032 – How and Why Talented Analytical Minds Leave People Scratching Their Head Around Data with Nancy Duarte

Experiencing Data with Brian O'Neill (Designing for Analytics)
Experiencing Data with Brian T. O'Neill
032 - How and Why Talented Analytical Minds Leave People Scratching Their Head Around Data with Nancy Duarte

Nancy Duarte is a communication expert and the leader of the largest design firm in Silicon Valley, Duarte, Inc. She has more than 30 years of experience working with global companies and counts eight of the top ten Fortune 500 brands in her clientele. She is the author of six books, and her work as appeared in Fortune, Time Magazine, Forbes, Wired, Wall Street Journal, New York Times, Los Angeles Times, Cosmopolitan Magazine, and CNN.

In this episode, Nancy and I discussed some of the reasons analytics and data experts fail to effectively communicate the insights and value around data. She drew from her key findings in her work as a communication expert that she details in her new book, Data Story, and the importance of communicating data through the natural structure of storytelling.

In our chat, we covered:

  • How empathy is tied to effective communication.
  • Biases that cloud our own understanding of our communication skills
  • How to communicate an enormous amount of data effectively and engagingly
  • What’s wrong with sharing traditional presentations as a reading asset and Nancy’s improved replacement for them in the enterprise
  • The difference in presenting data in business versus scientific settings
  • Why STEAM, not STEM, is relevant to effective communication for data professionals and what happens when creativity and communication aren’t taught
  • How the brain reacts differently when it is engaged through a story

Resources and Links:

Nancy Duarte on LinkedIn

Twitter: @nancyduarte


Duarte DataStory

Quotes from Today’s Episode

“I think the biggest struggle for analysts is they see a lot of data.”  —Nancy

“In a business context, the goal is not to do perfect research most of the time. It's actually to probably help inform someone else's decision-making.” —Nancy

“Really understand empathy, become a bit of a student of story, and when you start to apply.”  these, you'll see a lot of traction around your ideas.” — Nancy

“We've gone so heavily rewarded the analytical mindset that now we can't back out of that and be dual-modal about being an analytical mindset and then also really having discipline around a creative mindset.” — Nancy

“There's a bunch of supporting data, but there's also all this intuition and other stuff that goes into it. And so I think just learning to accept the ambiguity as part of that human experience, even in business.”  — Brian

“If your software application doesn't produce meaningful decision support, then you didn't do anything. The data is just sitting there and it's not actually activating.”  — Brian

“People can't draw a direct line from what art class or band does for you, and it's the first thing that gets cut. Then we complain on the backend when people are working in professional settings that they can't talk to us.”  — Brian


Brian:  Welcome back everybody to Experiencing Data. I'm really happy to have Nancy Duarte on the line. Nancy, how's it going?

Nancy:  It's going great. Thanks for having me.

Brian:  Yeah, I'm super excited to share your wealth of knowledge about presenting information and helping people encourage others to make decisions, persuading people to get what you want in some ways. But also to help them understand the findings in your own work when you find nuggets with data and you're excited about the potential for those, how do you get other people to buy into your vision? And so you are a communication expert. You've worked with Fortune, Time magazine, Forbes, Fast Company, a whole bunch of great names that everyone here will know and you've written a bunch of books and your TEDx talk has over a million views. And so you, you clearly know a ton about this space. But for those that don't know Nancy's name, Nancy Duarte, she's also the CEO of Duarte Inc, which is the largest design firm in Silicon Valley. Is that correct?

Nancy:  Yeah.

Brian:  Yeah. So you're running a large design communications company there and you're really an expert in this space. So I'm really happy to have you on the show. But now that I've kind of said all your bio like stuff for you, tell our listeners how you like to be thought of and what kind of substance that you like to share with the world, your missions.

Nancy:  Well, outside of being a grandma, which I love. You know what, communication is a mess, really, I mean if you think about it. And so what we're trying to do is help everyone communicate their best, whether that's through data, through technology, and a lot through the spoken word. So if you think about the spoken word and its power and it's really hard to find even an impassioned plea to a movement that didn't start with somebody saying and sharing the spoken word. And so it's kind of an honor to be mostly spoken word experts and we're getting more into the writing of the spoken word and the written word. And just how you communicate is just so important today to be clear and be brief and help others understand what you're trying to say. So that's what we focus on. We'll do everything from the communication plan all the way through to you know all the moments in time where you need to communicate to make sure that your initiative or your company or your project is going really, really well.

