, who’s a science communicator. And he’s called it an ABT Structure, which stands for and brought to the fore. So I like to think of the N section, you can have something and then something else. And that’s kind of setting the scene and provides a background. So any context, that reader needs to be able to understand what’s coming next, and then the middle section is a problem. So you have something and something else happens and something else that goes on and then you go back, which is a big contrast here. And that’s what engages people, that’s what wakes people up. And that’s where you have this kind of problem statement that you can lay out using data or whatever you’ve got. And then the therefore statement at the bottom just summed it all up and says, Well, what’s next? What are we going to do about it? What does this mean? And it’s a really simple structure, but it provides a lot of it’s a lot better way of communication, rather than just going here’s a graph of this, and here’s a graph of that. And here’s a graph of this and kind of expecting the data to speak for itself, because the data never speaks for itself.

Shane Gibson: Well, it probably speaks in many different tongues

Blair Tempero: That speaks for the person that’s designing it or building.

Kat Greenbrook: So you’ve got to be very careful. I think, is a data storyteller not to put too much of your own bias or the business’s bias even into what you’re saying, because it can get very, very biased in the way that you present information because you were dictating the narrative.

Blair Tempero: Oh, I’ve seen these complex spider web type.

Shane Gibson: I love those new network graphs. Because I think of them as London Underground. I love the London Underground as a visualization. But the reason I love it is that once I have the data in that format, once it’s visualized, I can verbally tell multiple stories. I can say, hey, look, this is what’s happened. I have to be there to verbalize it. As soon as it’s on its own, you’re overwhelmed. There’s too much. You don’t know what to focus on. You can tell yourself your own story about.

Kat Greenbrook: There is a different narratives you can tell from one visualization.

Shane Gibson: So that’s a good point.

Blair Tempero: So we’re sort of moving towards the click stories, using the part of clip that you can actually build it in narrative, but we’re not experts by any means.

Shane Gibson: So when you guys did the public facing stuff that you’ve just done, when you’re using some data or some visualizations to the members of the public. Did you guys take a storytelling approach? Or did you kind of reuse your dashboarding? We know this, we want to count or present?

Blair Tempero: So this was very, very much the first prototype or the first example. So we took something that we already had, and built it into QAP using QAP. But that’s certainly the journey we’re going to go on now that we’ve got clip for public, we’re going to be building a roadmap so that we can start putting a bit more effort into the user experience and in the storytelling, to become experts very quickly or no one look at them.

Kat Greenbrook: It’s interesting, you say that you’re relying on a tool to be able to tell stories, because I don’t think that’s gonna solve the problem.

Blair Tempero: Exactly. It’s the old technology doesn’t solve what people can’t do, but it might make us think about telling stories, whereas the moment it’s pretty much.

Kat Greenbrook: It’s gonna make it easier.

Blair Tempero: Yeah, hopefully.

Shane Gibson: Or worse, because you’ll bring back some visualizations where you could tell four or five stories without one visualization, you’re not going to come back with visualizations one number, you’re probably unlikely to come back with visualization and that’s one line on a bar chart. So if you’re going to bring back at least two dimensions, because we that’s our guesswork, if not three, or four, and then you’re going to try and either you’re not going to write a narrative around the way you could look at it and the outcome that you should get or you’re going to write one that’s so complex with FDNS but it could be that actually, you’re gonna read it and that was overwhelming. Maybe I’m not as good experiment.

Blair Tempero: Very good.

Shane Gibson: What do you do the next round? And then we’ll have a look and feel your retro. Told off you mean, encouraged to review what you’re doing and where you could?

Blair Tempero: Told off, because it’s easy to say.

Shane Gibson: So an Agile coach, very much. So I know you’ve got to move from doing to teaching. And I’m assuming at some stage where if you’re not ready now coaching and mentoring teams, you will be. So anyway, when you run your courses when you teach people how to tell stories, what are you finding the majority of the audience is made up? There’s a BA’s, there’s a cool visualization people, there’s a data transformation gurus, as a BI data teams, or it’s got to be a mix. What kind of standard audience at the moment?

Kat Greenbrook: Definitely max, I’ve had a lot of different people through from engineers to HR people. But majority of them work in BI analytics teams for big companies. So they’re dealing with is obviously quite thick. And they just need help with communicating their insights.

