Once upon a time, there was an analyst. And that analyst had some data. She used that data to do some analysis, and from that analysis she realized she had some recommendations she could make to her organization. This was the point where our intrepid analyst reached a metaphorical fork in Communication Road: would she hastily put all of her thoughts together quickly in a slide deck with charts and graphs and bullets, or would she pause, step back, and craft a true data story? Well, if she listened to this episode of the podcast with presentation legend Nancy Duarte, author of five award-winning books (the most recent one — DataStory: Explain Data and Inspire Action Through Story — being the main focus of this episode) she would do the latter, and her story would have a happy ending indeed!
00:04 (Announcer): Welcome to the Digital Analytics Power Hour. Tim, Michael, Moe, and the occasional guest discussing digital analytics issues of the day. Find them on Facebook at Facebook.com/analyticshour and their website, analyticshour.io. And now the Digital Analytics Power Hour.
00:24 Michael Helbling: Hi, everyone, welcome to The Digital Analytics Power Hour. This is Episode 130. I’d like to tell you about someone named Sheila. She works at a very large company as a digital analyst as you might imagine she’s responsible for a lot of data reporting insight generation on a day-to-day basis. And Sheila’s has been increasingly frustrated in her job. See, she produces all this stuff and despite her best efforts, the recommendations for how to advance the business are being ignored, in meeting after meeting. Finally in frustration, Sheila took the step of responding to a recruiter on LinkedIn and found a really great new job. Only a few months into the new job. Sheila is starting to see traces of the old and the new. With all of her experience in statistics and digital optimization she can’t seem to break through to the leaders in the company so they’ll act on what she’s finding. Alright, so Sheila’s not real, but her story gets repeated again and again in digital analytics. Moe, did you connect with Sheila’s story?
01:33 Moe Kiss: Well I did, because with a name like Sheila, she must be from Australia.
01:38 MH: Well, I actually looked up the connection between Sheila and Australia, because after I heard the name, I was like, that’s an Australian thing. I better make sure it’s not bad or something.
01:47 Tim Wilson: Well, we’ll gonna be talking about researching our stories later here, I can tell.
01:50 MH: Oh, well, there you go. So what about you, Tim? Did you ever live this out?
01:56 TW: I have. I empathize… I sympathize? I connect with Sheila.
02:01 MH: Yeah. And in my career I have been a total Sheila. Definitely.
02:05 TW: I’ve said that about you many times.
02:07 MH: Yeah.
02:08 TW: Don’t be a Sheila.
02:10 MK: Oh gee.
02:10 MH: Okay, so combining data and storytelling is extremely powerful and that’s what we wanted to talk about on this episode. So we turned to one of the best that there is in helping make that connection. Nancy Duarte is a communication expert who’s been featured in Fortune, Time Magazine, Forbes Fast Company, Wired, Wall Street Journal, New York Times, Cosmopolitan, LA Times, and CNN. Her firm, Duarte Inc, is the global leader behind some of the most influential visual messages in business and culture. As a persuasion expert, she cracked the code for effectively incorporating story patterns into business communications. She’s written five best selling books, four of which have won awards, and today she is our guest, welcome to the show, Nancy.
03:01 Nancy Duarte: Thanks for having me, you guys. Hi, Sheila.
03:05 TW: This one’s new. So has it just not really had a chance to win an award yet?
03:10 ND: Well, it just did. I should have maybe said something.
03:13 MK: Oh, wow.
03:13 TW: Oh, now five for five.
03:15 ND: So on Friday it won it’s award, its first one. It’s one I had never even applied to… So my publisher… Yeah. Anyway, yeah, so that one’s flying now. [chuckle]
03:23 MH: Well that’s awesome, and definitely deserved. And certainly we’re gonna cover that quite a bit, so to get us started, maybe Nancy, if you could just give our listeners a brief background on what you do and the work that your firm does.
03:38 ND: Yeah, so my company, we’ve been around for 31 years, and we have worked with the highest performing brands in the world. So, for years, we actually would help you write, visualize and deliver a great talk. So we have a service business that’ll help you do that or we will teach you how to do it for yourself. So we take all the learnings and the findings from actually working with real brands at scale, and then we kind of codified it into this training organization, so we do both, we will either do it with and for you, or we’ll teach you how to be a great communicator for yourselves.
04:13 MK: And just for context. I first found out about Nancy from Lea Pica who I saw present when she came to Sydney, and I remember talking to Lea about my desire and my goal to get over my fear of public speaking and Slide: Ology legitimately changed my life. It has sat on my desk ever since. I think every single time I write a presentation now, I go back and I look at it and I make sure I’m not committing any presentation sins.
04:41 ND: That’s awesome.
04:42 MK: Yeah. And it’s funny how many… ’cause now I actually work at a design company. And how many designers pick it up and they’re like, “Oh this is a great book”, I’m like, “Yeah”
04:51 MK: But yeah, I’m really… I’m much more pumped about DataStory. It’s like you heard… It’s like you heard and answered my prayers of what I wanted to address next.
05:00 ND: Yay.
05:01 TW: So do you have people… I’m curious, your organization, ’cause you have 120-140 employees, right?
05:10 ND: Yeah.
05:10 TW: So is it really divided? Is there kind of one side that is the training side and one side that is the services side?
05:16 ND: Yeah, so we have a team that actually will perform the services and then there’s these curators that harvest some of the ideas, turn it into instructional design, programmatize it, and then we test it. We do an Alpha, a Beta, and then we launch it as a new product. For DataStory, I did the work myself, and I dug through thousands of slides, thousands and thousands of slides to get to a couple thousand slides with just data on it. And so we use our own work as a research bed, so if you figure, if we’re working with the highest performing brands, and they send us content, we look at it before they send it to us, and even after they sent it to us, to try to see what, how do they shape it, what did they try to say, what are they actually trying to communicate? ’cause you struggle and you create something that you think is great. And then they retain us to shape it more strongly, and so we use that as kind of our research bed for creating. Some training companies will go to Stanford and say, “Will you take my concept and prove it’s true?” And you have to pay a lot of research dollars to do that, but we just go to what we feel like we know is true and find the findings in the bits of communication that we’ve shaped.
06:26 MK: So with DataStory, your latest book, I suppose, I’m curious to hear, what was the evolution from that? Was it clients coming to you and you saw that over time, data was becoming more entwined with what people were presenting and that was not being presented well?