Brian:  So this word gets tossed around a lot. I mean every fricking job application that I... When I was an employee, it's like, “good communication skills”. It's like must breathe oxygen, like that's the only kind of thing you can breathe here. It's like it doesn't mean anything. So, tell me what would be the symptoms of, perhaps someone working in... Our audience here on this show is data scientists and analytics leaders, technical product managers. If you think everything is just fine about your own skill and in this area to communicate, what might be some of the biases that you don't know that you have that may actually be problematic or greatest improvement?

Nancy:  I love that question. Yeah, there's statistics that state that the more technical, and specifically the data analyst roles, have a bigger gap in resumes that even say that they're strong at communications. So there's a higher demand for communication skills when you're in a particularly more analytical type of role. And the biggest thing that happens in communication across the board, regardless of the actual title or whether what kind of role they're in, is a lack of empathy. Especially technical people, analytical people, they become subject matter experts in their own field. They become deep thinkers in their own field.

Nancy:  So when you need to communicate to someone else, whether it's your peer or above you, or to a broader audience, like an all hands meeting, people forget to pause and say, "I know what I know. I don't know what they don't know." And really take a walk in the shoes of your audience and really think about, "Well, how does this person communicate? Do I need to even present? Or can it be in an email?" Or if you get an audience with them in person, take a moment to think through how you need to modify what you're trying to communicate so they understand it. Because you can communicate all day long. But if you don't empathetically understand how they receive information, there's going to be a miss. They're not going to get what you're either asking them to do, what you're asking them to approve, what you're asking them to fund. None of that. It'll just blow past them and waste everyone's time. So empathy is a big missing ingredient in almost every role.

Brian:  Sure. And if we had a word cloud for this show, this word would be one of the ones in large type probably.

Nancy:  Oh, wow, that's incredible.

Brian:  So we talk about this a lot. But do you think like on a practical like day to day level for someone who's doing work with data or they're highly technically skilled and then you know let's say maybe 10% of the time they're going out and presenting an artifact that either is communicated visually or verbally or a combination of the two. For example, what are some of those symptoms that it's not going well? Could it be like A, no one's asking any questions and you have blank stares from the audience. Or B, I'm trying to help someone understand like, "Wow, maybe I'm not so good at this and there's actually something to this." But I'm trying to help our audience see what would make me want to go work on this? Or I had this great idea. I'm so sure we should do it this way. And they're like, "No, we're going to go keep doing what we're doing."

Nancy:  Yeah, I think the biggest struggle for analysts is they see a lot of data. They go through lots of data sets and then they have to compile their findings from the data and sometimes you find these clever little bits in different places and then you have a hard time knowing what to cut. So sometimes I feel like they put in the whole kitchen sink and more. And part of the role of an analyst is when you hit a data set, really, really what happens is one of two things. You either find an opportunity or you find a problem and that you find a problem needs to be solved, that the data's pointing to. So the minute you're done digging through the data and you found a problem or opportunity, now you have a communication problem because now you've got to get other people to see the same problem that you're seeing, that the data's pointing to.

Nancy:  And then a lot of times you can stay the analyst or you can form a point of view and say, "If this is what I found, that's a problem. Therefore we need to... What?" And you move from being an analyst to becoming a trusted advisor, a strategic advisor, when you actually will form a point of view of what needs to happen because of the data. So I think you used an off the cuff statistic saying, "Oh, 10% might present." And I would say that it-

Brian:  That was a guess, by the way.

Nancy:  Yeah, right.

Brian:  No data to back that up.

Nancy:  Yeah, right? It was awesome. I love data. I form all kinds of data like that all the time. But it is interesting that even when someone requests data from you and all you're doing is sending it back, most data analysts have an opinion. They just don't feel that it's their job to share what the problem or the opportunity they've seen in the data. So always when you're sending someone data, pointing out an observation at the smallest or pointing out an action that should happen from the data, there's a lot more opportunity I think for that role, than they give themselves credit for because they see it. They see it. They just feel like it's not their job to communicate it.