Shane Gibson: Cool. So they’re trying to basically improve the way they behave?

Kat Greenbrook: Improve their effectiveness of what they do. So they’re really, really, really good at what they do analytics wise, they just need a little bit of help with how do they communicate that now.

Shane Gibson: And do you find that that process of telling your stories and data is a team support, or is it a at the moment kind of something that where people tend to do it on their own?

Kat Greenbrook: It could be either, so works well either way. I’ve had times where we’ve had post it notes all over the world trying to arrange a narrative structure and it works well. I guess it’s like anything, the more people you have involved in something, the longer it’s going to take, you’ve got more opinions, you’ve got more people that really, really want their data included, even if it doesn’t have anything to do with the narrative. And so what we find is that maybe there’s a couple of different narratives that come out of those kind of exercises as well which is okay.

Shane Gibson: Once you’ve proven the very first one. So is it more of like a storyboarding technique where you’re starting to use bullets on a whiteboard or a wall to see the different ways you could storyboard that and then focusing down onto which one the earthen slice which one we’re going to do first.

Kat Greenbrook: So I like to have it that it’s really high level first. So have a narrative structure, that’s just an elevator pitch. And then you flesh it out as you confirm those, so you might have a couple of different elevator pitches. And then maybe the team splits up and goes off and tries to nester the narrative structures under there that one.

Shane Gibson: It kind of decomposing the backlog down into almost tasks, if not user stories. So on there, do you form a persona or an audience that the story is targeted that? Does that something that you kind of craft around it to help focus on the type of person that is going to consume the story, therefore, the way you tell them the way you visualize the data to support it is different?

Kat Greenbrook: Absolutely. So that comes before you even get to the narrative. So for me, data storytelling is three important things. So messages opposite background, which includes narrative in data. But you’ve also got purpose in voicing our audience. So it is a form of communication. So any form of communication needs to have a purpose. Otherwise, what are you doing? There’s no point, trying to communicate something if you’ve got nothing to say. And so you’ve got no purpose. And if you don’t understand your audience fully, it doesn’t really matter what you’re saying if you’re communicating it in the wrong way.

Blair Tempero: So these are the important questions you asked the customer upfront, obviously?

Kat Greenbrook: Some of it is just going through certain exercises trying to understand audience better and it may be that you’re just guessing, which is fine. But you’re still putting yourself in a position to have a little bit of an opportunity to have empathy for someone else. And so audience understanding is really around thinking how would this person like to be communicated to because this person is not me? A lot of the time we communicate things in the way that we want to be communicated to. And so if you’re an analyst and you’re loving a lot of detail in the way that you’re communicated to, you tend to push that detail onto someone else, whether they like it or not.

Shane Gibson: And I suppose the other thing you’ll find is that when you say, who’s the audience? That will go everybody. Oh, no, no, no, everybody in the company and so that it’s easy. I can opt out despite going everybody that I have to think about, I don’t have to make a decision like that, I have to take a risk that I’ve got the wrong audience or communicated the wrong way.

Kat Greenbrook: And the thing is, if you try and please everyone, you end up with something so generic, that it’s not fit for purpose, any of purpose.

Shane Gibson: So when you talked about and, and, and, and, and then is an action? So years ago, we kind of worked on this idea that there’s no point building an analytical model, if you didn’t have a hypothesis about the action you’re going to take. So you talk about what an analytical model that’s going to do cross sell and why are we doing this? Well, the action we want to take is offer a person something else, and hope they buy it. So that way helped us kind of refine the model so that it was small enough, Agile enough to just answer them, we could build later, but we weren’t boiling the ocean. Do you in your way talk about storytelling, the therefore seems to me to be I’m inferring at the moment that it’s an action. So at the beginning, you’re saying, what’s the narrative? We want to talk tell? Who are we going to tell it to and what action would we expect them to take as a result of that narrative of successful, is that did I get there right?

Kat Greenbrook: It can be an action, doesn’t have to be. So it’s just a structure. So therefore, could just be reaffirming what you’ve already said. That therefore could be before we need to look into this further, which I guess is an action. So it doesn’t have to be an action.