06:41 ND: Yeah. So what originally what was happening is we kept getting feedback that they wanted us to produce a course that is a communication course for the way that analytical people communicate. So they would come to our course that say, resonate, which is a lot based on the discovery I made with that presentation form. And it’s a bit more, they think, for stage talk, and they would say things like, “Well, Nancy, I’m normal and I do normal presentations, and why don’t you study normal people?” and I was like, “I don’t wanna study normal people. I wanna study great people.” I didn’t hear the question behind the question, what they were saying is, “I make analytical presentations, make a course for me.” So what I did originally is I had a whole course built, it was a day and a half but it was really about visualizing data.
07:27 ND: And yet I thought there’s a lot of courses out there like that already, right? We’ve got Stephen Few, we’ve got even tough D in a way, you’ve got a lot of books even written around visualizing data. So when I step back, I brought somebody in, I had all the models and visual, so it was all pasted, like threw it up all over the wall. And this person came in and they were like, “I don’t see what’s uniquely Duarte here. I don’t see it at all.” And man that just spun me around and I was like, “Oh my god, I need to go into our own work and find what is uniquely Duarte.” and I remember I put it like literally this whole wall was just papered in little bitty bitty slides, little bitty like I think I did 24 up slides on 11 by 17 paper.
08:08 ND: And that was like immediately I saw the annotation taxonomy. And I don’t think a taxonomy for annotations over charts has ever been done, like what’s done in DataStory. So right away, I could see what my team did after a chart was plotted, how they overlaid it, and how they drew attention to different things outside of the typical things like color rising and highlighting, how they drew things on top of the charts to really create clarity. And so that was fun. And then I deconstructed all the words that were associated with the charts, I looked at the, what was the noun? What’s the adjective? What’s the adverb? What were all of the words. And then the big kind of aha was what were the verbs associated with the data ’cause that means that’s the action, someone was asking, someone to take because of the data. And that, to me was the most interesting part of that whole research project.
08:57 MK: Yeah, the verbs pace left me spinning for a few days, because I was actually working on some recommendations for our executive team. And I just kept going back to that list of verbs and my write up, and realizing how much and I kept kind of tweaking it trying different… So basically, in Nancy’s book, there’s a list of different verbs depending on what you’re trying to get your audience to do or what action you’re trying to get them to take. And I just kept going back to the list and rereading the sentence using different verbs. And I just don’t think I thought about what a difference it makes to your recommendation, and how purposeful you have to be with your choice of words.
09:35 ND: Yeah, I think that was the big thing was when, once I wrote all the verbs. First I categorized them into four things. And then one of my content, people’s like, “Nope, it’s only two things, and it’s process verbs and performance verbs.” So you have performance verbs, which are primarily what executives care about. And that’s what they’re measured to, KPIs or performance verbs and then process verbs, you could tuck a bunch of them underneath a performance verb and that’s how you get it done. It’s kind of like when you run you could say, “Oh, the verb is run.” But to run you have a lot of micro verbs like swing your arms, breathe in and out, pump your legs, right? Those are all process verbs you do in service of run. And so it’s kind of like that, there’s this performance verb that has a bunch of process verbs you could tuck under it, but you’ll get the attention of an executive if you use a performance verb over a process verb. Yeah, that was so fun. I love pattern finding, I just… It’s so cool to go into this work and find these patterns. It’s just a blast.
10:33 TW: It was interesting, it was fascinating to me that I have long contended that the reason so many presentations are terrible is because we watch so many terrible ones. And even though they’re boring and they’re terrible, and we can’t remember what was said, and they’re not impactful, we’re still learning from them. And then we go and repeat what was done poorly. And you really, with DataStories, is you flipped it on its head and said, “It’s not gonna work, just in kind of theoretical world with what we think should work,” you went in and kind of found stuff that worked, and then did an analysis of that which was actually, you looked through so much stuff. And also a lot of it was influenced by what you had done. So you’re actually gonna find good stuff. I’m pretty sure there are organizations you could go to where you’d go through everything they’d ever done.
11:18 TW: And I have some of those companies as clients and they all make me cringe. But can we back up a little bit? Because, and I know I’ve watched Lea struggle with this. I’ve struggled with this when I’m working with analysts. I imagine you have to have run into people who say, “I get all this theory, I get how much better things are that you and your team produce. I even get that if I internalize all of this information, I could produce better things. But either I don’t have the time or I’m not really presenting to executives that often. And does that mean that… I mean, I’m sure nobody would be so rude just to say, it means this doesn’t matter. But how do you… Do you find yourself having to kind of make the case for fundamentally why? And this is DataStories and even broader, let’s go Slide: Ology and Resonate, kind of the whole premise, ’cause I feel like that case still needs to be made to analysts and sometimes they… It’s successful, sometimes it’s not.
12:24 ND: Yeah. There was a really interesting study that Jeff Weiner talked about, he’s CEO of LinkedIn, at his Talent Connect Conference, and now that LinkedIn has the job openings and it has the resumes to fill them, and by far, barn one, number one gap is a soft skills gap, and that’s a communication gap. Huge, like, 1.63 million or something gaps in resumes; by far, biggest of the number one in that category was communication, oral and written, number one gap. That’s what everyone’s looking for. I’ve burnt through, little really burnt through two data analysts because they couldn’t thrive in a communications firm, they could only kick out… They just do little projectiles of little bits of data. And it didn’t really help anybody. And it is interesting to me how a lot of people think they don’t need to communicate with the threat of artificial intelligence, being an empathetic communicator is one of the few things a robot and AI will never be able to do.
13:25 ND: Now, we’re getting really close where AI can synthesize data, it can read data, it can make observations about data, it can point to a potential anomaly in the data and it can say, “I think, quarter over quarter, Jimmy Bob is down. You might wanna look at that.” it won’t say, “This is what Jimmy Bob is doing that made his numbers go down”, it won’t say, “Here’s how to have a conversation with Jimmy Bob to make his numbers go the right direction.” It’ll just make a claim about the data. Now robots will be able to do that. So analysts, somewhat, a lot of their job would be teed up to a point for them, but having the conversations in deciding… We go into data… Nobody’s challenge me. Maybe you guys are a better trio to do that, but I just really, really couldn’t find any other reason to go into data unless it’s to find a problem or identify an opportunity. Well, that’s the analyst’s job is to go and see what’s going on in the data, but at some point it turns into a communication problem, ’cause once you find the problem or the opportunity, it becomes a communication problem. If you don’t wanna sign up to be the communicator in that instance, then a robot may eventually be able to take your job. I don’t know. AI.