Brian:  And would you say that the majority of the time... So I see this manifestation in technical data products and analytics tools and decision support applications, where there's a tendency... When designers are not involved in this, the tendency is to err on the side of delivering tons of detail up front thinking that, "Well, we can't be sure... We have some monitoring tool that looks for something, widget that breaks in the assembly line. And so we're going to give you all the telemetry because we're not actually sure. We have an estimate about why this thing broke and here's the proof."

Brian:  No. Instead it's like here's everything because then that way we have no responsibility if something's wrong. And I'm like, "Well, if no one actually will use this tool to do anything with it now because it's information overload, then what value did you really bring?" The quantity does not help you provide the value. So can you talk about that balancing of detail? And you went into this in your book, which [inaudible 00:09:11] talk about these slide handouts. And so you have your presentation to accompany your verbal delivery of the information, but you also have these things called slide docs, which are kind of in the PowerPoint format, but they're intended to be read.

Nancy:  Read.

Brian:    And I think this is a place where a lot of times I see presentations that are effectively slide docs that are being read. There's way too many words on the slides and they need to be read. You can't read and listen at the same time. You're only doing one.

Nancy:  Yeah, it's interesting. We did a survey. We work with really incredibly high performing brands. And I have 140 people, we just build presentations all day. And what was interesting is I surveyed our customers, about 85% of them actually circulated their decks as something to be read, which means the verbal dialogue you may use, just they don't know what it is. It just travels around with the help of a presenter. And I found that those were critical in communicating within cultures is how much information wound up being in these internal facing documents. We called them slide docs.

Nancy:  What happens though is at the very beginning of your slide doc, you need to make sure you have a very clear narrative arc. It might be three slides, it could be 20 sides, but you take a brief you make it into a briefing. And these at slidedocs.com I put some really beautiful templates with beautiful type setting so you could use a really nice design base to start building slide docs from. And what you need to do is create a narrative arc with a structure, which Data Story gets into how to structure it. Have that be brief and tight and clear, and then you can put a fricking appendix in there.

Nancy:  That appendix can be 200, 400 pages. Put everything you think in there but call it an appendix. Don't stick it in the front. Don't co-mingle your backup data with the message you're trying to convey from the data. And that'll right there, just calling it an appendix and sticking only the most important things there. But things that don't clutter the actual message from the data is a great place to start for clarity’s sake.

Brian:  Yeah. And by the way we're referring to your latest book, it's called Data Story and that's the one I've been checking out most recently. It sounds like the summary advice there, if I can play it back to you is that for the data science or analytics type of person, you're probably really good at the appendix piece. And it's the brief piece that you need to work on. So think about how you condense that down, taking an opinion on something, having a story to tell about that information and what the next steps are.

Brian:  I mean I don't know what you feel about this, but instead of having this... And I understand the science side of trying to remain extremely objective in how you approach the work, but in a business context, the goal is not to do perfect research most of the time. It's actually to probably help inform someone else's decision-making. So I think the rigor goes into the process that you used. I don't know. Thinking about the presentation as not so much about their rigor, but debrief the information, briefing being the word there, right?

Nancy:  Mm-hmm (affirmative).

Brian:  Consolidate it down. Don't be afraid to cast your opinion about what it means and what the story is there even though if that's not necessarily what the ultimate decision isn't made based on that. I would say putting that stake in the ground, most senior stakeholders would find value and that you're not just doing this kind of computer like work and not taking any... It's like, "Well, what did it mean?" Help us move forward.

Nancy:  Move forward. Yeah, and that's interesting to me because there's tools coming out like Tableau has some artificial intelligence. You could hit a set of data and it'll come back and say, "Oh, look at Jimmy's Q3 over Q3 sales are lower." Right it makes what I would call an observation. What it won't ever do is say, "Therefore you need to tell Jimmy to do this." It'll never say what action-

Brian:  Prescriptive.

Nancy:  Yeah. And it won't say, "Oh, therefore the data says this, so go do this action." So some say that AI will replace some of the analysis and some of the observations that you make in the data. So it is, I think going to require more people in data to learn how to create a point of view of what needs to happen based on what they found, and then articulate it really clearly.