Shane Gibson: There’s not an action, what’s the point apart from was pretty cool about my website?

Kat Greenbrook: Well, therefor could say you’ve got a problem.

Shane Gibson: Sorry, I just on the spot now that’s why we doing AgileBI podcast.

Blair Tempero: What about therefore we accept that, nothing’s gonna help this.

Kat Greenbrook: You’re there for could backup your hypothesis, the beginning, you can say so. So therefore, we were now thinking that the database are.

Shane Gibson: So before the actions we’ve taken based on that data and are supported by the narrative, okay, I like that.

Blair Tempero: I like to put on the story cards that we put up there.

Kat Greenbrook: The user stories of our audience understanding exposes.

Shane Gibson: Oh really, tell me about that.

Kat Greenbrook: Another time, when you’re designing something, as you said before, you want to design it for everybody but that doesn’t, as we’ve talked about. So I get people to write down all the possible different audience groups that that could be interested in something like this. And that we end up with a whole bunch of posts, it’s all over the room. And then for each of those audience, try and get people to write as many user stories as they can about those audience groups. And what we find from that exercise is, you’ll get certain audience groups that you know a lot about, and you can easily write user stories for. And most of the time, this audience group ends up being your, your primary audience, it’s good to be aware of everyone else who could be interested in what motivates them. But at the end of the day, you’re designing for a primary audience.

Shane Gibson: That’s cool, because that’s bringing that whole Agile technique that as we learn how to do new things, we should try and isolate the risk or the change or the things that are hard in other areas. So if we picked a group where we didn’t actually know a lot about them, we couldn’t articulate a user story and then we use them as the way of practicing and learning how to do data storytelling. They were naturally having to learn two things, what do they want? What do they need? And how the hell do I do this process by isolating it down to things where you actually know quite well, then we’re just focusing on that data storytelling. So I’m stuck with user story land as I want. So typically most of the data BI and analytics teams I work with, we get down to a relatively technical user stories by natural, if you got an example of what a user story would be for you. And they’re kind of in that framework?

Kat Greenbrook: So I do exercises in my workshops around climate change data, it’s a very big topic and involves a lot of different audiences.

Shane Gibson: And some of you are passionate about?

Kat Greenbrook: And some of I’m passionate about. And so we go through an audience brainstorm exercise and a lot of the time the groups are quite big. And so what I like to do if we have like a general public audience is split it into what are they believed they believe in climate change, or do they not believe in climate change? And so writing user stories for those two different groups, you’ll have quite different user stories. And it goes back to actually understanding the purpose of why you’re communicating to this group. So I give them the purpose or purposes to get people to change what they do. Through against a near the negative effects of a changing climate. So get people to change what they do to counteract those effects. It’s not to get people to believe in climate change and that’s the important thing. So purposes don’t change when you’re communicating something, but the message can be tailored to the audience. That’s why it’s so important to understand the audience before you start writing the message. So writing a user story for someone who doesn’t believe in climate change, it’s never going to mention the word climate change, versus someone who is does believe in climate change. You can mention it a graph government, but it’s realizing what motivates people. And that could be really simple things like a user story for someone who doesn’t believe in climate change could be as a climate change nonbeliever. I want New Zealand to reduce their fossil fuel use to keep out so that New Zealand can keep its clean green image. So you’re still design designing for that purpose in mind, but the way that you communicate to that person is not going to mention the way climate change go.

Shane Gibson: And then the narrative and the data has been to support that user story, and encourage that behavior. It’s something you talk about, you can’t use word climate change for people that don’t believe in climate change, number of times on coaching a team where we have to use the word attractive or a new way of working or Agile bi way, or iterative bi way or new way. Because somebody’s experimented with doing stand ups on a daily basis, haven’t changed any of their other behavior. And therefore, I’m using my fingers with. They’ve tried Agile and failed, so it’s interesting. So when we bring out this, this lovely world of data science and all this, this new Nirvana that automate our lives, what I found is that the first part of data science when we started that journey, we moved away from teams to teams of one, we had the unicorn, this was a person that I articulate could be a business analyst and engage and understand the narrative of the story that needed to be told, could go away and engineer the data in a repeatable way and write the code. And then for the third Trifecta was, actually you could then write the do the annular composites, the science of it off. I have an experiment, I want to prove that is true or not, and then close that out. So the true unicorn, they could do those three things well and repeatedly. What I’ve seen is actually we end up with teams of three. We end up with somebody that or teams or two, where there’s a bit of an overlap, but typically three core skills, the facilitation, understanding, and dealing with people the engineering of the data to see whether the data is made the data available for parties to hypothesis and the testing of the hypothesis. But I don’t often see the storytelling, I’m not sure whether it’s naturally a T-skill within one of those three roles, or actually we just need to introduce their fourth skill. I don’t know, whether the data scientists are naturally producing data stories that have narrative and content that can be used without them waving their hands and saying the words, what’s your experience?