14:39 ND: Communicating in general, I think it’s a make-or-break career thing. I just, I really do. It’s a threshold some people choose to cross, I think the ones that choose not to cross it do tend to stay individual contributors, which is fine. It’s wonderful to have individual contributors that are really strong in a craft but I do think becoming a communicator differentiates you as a leader eventually.
15:03 TW: I think those people also wind up feeling like, they’re the ones who also are like, “Why am I just generating reports? Why am I not… Why do not I have a seat at the table?” So it is kind of self-fulfilling. I do that LinkedIn stat because it was in the book, and I don’t know that I’ve seen a job description in the last 15 years that doesn’t say “needs strong written/oral communication skills.” I feel like that’s like filler. So I was curious, we don’t need to go into that, how they actually… I don’t know, it just feels like that always gets slapped in and I don’t know how you actually…
15:32 MK: Okay, Tim, can I just interrupt you for a sec?
15:35 TW: Yeah.
15:36 MK: This has been on my mind so much because I do a lot of interviewing for our team and a really strong technical capability is absolutely important. But I had an interview a while back and I really struggled with the decision because the interviewee was exceptionally technical. We do an interview that goes through their communication skills, their values. We do a technical interview, and then a dashboarding interview to get their idea of data visualization. And she could explain every single technical concept. She could get it, she could be like, “I need to run this model”, but when I tried to drill into, like, “Okay, why that model? What’s the question you’re trying to answer? What’s the business problem? How would you communicate that to someone that doesn’t understand what regression is?” or… And that disconnect was there and it really made me think a lot about, is it a trainable skill? Do you hire someone knowing that they’re not good at that and do you try and invest and train them, or is it like you’ve got it or you don’t?
16:42 TW: Oh.
16:42 ND: Yeah.
16:42 TW: Wow, what is the answer to that one?
16:44 ND: The whole “Are you born with it or not?” You know, what I think, not to get completely stereotypical here, but I do think introverts and extroverts are different types of communicators. An extrovert will just blather and shout things out and shoot from the hip and it doesn’t have as much content, it’s not as thoughtfully crafted, it’s not as thoughtfully thought through, because they just don’t spend their time fully thinking sometimes. Whereas an introvert, always a lot of times, has the right answers. They just don’t have the guts or the bravery to speak up and say it. So I tend to be the front person in the company, but my husband’s the more thoughtful, careful communicator, if you can get him to stand up at a staff meeting, that’s the big if.
17:24 ND: He’s just so beautiful, his word choices are great, his concepts are great, his metaphors are even great ’cause he’s a deep, thoughtful contemplative person with a deep well of wisdom, but it’s just getting him to speak. And so I do think that a lot of the data roles are more analytical people, and they do tend to not be as comfortable. So if we can get the analytic people to be more comfortable speaking up and framing things carefully, I think there’s more wisdom that comes out of their mouths and I’m wishing for more of that.
17:55 TW: I wonder if there are introverts who watch the extroverts just kinda maybe sometimes be a little all over the place, but still be energetic. Maybe not the best presentation. And then sometimes… If that’s all they have… I think that’s where the service… I’ll fanboy out a little bit, like the books and the examples you’ve laid out because they’re so prescriptive, it’s like I just kinda wanna beat people over the head and say, “Just find the time, read this, because if you actually try to apply it and you’re not gonna be perfect with it, and you may not feel natural presenting until you’ve done it for a while.” But I like the idea of the introvert who I would think is also maybe just more concerned about, just has more nerves about it. And therefore is more inclined to prepare and try to… Feel the importance of being able to say something ’cause they don’t wanna just find themselves kinda winging it.
18:51 ND: Yeah, and I think that we need to give more oxygen in the room and not speak up so quickly. Like my son is an introvert. Like on the scale of introversion, he might be like hermit, way over here. [chuckle] And I literally figured out that I have to count to seven, like “one Mississippi two Mississippi three… ” like literally in my head.
19:10 ND: ‘Cause what he does is he frames and thinks through what he’s gonna say. So, it’s beautifully formed, and then it comes out of his mouth. But what happens is, my daughter and I are like, “Blah blah blah, blah blah blah,” and then he’ll just sit there, and he has really rich stuff, but he’s not given enough time to shape it the way he wants it so it could come out of his mouth carefully.
19:31 MH: I feel like this work that people need to do to grow in this skill is also very deeply personal. Because to have empathy, to start to dig into these things, as it pertains to personality and how I interact with others, how do you coach people to grapple with that? Because it’s almost like a phrase that’s been going through my head and I’ve been testing a lot lately is, “To gain context on someone else or something else we first have to look through ourselves.” And so, our own level of self-awareness will dictate the level of the context that we can draw from or see in something else. So, if I’m helping advise someone on what to do with their data and analytics, I’m applying my own set of filters first and then getting to theirs. And I’m more or less helpful, depending on how that works, but I just wonder like, I’m feeling some weird emotions actually talking about this. I don’t know if anybody else is, but I feel…
20:32 TW: My lack of emotional intelligence means I’m not picking up on it, so…
20:36 MH: No. But that’s why I’m just wondering, do you ever have that happen where somebody has a weird emotional reaction to this kind of stuff?
20:43 ND: Meaning like a visceral reaction ’cause they’re scared to present? Or…
20:48 MH: Maybe. Not the same, fear of presenting or fear of being up in front of people, it’s pretty well understood, but the idea of grappling with these concepts and like, “Hey I’m gonna develop a set of skills that’s gonna make me a better and more empathetic communicator.” I think maybe I’m just really passionate about analytics, but I’m like all kinds of emotions right now.
21:09 ND: He’s all emotional about it.
21:11 MH: Yeah, I know, I’m all emotional about it.
21:13 ND: Yeah, there hasn’t been a lot of work done bringing empathy to data really, like data will just sit in its little cells unless someone pulls it out, decides a point of view, and then decide something needs to happen from it. And once we decide that the trajectory of the data needs to change… And humans can do it. So you think about how much data we collect, it’s collected mostly by human or human activity or human behavior, or Internet of Things, like just vast amounts of purchasing habits. Humans generate a lot, a lot, a lot of the data. So that means that if you want your data to be different in 18 months, humans are gonna generate data in a different way. You’re wanting them to change their behavior and that’s gonna take empathy.