Brian:  Part of your book is literally called Data Story. I wanted you to tell us a story and what I want you to do is to think about one... I imagine you've had a chance to observe a lot of other people presenting. So I'm curious is there one like from a data scientist or analytics leader or a product manager that totally bombed on delivery. But maybe there was like really solid content and data, there was something really solid there, but the delivery either visually or from the audio standpoint or not the audio, but the diction and word choice and storytelling did not happen. Can you tell us-

Nancy:  Yeah, I was just reading. We kind of collect stories of wins and failures and stuff with clients. The weird thing is I can't say names because then you guys will figure it out.

Brian:  That's okay.

Nancy:  So we work a lot with TED.

Brian:  Acme Corp.

Nancy:  Acme. Well, we work a lot with TED, right? And the stakes are so high on the TED stage. And there was a presenter who reached out to a super... We found loads of material. It was all based in data and a lot of the stuff that hits the TED stage, even though they try to simplify how easily it's communicated, it's based in data. We helped write his talk, we built brilliant slides, and then we will watch them on the stage. They sweat, they ruin the delivery. Or they decided, "Well, I know better. I'm going to change what Duarte told us to do and I'm going to change it."

Nancy:  And so I just got this whole summary with a link to the talk and I haven't had the guts to watch the guy's actual talk because I was like, "Oh my God." But it's so funny because if I were to rank everything, I would have said the content and then delivery and then the slide. Because if you don't have good content and a terrible delivery, you can have awesome slides and really everybody gets frustrated. And so that's how I rank it. And we work a lot with people who...

Nancy:  But then you could look at the opposite, like Al Gore, right? People used to call him a dullard, that he was a terrible presenter. The listenership is probably too young to know, but very stiff, not credible, never came across as warm. And then we help with this movie, Inconvenient Truth that has data. He's not necessarily a scientist. [inaudible 00:16:03] created a movement. There's someone who actually worked on the delivery skills. He gained credibility. Now he's considered a fantastic presenter than he was before. I think that helped him actually that he'd worked on his actual delivery skills.

Brian:  So on this topic of communication skills, I'm sure, all of our listeners are familiar with the STEM education and there's also STEAM education with the A which kind of refers to the arts and humanities.

Nancy:  STEAM, art.

Brian:  You know I've had other leaders in the space that we focus on talking about the value of liberal arts education, humanities education in part because it helps develop some of these skills that are non-technical and non-analytical. I'm just curious, do you think that a lack of arts in the schools from an early age is partly to do with why some of this doesn't come as naturally to some people is that we're not developing. Maybe it's seen as fluff. I always kind of think that people can't draw a direct line from what art class or band or whatever does for you. Then it's like it's the first thing that gets cut. And then we complain on the backend when people are professional about why can't they talk to us? I have no idea what this guy does all day but they cost a lot of money and there's like... Like what the hell?

Nancy:  That's so funny. The arts, it's so funny because arts, you say band, you say art and all those things. In music, you learn systems thinking, you learn community, you learn how to communicate with each other. Liberal arts, you learn history, learn how to write, you learn creative writing. It's so funny. I had a gal who I really admire who's a data science and has all day long programming charts in R and super respected in the startup community. And so I asked her, I said, "Well, look at this program and tell me what you think." And she's just like... Because she's from Europe and there, you almost kind of pick your major and you stay only in your lane. And she said, "I never even knew the parts of speech."

Nancy:  All of it was new and she's close to 40. She did not even know that there's a certain way to construct this sentence. That's how narrow her education was. So she's like, "You just don't learn how to communicate. And now that's going to be the number one demand whose role is to be a communicator." So I agree with adding the A there and some people think art mean art class, like it's all art.

Brian:  Right. Right.

Nancy:  But it's all of the arts obviously, right? It's the history, the writing, the creative thinking, all of that stuff.

Nancy:  We've gone so heavily rewarded the analytical mindset that now we can't back out of that and be dual modal about being an analytical mindset and then also really having a discipline around a creative mindset. And to me communicating and crafting, and really shaping a piece of communication is actually creative [inaudible 00:18:59] an analytical one. And what I've tried to do is make it as far analytical structurally as possible where there's actual kind of almost like a formula you could use when you go to communicate data, just to remove some of the mystery around the process. But it's still creative in nature.