Kat Greenbrook: Data storytelling is a relatively new. I don’t know if you call it an old it. I updated my website maybe 18 months ago, with data storytelling more as the focus service. And I found that I didn’t get as many hits. We weren’t talking about it. So I rebranded to data visualization and made that the focus because everyone was talking about it. And even though it was the same thing, we didn’t understand the concept of data storytelling back then, even just 18 months ago. Now, data storytelling is the term that everyone understands and wants.

Shane Gibson: Do you think in New Zealand or globally?

Kat Greenbrook: I don’t know yet. Well, it was listed as something that teams should train or educate more around the story time field. But it’s never been listed, I don’t think before. So it is a relatively new thing. I think a lot of the time it gets incorporated into data visualization is something that data visualizers would just naturally do or should do, especially if you’re creating more of the infographic kind of visualizations. But the amount of infographics that you say that have no narrative whatsoever, they’re just a bunch of statistics on a page and there’s not a data story.

Shane Gibson: For me, I’ve seen infographics digress or regress stories. They used to take a long time because you were hand crafting them. And I’m assuming that’s because you’re doing the process you’re talking about. You figured out what you wanted to say you wrote the narrative and then you created the visualizations and supported the narrative as more tools coming up with some form of infographic creation. Really, what you’re doing now is just putting a bunch of graphs on the page that doesn’t look like a standard matrix dashboard. You’re not actually crafting the narrative. So I think we’ve lost the out of the infographic?

Kat Greenbrook: Well, I think the more tools that come along that make it easier for you to just put a bunch of graphs on a page and maybe an icon or tool to make it look a little bit more engaging. Maybe there’s less value, and the people that are actually crafting the narrative and spending that time.

Blair Tempero: I’ve seen some really good examples. And probably the best one is during the Brexit, voting where they had just a map of the UK. And just a heat map around the country and no words were needed. It was just, this is where they’re saying yes, this is where they’re saying no, but it’s pretty rare that you say that you wouldn’t need words to back it up.

Kat Greenbrook: Well, I would still call that an exploratory database. They would have made it knowing that really, there’s one big narrative that people are going to see naturally from it. So they don’t need words, they don’t need to show that narrative, just the visuals.

Shane Gibson: That’s unusual, we can distill our data and our hypothesis down to a single use note, or a pie chart with a big slice and a little slice which is effectively what we’re doing.

Kat Greenbrook: Or relying on the audience to have an understanding already of the background and what could contribute to the narrative.

Shane Gibson: Which comes back to right at the beginning, you talked about who are we writing this narrative for? And therefore, you could gauge the level of skill or the domain or the knowledge around the subject, and therefore, you’re telling the narrative to support who they are and what they already know, teaching them something new and not teaching the sucky eggs or making something incredibly complex where there’s no way. Haley had a really interesting one, she’s making sensors for houses that monitor carbon dioxide humidity, and that for healthy homes. She was a product owner, she’s been a part of that for a long time since her company, she took an experiment and failed her, the experiment while she thought she could get the main parts, and then deliver it out to the houses where they could put them together. And that way, she could save money and make it cheaper for them to buy. But she found when she went and tested it there, actually a number of the people didn’t actually know how to use a screwdriver. And so she had to document the [inaudible 00:32:36]. And again, we forget that our audience is not us. We designed for ourselves so really, really important. So the magic that’s coming in terms of all these great tools, these storytelling visualization tools and AI again, where it’s going to look at my data and use natural language magical machine learning and neural machine AI thing. And just tell me the story. Is it coming, or not coming as a hype cycle?