21:55 ND: That’s where empathy comes in, right? You say, “Oh, hi employees. We need our revenue to be higher, therefore, you need to behave like this to change this number” and communication will not be effective without empathy at its core. And every one of my books has empathy at its core. It’s about making it clearer, making it more understandable, making it audience-centric, making it so the leader understands what change management looks like, whichever of all the books, they all have something, a tool or a visual or a structure for empathy. And that is largely because I’ve never had empathy modeled for me. So it’s partly my own quest as I’ve grown myself as a leader and find myself in situations where I’m struggling to see things from someone else’s perspective. Then I’m like, “This must be a void.” So even with DataStory, so we have lots of data here, but I also kept getting feedback in our anonymous surveys which are always the funnest ones, right?
22:55 MH: Yeah.
22:55 ND: “Nancy doesn’t make decisions.” “Executive Team doesn’t make decisions quickly.” “There’s a whole bunch of stuff that need decisions and no one’s making it.” And I’m like, “Tell me, I’m super fast at decisions if you tee it up clearly.” And that’s where that section in the book is. But I’ll be sitting, serving coffee in the break room. And I think someone’s just making small chatter and they’re actually asking for a decision.
23:17 ND: And I don’t know, I’m thinking, “What an interesting day you’re having”, not thinking they’re like… I’m thinking they’re just like, “blah blah blah.” I’m like, “Good luck solving that.” [chuckle] you know?
23:28 MH: Right.
23:28 ND: And so I don’t know that there’s been a book where an executive wrote a way that they need to be communicated to ’cause it just wasn’t clear to me as the leader that they were asking for decisions to be made. And man, I can whip out decisions, I always have opinions. And so that was a little bizarre to me I would be accused of that. What was hard for me when the book was done was how few charts were actually in it. Outside of the annotation section, I think there’s only like seven charts and of those charts, I think two have real data and the other five are made up, ’cause it’s really about making a point and how do you communicate it… If it’s this, how do you communicate? If it’s that, how do you communicate it? So it was weird, it wasn’t until I actually got the digital file, when it was going to print as a book, I was like, “Wow, that’s how many charts that are in it. There’s not that many. Wow.” Then I thought, “Oh God, I hope it’s credible ’cause there’s not a whole lot of charts about regression.” You know, and all that stuff.
24:21 MK: But I just… I love how much thought goes in through your stakeholders. And actually I don’t think people do it enough. Thinking about how does this person absorb information? How do they think about stuff? What language do I use that’s gonna be effective for them versus your own personal preferences? The only question I do have on that is… Because I was thinking so much about the section you talk about whether someone’s visual or how do they like to digest information, is there a particular type or a particular way they like to see numbers? What do you do when you have a CEO that has one particular preference and then say like, a COO who has a very different type of preference, and whenever you’re getting a decision made, you’re gonna have multiple executives in the room and they’re polar opposites.
25:15 ND: Yeah.
25:16 MK: I was thinking a lot about that ’cause it’s really hard to appease both audiences.
25:21 ND: Yeah. We have a client where the guy who runs the whole thing, the chairman, will only only ever accept data as tables and it can only either be black or red. That’s it, and it has to all be in table format, whereas others are like, “Woah, there’s so much more sophisticated ways for us to communicate to our customers or communicate to others, or to get decisions made.” I think you have to know your audience and it depends on who is who. So if there’s someone who only wants tables in a printout… I’m telling you, if you’re looking for a $100 million budget… Or I don’t know what you’re in there asking for, but I’d give each of them, whatever it is, they want. Stand and deliver and then have the freaking black and white with the red charts, like whatever it is that each one needs of how they receive information.
26:06 ND: We do a lot of pre-reads, here, so by the time humans are in the room, it’s not a presentation. The documents are read, and it’s a conversation. And so we send out the read-aheads and then if they didn’t read it, they get 10 minutes at the beginning of a meeting. Everyone just reads it and you’re saving the entire time for alignment, for decision making, for getting real work done, real business decisions done. So that’s it’s… So it just expedites things to do it that way. And in that case, I would give it… Everyone the way they like information.
26:37 MK: Guys that kind of sounds like the old memo rearing its ugly head. They gave me lots of shit because my last company used to use Jeff Bezos’ memo, and we write like a one or a three-pager and everyone would read it before, all your analysis in the appendix, and you would go to the meeting purely to make a decision. And I thought it was completely and utterly fat, but I’d get lots of shit for it.
27:00 MH: Well, I’m concerned, Moe, because you don’t remember that actually I was a fan of that and only Tim was giving you crap about it.
27:07 MK: Well I generalize.
27:09 MH: Yeah, yeah.
27:10 TW: So I wanna call out a little bit… If we went to all of our listeners, and said, “How often are you stuck presenting to a CEO and a COO, who have differing… And you know?” That’s a little bit of a strawman kind of construct. I feel like, I certainly find myself presenting to the same stakeholders again, and again, and there’s a degree of just paying attention to what they gravitate to and what they don’t when it comes to figuring out how to present to them.
27:43 ND: Yeah I agree, it is more common. And that’s where getting to know them… In the book, I say find a sponsor, find somebody else who’s communicated with them successfully, find out what they really like, find out the way they like to receive it. Not everyone likes a read-ahead but anyone who works for me knows, I will read whatever you send me the night before our meeting, and I will come with it red lined with questions. It’s how I prefer to do it ’cause you get… I feel I get more done when the time is used around that, but not everyone’s that way. Other people do like to be presented to. We have a client. I’ll do this really thoughtful slidedoc, beautiful. It’s got all the answers in it, and I’m, “Hey, read this and we’ll meet.” And every single time, it’s like, “Well, I have to hear your narrative, I have to hear the verbal narrative that goes with it.” I was like, “No, it was a document. No, read it.” So, some cultures just don’t read. They want you to have a visual aid and a verbal dialogue. And I’m starting to… Now that I’ve sent two of these over, and realized I don’t think they’re reading it, we’re gonna not put the effort in the document and we’re gonna just make a couple of slides and do a verbal narrative.
28:41 MH: Maybe send ’em an audio file?
28:43 ND: Yeah.
28:44 MH: Maybe they’re auditory.
28:46 TW: I’ve recorded private little videos with voice-overs to get people to…
28:49 ND: We do that all the time.