Brian:  Yeah. One of the things that stood out to me when I was going through Data Story in this book is that you know for all our math nerds in the audience, and I say that with love, is that there's science and analytics built into the proof that's in the pudding about the three-act model that you talk about. And I think it was [inaudible 00:19:35] that had asked for a study of the story's structures. And so you guys... They went out and did a study of like the narrative structures to look for these patterns. And so if you want the data to prove that this isn't fluffy, it's actually in the book. So it's really cool that there's like kind of analytics and data to back up what may seem like very qualitative... You know this is what Nancy thinks.

Nancy:  Yeah. There's brain science, lots of brain sciences.

Brian:  Talking about some of those findings.

Nancy:  Yeah. I mean now that we can hook up FMRI machines to the brain, while a story is being told, there's incredible findings that the brain performs differently when a story is being told than almost any other communication mediums. So what'll happen is if I'm the storyteller, you're the story listeners, our brains will sync up. They'll actually fire in the same order, which means there's something really powerful happening in the human brain. It also lights up the sensory, all of the sensory, right? And the other thing that's interesting is your critical mind, like your judgemental nature will suspend while a story's being told and you're open to alternate ideas.

Nancy:  So that applies like in business where like I'm so entrenched in the way I work and what I believe for my department, we're not as open sometimes to gain. And this tool is used incredibly an enormously through change. And the study that you were talking about was done by the Computational Story Lab and they fed in the entire library of the Gutenberg project. Every fiction book that's written has been a public domain practically. And they add it in and look at the arc, the emotional arc. The rise and fall of emotion during a story.

Nancy:  And they did find that it's finite. There are six types of arcs. And so I took these six arc and applied it to data and its arc and how it kind of rolls. So you're right, I mean in the three-act structure is timeless. I'll go back to Aristotle. So nothing in here is really my opinion. I just applied it as a communication device. He applied story as a communication device over data so that you could shape it in a way that people will receive information and their brain will light up when you talk to them, which is cool.

Brian:  It's pretty meta the way you did that because in a way, maybe not even in a way, literally there's a bunch of analysis that was done. In this case, maybe you weren't doing the primary analysis, but you took quantitative data to back up a story about how to tell stories. And so you're kind of, as we call it, dogfooding on the show, your dogfooding your own stuff, and I think that's really powerful. And I think the more analytical people in the audience maybe will recognize that that's going on. And I think that's fantastic.

Nancy: It's like the movie, Inception.

Brian:  Right.

Nancy:  It's like what?

Brian:  So I'm going to be selfish for a second and ask a question that I want to know about, that I care about for me, and most time I try to think about what my listeners care about. And maybe they can relate to this too. So a lot of the speaking that happens can be in the context of things like conferences and conferences have this weird chicken and egg problem, which is you propose a talk on some subject with a broad understanding of who's attending the conference, but they don't know who's attending the conference because no one's bought a ticket yet. They don't have the speakers picked. And so you end up coming into this room with no idea who your audience is. And for me this is constantly a challenge.

Brian:  I'm curious, do you have any strategies besides kind of trying to survey the audience with, "Raise your hand if you're a product manager, raise your hand if you're this," and try to steer your thing in the last minute. You're like ready to give it. What are some strategies when you walk into not really knowing who that audience is at the level? And maybe this is... I'm worried if I'm slicing this too thin, but what do you think about that?

Nancy:  Well, if I don't... So usually what I do is I speak probably at maybe more already established conferences so maybe they know more a bit. Usually I have a conversation as close or an email exchange, as close as I can for the date itself to find out what the profile wound up being of the attendees, if they can tell that. If they can't tell that, I'm always there a little bit early and we'll try to talk to the people in the audience. So what happens at a conference is if they have a choice between, say, a description of your session and they have a choice between yours and someone else. They clearly saw something in your overview of what you're going to talk about to choose you over someone else.

Nancy:  So it's too late at that point to change any of your content. But what I can do is I could wrap it a little bit. If I got there and realized, "Oh, these are more technologists than data people are or these are real more sales people than marketing people," I can actually nuance the talk with my narrative and not have to necessarily do something with my slides, because you always want them to empathetically connect to why you're saying what you're saying.

Nancy:  So the benefits, you could wrap the benefits verbally around what you're trying to share. But that's a tough one. I try to have a pre-consult or a call with whoever asked me to come in because they collect a lot of attendee data. They collect a ton, but finding out why someone chose your course that can be [inaudible 00:24:59] like sprinkle across the audience and figure that out. But you can also... Obviously, like you're saying, a show of hands is sometimes faster than getting them to do a poll or anything like that. But I don't think it hurts to have a quick show of hands that I just don't know that there's any other way to solve that without having to open up a digital tool.