Kat Greenbrook: For different purposes, so I think AI is gonna get better and better and better. But that’s something more I think, when you’re designing for maybe a tool for an operational purpose, incorporating AI into those kinds of solutions works really well as data storytelling is completely separate. And it’s far more communication than it is trying to create a tool or the software from it. So chalk and cheese.

Shane Gibson: So coming back to those Agile principles that conversations are important, collaboration is why we do it.

Kat Greenbrook: We actually have to think for ourselves.

Shane Gibson: I think I used to say, “You still need a brain”. So data storytelling in New Zealand apart from yourself, how many other people would you say teaches or coaches or experts in this domain and the whole of New Zealand?

Kat Greenbrook: To be honest, it’s probably not a lot of storytelling in the way that I’ve described it. And the way of actually including that narrative or writing that narrative first. There’s a [inaudible 00:34:17], she’s from the US. She runs data storytelling workshops all around the world. She’s been doing it for a while, so she’s really big internationally in this field. It’s almost niche audience for this as well. So data storytelling is going to be really beneficial for people who are in need of deep data and have to communicate their insights that they find to a wider audience. If you’re thinking about it from a brand or a company point of view, trying to communicate to the general public it would be quite different.

Shane Gibson: So you think that the idea of the way you teach data storytelling the way you teach it that you need to understand the message you think you want to tell, you need to understand the audience you’re going to tell it, you need to craft a narrative but it’s a process. And it’s a process that any team that deals with data analytics, or BI visualization, that process itself is really valuable. It’s a process of iteration. It’s a process of collaboration. It’s a process of learning fast, you’re not actually going to do what you what you committed to. I just hit a whole lot of Agile techniques, even though you never use the word Agile but that doesn’t plus it and the way you’re behaving in the way you’re coaching. So for me, that’s cool. It’s just a reuse of better ways of working. But on a whole new area, which is a narrative that has been around for a long time, but undeserved.

Kat Greenbrook: I think any new discipline that comes along, it kind of incorporates a whole lot of things from a whole bunch of other different disciplines, and then creates its own.

Shane Gibson: I think to me, that’s the key word. I think data storytelling is becoming a discipline. So we may end up talking about data analytics, visualization and storytelling as a forth discipline. Because at the moment, I don’t think it’s seen as a discipline but it may be coming that way. Hey, look, thank you for your time. That’s been pretty awesome. I know that the company started rogue penguin, you’re kind of doing some really cool stuff do you want to kind of close out with just tell us a little bit about what you and other guys do?

Kat Greenbrook: So rogue penguin when is shown a half years old now, set it off is offering a range of very broad services and through from graphic design to analytical model. So it’s kind of been narrowed down and the service offering based on watch what the need was. So data storytelling has come out is the one challenge I think that a lot of analytics teams have. I like to work from the point that I don’t want to do the work, and then leave, I want to teach other people how to do this so they can carry on doing it. And it’s not going to be something that everyone wants to do, but I think if you can get a team educated on the basics then you’re going to have those champions come out within a team. So what I finding is that if I go in and do training within a team, there are going to be a couple of people come out as data storytelling champions are really, really interested in this kind of thing. And so then I work with those people on a mentoring one on one basis. So half of the business is geared around workshops, so I’m running them across New Zealand at the moment. And the other half will be me actually doing being involved in those client projects and doing more data storytelling and visualization work.

Shane Gibson: Excellent. And if someone wants get ahold you after this podcast and have a chat?

Kat Greenbrook: It’s pretty much get me on any social media channel or go straight through to the website

Shane Gibson: Do figure out how to put those contact details on the podcast when we publish them, or add those to their podcasts. So they’re the backlog story for me at the moment that I haven’t quite worked out yet but not a priority yet until we do publish. Thanks for your time. It’s been freakin awesome to catch up and hopefully we might get you back on in six months or years’ time when Gartner Hype Cycle data stories, the online training course and you keynoting around the world and it becomes actually a discipline that everybody’s doing as they should. Thank you very much.

PODCAST OUTRO: You’ve been listening to another podcast from Blair and Shane, where we discuss all things AgileBI. For more podcasts and resources, please go to