28:50 TW: So I spent years railing against slideuments. Garr Reynolds kind of coining that. And I think in DataStories, you briefly kind of allude to the slideument, like the concept behind… ‘Cause I see a lot more slideuments and my fear would be people would hear, they hear slidedocs, they get a quick overview, they may not remember in the moment that slidedocs does not mean just slap everything on to a slide and then present it. The large financial services and healthcare companies I’ve worked with where literally 100% of what they present are slideuments. And they may say, “this is what we’re used to.” There’s this layer. So I guess, can you sort of define the difference between a slideument and a slidedoc, and then maybe also in DataStories, you talked about the three ways of persuasive presentation versus a slidedoc versus exhaustive… I’m trying to remember, citing this from memory… Exhaustive documentation? Can you kinda draw the lines between those?
29:52 ND: Yeah, it’s funny because when Slide: Ology came out, people started to understand, “Oh my gosh, I’m supposed to be using cinematic. It’s supposed to be a backdrop, it’s supposed to be highly conceptual, because they can’t read at the same time that I’m talking.” You can’t do a verbal and a visual stream at the same time. So the pressure to become really simple, and clear on stage became a norm, and then what was happening is internally in other… I started to talk to all our customers, started to talk to everybody who interviewed me, “What percent of presentations that you do are internal?” And they’re like, “Oh, 85%.” “Of those, how many of them are cinematic versus dense?” and they’re like, “They’re all dense.” “Well, why are they dense?” “Well they have to be dense. Because the nature of what we’re talking about, number one, it needs to float around the organization without a presenter and be understood. Right? So it had to be able to travel.” And that I realized that slides were… Instead of everyone having to go to InDesign and hire a designer, they were actually using these for their visual documents or their visual memos, if that’s what Moe wants to call ’em.
30:53 ND: They’re visual memos or visual documentation. So most of them were done that. So instead of vilifying that format with the slideument like Garr did, what he and I both agree is wrong, is when you stand up and present with a verbal stream, very dense slides. But if you wanna make dense slides, and this, we call ’em a slidedoc. It could be a read-ahead, it could be a leave-behind, it could be something that floats all around the organization with the context of your narrative coupled with it. That’s awesome. In fact decisions get made…
31:30 ND: In fact, there’s a whole new movement obviously, that happens now with Google Slides and Google Docs, people don’t even do meetings. They just co-create a draft. Everyone says a thumbs up. “We all agree this is true.” They move on, they don’t even have to meet. They pile all their docs, they pile all their charts in there, they pile all their narrative in there. And everyone just reads it together, and then they have consensus. So, so much of what we do is in service of building consensus. So there’s this, the denser slidedoc, it’s like a magazine layout. People don’t realize that Keynote and PowerPoint, there is a way to build masters, they can have a six-column grid, just like a magazine, beautiful. And you can put six, eight-point type in it, you put all your type in it. They look like a magazine. Beautifully done. We do entire sales enablement systems for companies as slidedocs ’cause then the sales team can re-arrange and make their own little brochures out of this beautiful deck of 100 or 200 slides.
32:22 ND: They can move things around and re-arrange them and they’re stunning, they’re client facing, beautiful, and ready to go. Now, what Garr and I absolutely agree on is that this weird thing in the middle where it’s not a document, and it’s not a cinematic visual aid, that’s the devil’s work, right? When it fits in between.
32:40 ND: Where it’s not so dense, it’s not terrible, but it’s dense enough that you’re using your slides, as a teleprompter. That’s where it gets really in this bizarre place where the presenter doesn’t do as well, the content isn’t delivered as well, and so that’s kind of that spectrum, the spectrum of how you can use presentation software, and we’re trying to knock that middle part out and say, “Look either go full-on visual aid or just pass around a document, but don’t do this weird thing in the middle.”
33:08 TW: So one, just to call out, in case we forget. So slidedocs.com, which redirects to duarte.com/slidedocs has all sorts of super useful information. And I think that to me, that was the care that the people who… A presentation that’s created with, “I’m just getting all my thoughts down. And now they’re in slides and I can present them.” That tends to be the slideument, whereas what you go into a lot of detail talking about is, if you’re doing a slidedog… Slide dog? Or slidedoc, you’re very polite about it. To me, I read it as that’s not an excuse to just say, “Oh, it’s a slidedoc. So now… “
33:49 ND: Make it ugly.
33:50 TW: Go nuts. You talk about the structure and approach, and purpose for all of that as well, so…
33:56 MH: Alright, well, the show is going great, but we need to step aside and talk about one of our sponsors here of the multi-touch moment. Hey Josh, you know how different organizations have different ways of selling? And there’s all kinds of different ones, but finally there’s an analytics platform structured to recognize that fact. If your company is a B2C company, then you can sign up for the B2 Analytics B2C package and get a predefined set of reports that are consumer-focused. Or are you more of a B2B operator? Well, then you just sign up for the B2B package, and those reports are all set up for B2B marketing.
34:35 Josh Crowhurst: Well, you know Helbs, I think it’s a little more complicated than that.
34:38 MH: You’re absolutely right. What are you more of? Maybe you’re a B2B2C company and so you need to get a package for that and B2 Analytics has you covered there as well.
34:49 JC: Yeah, that’s amazing. And on top of that, for Star Wars fans there’s a B2R2 package.
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35:19 MK: I wanna turn things in a slightly different note. So for context, I love the idea of a data story, and I think really hard even when I’m just sharing some analysis that I’ve done over email, I think about the titles of the paragraphs, I think about the points that are in there, and I suppose one of the things that I kind of struggle with is that you do all of this work, you have this amazing data story, you present it to your stakeholders, keeping all of the best practice in mind. And I’ve started using this phrase, which might be a little bit controversial, but it’s called “weaponizing data”. So you prevent this amazing data story, which has been set out, you’ve used all the best dataviz practices, and then a stakeholder gets that, they understand it because you’ve thought really well how to explain it and display it, but then they pick the bits they like, and then it goes on to someone else in the business. And then suddenly, you don’t have control of that story anymore because it’s now become part of their narrative. I suppose I’m just, I’m wondering, how, is there a way or a technique that you could use… It comes down to data politics, I guess. I don’t know. Just curious to hear your thoughts on that.