Brian:  Sure, sure. I wasn't talking about making any type of slide change at that point, but just... And I actually do the same thing.

Nancy:  It's too late.

Brian:  Just some of the best conversations you have are when people show up early, and you're you're hanging on by the podium.

Nancy:  Because they're enthused to be there.

Brian:  Yeah. You go figure out what's someone's head space is at and where you can meet them and hopefully push them in the right direction with something you have to share.

Nancy:  Yeah.

Brian:  There's this topic, a thing you guys have created, a model called the data POV, point of view. So what is that and Data Story?

Nancy:  Yeah. So what happens is if you're digging through the data, you start to synthesize it and you do wind up with a point of view. And what that usually is, is what is the problem I've identified because of the data and what's the opportunity I identified because I dug through the data. And the problem or an opportunity, and what you do is you shape that into your point of view. So point of view has two components to it. It's like a problem or opportunity I found and what's at stake if we do or do not do that.

Nancy:  So when you state what the problem or opportunity is, you also have to state a verb. Here's the problem, therefore we need to do blah. What you're trying to do is figure out what are the actions we take to make the data go the direction we want it to. So most data is created by a human or a cell or something that can [inaudible 00:26:44]. I'm really screwing up that word. They can be anthropomorphized and do something [inaudible 00:26:52]. So they leave this data. they leave this little pile of data. Humans usually generate that data. That means that if a human paint this behavior, it would impact the future data.

Nancy:  And so what you're doing is you're trying to say, "Hey, here's the problem or the opportunity that I found in data, therefore, we need the humans to start to do this other action so that we can transform the data. If they do do it, this is great. If they don't do it, it's terrible. So it's this lockup, their point of view plus what's at stake. That was a long definition, but it literally is easy as that. My point of view about the data is, and then you say your point of view and then you'd say what's this thing and or do not do that action that you're asking them to do.

Brian:  Do you have any... I don't know if you've ever model this, this way, this type of observation in the wild with people working on presentations, but I know that one area that is sometimes a struggle, especially in the data science world with machine learning algorithms and predictive technologies is the precision about recommendations and predictability versus making some progress in the right direction and it's like, "Well, is a 58% probability that a prediction is correct enough to go to the business and say we should change our CRM sales funnel, whatever it is because of this 58% thing?" And they're feeling like, "Whose job is it to decide 58% is enough or not?"

Brian:  I'm going to retreat into my object, my objective scientific thing, which is 58% and whatever you guys want to do is fine. I have another project coming up. Can you talk to me about-

Nancy:  Yeah, that's funny.

Brian:  ... that decision versus taking that stand, especially when we can't be... And I'm always like, "Dear data scientist, you're not God, you're not going to predict the future perfectly. Even your 99% thing could be wrong." If you just accept that for a second that it'll never actually predict the future perfectly all the time, then we're already living in this gray space.

Nancy:  Right. I love, love where you're going.

Brian:  Just talk to me about this.

Nancy:  It's so funny because I built a course and then the book, and it was so interesting to see... We built a case where a piece of the answer is just completely missing and you'll never know. Therefore, you have to kind of guess, but you have plenty of data and it is so crazy how people absolutely want there to be a right and a wrong answer. And I left it ambiguous on purpose. What has to happen is you'd stay in business longer and longer and longer. And I'm sure you've developed [inaudible 00:29:42].

Nancy:  You have to develop an intuition, a sense of intuition, best business decision I ever made, the data told me to do something different. This goes into that part where AI can never replace this part of this job, right?

Brian:  Mm-hmm (affirmative).

Nancy:  I don't know. An expert will need to know at 58% probability do we move forward or do we not? It will never tell you your exactly destination, but data will tell you at least the rough hemisphere to head into. At 58%, the answer there might be, "Let's give it one more quarter of data." That 58% might be, "Oh my God, that's enough to be... We're already behind our competitors and we need to dive in." This is where context comes into place. And also intuition, because I can make a data, I can make a decision on data when it's less than 40% because I've been doing this for 30 some years. I've been looking at my industry data, I'm looking at my data.