36:37 ND: I don’t know, you know what is interesting about that whole thing is clearly something that was done stuck enough that it took on a life afterwards, like really great slides do that. We say they breed like bunnies. And you want that, you want your best thinking to be picked up and multiplied and multiplied. You want the strategy slide you made, you want the whole company well just rip and suddenly you see it in everyone’s deck, right? That’s how you make a way. What’s too bad about data is the framing, right? The context, the assumptions, the how careful you were to also make sure it was approached without bias, right? All the kind of thoughtful things you did and it can take on a life of its own. I guess the good news is, it took on a life of its own, the bad news is it’s just like in life, you can’t control the messaging ever. You just can’t.
37:27 MK: I suppose, though, particularly in that context, people will come back to you because eventually, it’s like, “Oh, Moe did that analysis”, and then someone will loop back to you. I suppose my technique at the moment is to be like, “Actually I think it’s worthwhile reviewing the full analysis, here is what I initially prepared, which has all my assumptions. Are you happy for me to share that with you?”
37:48 ND: Yeah.
37:48 MK: But other than that, yeah.
37:49 ND: You keep sending them back to the origin. Or you can always put on the bottom of every slide, or however you do it, or the graphics you can say, “Put the directory of where the original file is, attach it to every single thing where it is on the server, or what the name of the original… ” There’s a preferences field in almost every software where it’ll say “authored by” and you can put your name, “authored by”, that way when they open the file, it will say you were the author of the file. There’s just things like that. So you could just say, “Hey, if you have questions about the origins of this”, to put your contact information all over the place, all that stuff. It doesn’t hurt to do that. Especially if it’s high stakes, man. I mean, some of the decisions made from data are glorious and terrifying all at the same time, and so we just maybe need to treat it with more respect. Like you’re pretty passionate about, that’s cool.
38:36 TW: I’m curious about the executive who wanted things… Only wants things in tables. Whether…
38:43 MH: It’s Tim. [chuckle]
38:44 MK: I had an exec that only wanted line graphs ever, ever. Did not want to see another graph. I just gave her line graphs.
38:52 TW: But I can’t remember if that was in the book or if it was on Lea… I remember that anecdote and I go back to when I’ve had clients and they’ve said, “Well, this is the way we like to see it” and it’s a sea of numbers. I’m like, “yeah, You’re getting this every week.” So this is more on the reporting front where it’s what they’re used to, but just as human beings, just with iconic memory and the visual sensory register, they can’t possibly take it in. Do you find yourself where people say, “This is what I want” or even when you, your example of going around the organization and they said, “No”, when you were discovering the need for slidedocs, was there Like 30% was bullshit when they said, “No, no, no, this is the way we have to do it”? How much of it is inertia, condition without thought? Versus if you can work in a better, more visual, more narrative, cleaner… I don’t know. You just kind of accept it, like…
39:48 ND: Yeah, I guess I’m trying to think about… I do… I’m a visual person but I have a chart right here that’s got rows and columns and one of them is highlighted, and then things are checked off. My team knows that I can process certain things quicker and I don’t ask for everything this way, but I would rather serve up information the way a key stakeholder says they want it then blow them off. Right? Then try to trans…
40:13 TW: You’re wrong.
40:15 MH: Oh boy.
40:18 ND: Yeah, right? Like to try to say, I… And it kills me, but I need paper, I know exactly, I have different little piles on my thing, I can go and recall a piece of paper faster than I can a digital one. And so, a lot of my – they know – come and it’s printed and it’s clipped and I’m not high maintenance at all, but then I can go back and refer to it, ’cause I have things kind of chronological, and then I have literally the urgent things right here on the floor to my right. So it’s like I just know where everything is spatially, I’m spatial. And I just think everybody is a different type of learner, some can handle sitting and reading narrative. But if they have a driver, if an executive has a driver, they can spend time reading stuff, but the speed in which they go, if they don’t have a handler briefing them verbally all the time, it’s hard to get through a ton, a ton, a ton of sitting down and thinking carefully.
41:08 ND: We have Domo, I have other business intelligence tools and it never failed, even though it could plot things, I still was like, “Wait, I gotta see that number.” I was constantly downloading it into Excel files myself and just being like, “Something seems off”, right? And then I would try to figure out and solve it myself, so I don’t know, I’m only speaking for myself. But man, we give our clients the data, the powerful ones, ’cause we work with the top execs, we give them their documents in the format they process information. I wouldn’t mess with that at all.
41:39 TW: Do you have clients though that have said, “Organizationally, we’re not doing a good job of communicating from soup to nuts” where you’re kind of doing kind of a strategic communication overhaul?
41:51 ND: Yeah, so we do strategic communication, so we’ll do strategy, their whole comm strategy for a department or a brand, and then it could last 18 months, we’ll do larger digital transformation strategies, we have a couple of the software companies here that are gonna start to take away some of the entitlements that they get, all the free stuff they’re giving, so that’s gonna be fun. We have to make communication strategies for that. Yeah, and then we’ll take it systemic. That’s what the book Illuminate was about, ’cause there I took storytelling and coupled it with movements. So we looked at the Civil Rights Movement, we looked at the open software movement, we looked at the migration from Mac OS 9 to 10, that was a movement, so we looked at all these different movements over time and looked at the communication that was done to move people en masse over time. And so that’s what we do for companies. So we’ll do just their moment or we’ll help them drive their whole movement of which these moments are moments in a larger movement. Like an epic tale.
42:46 MK: Speaking of movements, if you were tasked with… I feel like every business at the moment is on this journey at the moment where they’re like, “How do we make our business data literate?” So basically, if you want to create a culture of data virality, where everyone actually thinks about, at least using some numbers to inform their decisions. Like I have my thoughts about how I would strategize that, but I’m interested to hear how you would go about it.
43:14 ND: You know what’s interesting is, I mean, there’s data literacy, people we call it, there’s data visual literacy. And I actually think that’s easier to teach, say, than also using your gut because data will only take you so far, and we will never have perfect enough data to make the perfect decisions. There’ll always be some assumptions, always be other things where we got, “Oh, the data’s pointing a projected direction”, but we will never know black, white, it’s really hard to get massively black or white on all data, a lot of it can be that way. So I still would say that for someone to be completely data literate, they also have to know how to communicate it, which means how to interpret it, how to synthesize it, form a point of view about it, and then, rally others to change. So it just depends.
44:02 ND: So I have not every firm, not every company needs every single person in their workforce data literate, and we’ve tried to be really careful where it’s like, we had a whole business intelligence goal. “Yeah, everybody get your data right.” Oh my gosh, we can’t manage our projects, we can’t even do resourcing ’cause the data’s not correct.” “We can’t even do this… ” we’re just like, “Holy cow.” So we couldn’t just say, “Everyone needs to be data literate everyone needs to have a business intelligence goal ’cause that’s how we started.” Then it wound up being, “Everyone just get your time cards in, make sure they’re accurate.”
44:32 ND: Right? Now, how much data literacy or training do they need for that? But we had to break it down into this year, we’re gonna just make sure everybody does this one piece of data accurately because it was just killing us, it was just killing us, ’cause we couldn’t resource ahead. So anyway, I do think that it does segment out that those who do need to know the data definitely need to know how to read it, how to interpret what it means, how to synthesize… It’s just classic… Just the classic things everyone needs to know.
45:03 TW: Having just come through over all of our professional services automation platform, like Michael got out right as that was happening. But is there… That’s the sort of thing where there’s a data story that the whole organization needs to hear, as to why it’s important to fill in your time cards, following the guidelines. They don’t necessarily need to be making the resourcing decision, but there is a story to be told that, “This is why it matters. This is why you can’t blow off. This is why you can’t wait ’til the end of the week to enter your stuff.” Right?
45:34 ND: Right, and then it hits home. It’s like, that’s why you had to work the weekend, ’cause nobody put in the right projects, and we could have known and brought in resources ahead of time, but because job tickets weren’t in, you had to work the weekend. [chuckle] whatever. But yeah, there’s always a price to pay to the human condition when data is not accurate.
45:53 MH: This conversation is extremely valuable and just like your book Nancy, it’s so easy to consume some very key insights and information, but we do have to start to wrap up. And it’s unfortunate, ’cause I’m getting a lot out of this conversation, but one thing we do love to do is go around and do a last call and that’s just sharing something we found recently that we think might be of interest to our listeners and/or something we’re up to that might be useful. So Nancy, you’re our guest. Would you like to share your last call?
46:26 ND: It can be anything?
46:27 MH: Anything.
46:28 ND: I just finished… I don’t know if you guys have seen it, but Malcolm Gladwell’s new book, Talking to Strangers”
46:33 TW: Yeah.
46:33 MH: I have not heard about that.
46:34 ND: I don’t know how well it will do ’cause he is very challenging and he goes, and has a lot of data to support the fact that two perspectives and the way we perceive them, the one that’s right might really be wrong, and the one that’s wrong might be right. It was a page turner. I really enjoyed it. I just finished it last night on the airplane.
46:52 TW: What he did… ’cause he’s made a different kind of audio book out of that. So on his Revisionist History Podcast, there’s an episode where he took the chapter, which I’m now forgetting which one it was on and plays it… But he kinda said he was trying to go into a different… ’cause he’s doing podcasting now, he was like… Can we blow up the audio book? My daughter’s a huge Gladwell fan.
47:13 ND: Yeah I don’t know, I didn’t listen to the audio book, but man, it was good, it was startling to me. My heart raced a couple of times ’cause it didn’t… It didn’t go the direction I thought. It was very interesting. And it’s very emotional topics that he was sussing apart, it was good, I enjoyed it, more than I thought I would.
47:29 MH: That’s very nice. What about you, Moe? What’s your last call?
47:33 MK: Well, we’ve been talking a little bit about data visualization, which is a topic near and dear. But someone in our data team shared a couple of graphs the other day, which I just found so fun. So they’re goodies, but oldies. One was on the ABC in Australia, which was Australia’s most and least popular birthdays revealed. But then the other one is from Scientific American, which has why are so many babies born around 8 AM? And it goes through some really amazing data visualizations and the causes, and really breaks it down. And our whole data science team was going a little nerdy over these graphs. So I’ll share those.
48:10 MH: That’s very nice. Okay, Tim. What about you? What’s your last call?
48:17 TW: I’m gonna go with two, ’cause one came up while we were talking and it’s refuting myself, but I don’t know. It’s a fairly new three-part documentary on Bill Gates on Netflix called Inside Bill’s Brain. It’s like three one-hour things. It’s really well done. But the thread that runs through it is how he walks around with this satchel of 20 to 30 books all the time. So I think when it comes to the, people presenting to Bill Gates, it comes through very clearly that he has a highly atypical brain and way of taking in information and processing it. But it was fascinating. And then the other one that kinda relates to this topic as well, it’s not so much on the presentation. And this was a couple of months ago in the New Yorker, John Seabrook had an article called, “Can a Machine Learn to Write for The New Yorker?” And it gets into OpenAI and GPT-2 and Grammarly.
49:06 TW: But it’s actually, when you’re reading it, there are parts where it will show you what the OpenAI wrote for an entire next paragraph in his article. So it kinda starts with just like the Smart Compose and the Smart Reply. But I love his writing. And so, it comes at it of from a whole bunch of different directions and sort of muses about the future of the ability for AI to generate and write a narrative. So it was a good read. So only two, but they were quick. So what do you have, Michael?
49:35 MH: Well, I’ve been thinking about this topic and so obviously, what I’ve been reading lately is Nancy’s new book. So that’s sort of one of them. So I’ll make that one of my last calls, not to be too self-serving.
49:47 MK: Can I just… Can I interject for one minute?
49:50 MH: Yes.
49:51 MK: I genuinely think that every single analyst, if you work in data, you should read DataStory.
49:58 MH: Moe, can I just do my last call?
50:01 MH: Can I just do my last call? Can I?
50:03 MK: Someone sent me a message the other day saying they liked my book recommendations, so I wanted to make sure we both recommended it.
50:10 TW: She wants to get partial credit. [chuckle]
50:13 MH: So if you buy this book, make sure to write into the podcast and say it’s because of Moe that you bought it. That will help us all out. No, so that was part of it, but I wanna describe a little bit of my reaction as I’ve read it. The book is beautiful. So if you haven’t bought it, it’s both a great read as well as I can already tell would become a reference. So that’s really good design of a book, but it also has this weird effect on me that I think maybe others will share, and so I’m gonna talk about it so we can all commiserate together, which is, I read through this and I think, “Oh yeah, this all makes so much sense. Yeah, of course.” and then I go to write something on a slide and I immediately have this sense of dread of like, “I can’t make anything that looks that nice.”
50:58 MH: So some of you will go through that and just don’t give up, just keep trying it and the skill will grow I think. I’m probably, of the four of us right now talking, I’m probably the worst at PowerPoints and slide design and those kinds of things.
51:13 TW: I’ve seen you present. I think that’s an accurate assessment.
51:16 MH: Yeah, thanks, Tim. [chuckle]
51:16 MK: Aww. [laughter]
51:19 MH: But between you and me, Tim, who gets through to executives better?
51:25 TW: It’s all your emotional intelligence.
51:27 MK: Ooh…
51:28 MH: There you go, baby. So can’t make a slide to save my life, but I connect with the right people. Okay, but the other one, and this is the actually the one, and I wanna use the chance that we have to talk to you, Nancy. So of course, thinking about data and DataStory, this thing comes up in my mind, there’s this book that was called the Seven Basic Plots.
51:48 ND: I love that.
51:49 MH: So this guy, Christopher Booker, basically, came out with a book that said, there’s only seven stories that really exist. So first off, I just wanted to get your sense of that, Nancy. Do you think that’s true, or is there more than that, less than that?
52:02 ND: So some say there’s seven, some 26, there’s one that says 42. I think I would agree, there’s a finite set of story plots. I don’t know that anyone has the exact number, exactly perfect. I did love the Computational Story Lab. They put in all of these fiction stories, and came up with six basic plots, which was pretty powerful. And they’re pretty close to Booker, loved Booker’s work, studied that. And then sometimes, scripts and movie scripts, and they all have different kind of structures and formats, so it just depends. So I really admire Booker’s work too.
52:37 MH: Yeah, anyways, I love the simplicity of that idea. And so it was resonating with me, so I thought I’d mentioned it. Okay, so that’s my last call.
52:45 TW: Well, but can I ask, can it even when, again, not to presenting to an executive, the challenge would be to… I’m just delivering something to a stakeholder to, have read DataStories, to think about that, and apply that, even if it doesn’t feel like it’s that important, I guarantee it will move the quality and the compellingness of the… I don’t know, I feel like that’s the little bird that gets in my saddle is… And I’ve had this discussion with Lea as well. It winds up going down the path of presenting to executives, and I just don’t want anybody to walk away thinking, “This doesn’t apply if I’m presenting to my peers.”
53:26 ND: Yeah, it is true. A lot of times, the stakeholders, the ones have to fund it, and that section could have been a little bit more toned down. But we’ve thought, “Let’s go for the hardest.” Like let’s go for the hardest, and everything after that is just cake, right? It should be easier.
53:40 MH: Yeah.
53:42 MK: I do recommend also listening to Nancy’s episode with Lea because I think they touched on very different points to what we’ve discussed today. So if you do… If you are listening, and you want to delve into the actual specifics of some of the content of the book a bit more, I think that was also a completely amazing discussion.
53:57 TW: We keep saying, Lea, Lea, Lea, so Lea Pica, Present Beyond Measure Podcast.
54:02 MK: Yeah, Present Beyond Measure.
54:04 MH: Okay, I’m sure you’ve been listening, and you’re thinking, “Oh I’ve got all these ideas.” Or, “What could I do about this situation?” We’d love to hear from you. The best way to reach us is on the Measure Slack group, or on Twitter, or on our LinkedIn group page. So please reach out if you have more questions. I don’t know, Nancy, if you get onto social media, I think you’re a big deal, so maybe a lot of people reaching out to you directly…
54:30 TW: She’s all over the social media.
54:32 MH: Are you? Okay. Well, there you go.
54:33 TW: And I believe it’s true story, you don’t ever decline a LinkedIn request. You’re worse than I am.
54:38 ND: I take all my LinkedIn requests. That’s pretty active out there, that little community, huge community.
54:43 MH: Oh, well, I just learned I could connect with Nancy Duarte in LinkedIn today.
54:48 MH: So awesome. Anyway, we’d love to hear from you, it sounds like Nancy is a person who’d love to hear from you too. So please reach out. And obviously, if you are a senior leader of a massive company, now you know who to work with on communicating stories and data effectively. Alright, I know I speak for both of my co-hosts, both Tim and Moe, in first thanking you, Nancy, for being on the show.
55:13 ND: Thanks for having me. It was fun, you guys.
55:15 MH: And of course, we always do the show with the support of our producer Josh Crowhurst. So thank you, Josh. And remember, no matter how good or bad your visuals are, whether your executive wants to see just tables or pie charts, remember, keep analyzing.
55:35 (Announcer): Thanks for listening, and don’t forget to join the conversation on Facebook, Twitter, or Measure Slack group. We welcome your comments and questions. Visit us on the web at analyticshour.io, facebook.com/analyticshour, or @AnalyticsHour on Twitter.
55:55 Charles Barkley: So smart guys want to fit in, so they’ve made up a term called analytic, analytics don’t work.
56:03 Thom Hammerschmidt: Analytics. Oh my God, what the fuck does that even mean?
56:12 MK: Let’s podcast.
56:12 TW: Moe’s so young and adorable. Moe doesn’t even remember overhead transparency projectors.
56:22 ND: Child.
56:23 MK: I do remember them, thank you very much, I just was in primary school.
56:27 TW: You were using PowerPoint in middle school? She says Zinio, yeah.
56:35 ND: You do not know what a 35 mm slide is.
56:40 MH: It’s that next generation perspective.
56:43 MK: Subliminy, sublimal, sublimilamy. Yeah, just pretend I didn’t say that.
56:49 TW: So Michael, are you watching time? Or do you… ‘Cause I… I could…
56:54 MH: I am. I think we’re okay for time… Oh, we are running a little short on time. Sorry.
57:01 TW: I thought it was me asking are you watching time? You know who Stephen Few is?
57:08 ND: Yes, I know Stephen Few well.
57:10 TW: I once… The elevator doors opened, and I was riding down, and he got on. And I was so flustered that I’d got off of this floor, not at the lobby.
57:20 TW: And we were going to the same spot, it was not a large conference. So…
57:24 ND: Were you talking to him the whole time?
57:26 MH: Probably not.
57:27 TW: No, that was the first I saw him. And I was like, “Huh.” And I just walked off and he got on, and then I realized I was on the third floor. And he was like, “Yep, that idiot got off and it’s not the lobby.” And I knew I was gonna see him 15 minutes later.
57:43 MH: So good. So glad you did a time check earlier.
57:51 MK: Nancy, we didn’t even talk about… I had another 100 questions we didn’t even get to.
57:56 MH: I know, I was so crestfallen. I had this question all teed up, and then Tim goes, “How are we doing for time?” And I was like, “Well.” Anyway, it’s okay.
58:06 TW: Rock flag and slidedocs.
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