Nancy:  I can jump on things that other people might not. So that's when you become that strategic advisor. I was saying you move away from just being an analyst to being an advisor because you're so well read. You have context, you understand the industry, you understand markets, you understand models and that's when we marry intuition with data. And not everyone can do it. Not everyone have the guts to do it. It's like you said, you're going to be wrong probability and there's a lot of people who want everything to be perfect in our [inaudible 00:31:04] move forward in real life, and business does not work that way. So you're on the same page there, dude. I mean I'm exactly where I'm at.

Brian:  Until you like... I don't know if you noticed this just as your career moved and you move into this leadership position that a lot of leaders are making a lot, if not most of the decisions based on these intuitions. And now we're wanting to use data more to do this, but none of us know like, "Wow, should we open this plant or not? 100,000 employees. It's a multi-billion dollar investment and it comes down to this CEO to decide yes or no." It's someone's decision, right? And there's no report that says, "Yep, you should do it." There's a bunch of supporting data, but there's also all this intuition and other stuff that goes into it. And so I think just learning to accept the ambiguity as part of that human experience even in business.

Nancy:  Yeah. And I think that's why so many people resist change, because they could look at me and be like, "Nancy's an idiot. Did she not see this one itty bitty tiny, narrow piece of data I look at all day?" Well, I have to look at that person's little itty bit of data in the context of a whole bunch of other data sets. And so I think people can imprint themselves because of data. I think data actually slows down our decisions or embolden someone to be against an idea. I mean, you see it in public. All presidents, they look at one data set that makes them look good and other people will look at a different data set that might... And so you see it happen all the time. And so I just think it's fascinating that data is going to help us incredibly but then also think in some ways hinders forward progress because of exactly the point you're making.

Brian:  Yeah. I'm curious, is there one piece of advice you might have given to yourself if you could redo something from like 20 years ago perhaps around data or how you approach using it in your work or anything like... What would you have done differently?

Nancy:  That's a good question. I've had to really get way more involved in the finance and the data. It's amazing, 140 person firm. I have an analyst, I have business intelligence people who I have to pay 35 grand a year just for the [inaudible 00:33:11]. I never ever would have imagined how much it would cost me to just have access to the data and considering in my long history, 31 years I've been running this business. I mean, it's only been in the last eight, nine. I've always had data in a really simple database, but now I'm having... It's almost like what happens at my deck table will say we would maybe talk about a topic and then we will be ready to move forward and inevitably someone will say, "Can we get any data to support [inaudible 00:33:40]?" It's like, "Holy cow, we read a decision and now you want data to support it," because we have to move forward intuitively sometimes.

Nancy:  I would say to myself, actually I feel like if I were to start today, I would not have the highly developed sense of intuition. I feel like I need to be a bit more grateful for all the time I didn't have any data really at all and then I had a season where the data was super clear and accessible and very small. It was enough data to get me just the right amount of a level of insight and now I'm swimming in it and that's one of the reasons here I am. I had to write a whole book about it. My clients are drowning in it.

Nancy:  I mean we work with like I think 35 of the top 50 brands in Fortune 500 and so it's like they're dying. They don't know what to do with it. I just feel like, wow. It's important now and it's more important than it's ever been and I've had to become better at it. I spend more time in Excel than I dreamed I would and more time looking at my dashboard and stuff that I ever had. So it's becoming a competitive differentiator and you need data enough to turn instantly on a dime. Just instantly, I need to change my decision based on data outcome. So that never used to be that way. So I think I'm I'm having to swing from mostly an intuitive leader to a completely data driven one.

Brian:  I can understand that. It's the theory that all this data is going to help us make all these better decisions, but there's this giant tax that also comes with it, and this is where, again, the storytelling and design can help us actually put these raw ingredients to better use and help us make those decisions because ultimately if we talk about in the context of building data products and applications, things like this, it's really about decision support that that's the outcome that you're going for. And the output is your code and your model. You're at your software application. But if it doesn't produce meaningful decision support, then you didn't do anything. The data is just sitting there and it's not actually activating.

Nancy:  Exactly.

Brian:  And we want to activate for outcomes as much as possible, but harder said than done.

Nancy:  Yeah. I like how you said that it's just sitting there and not activating.

Brian:  Yeah.

Nancy:  I love that line.

Brian:    We've been talking to Nancy Duarte here about her latest book called Data Story and your experiences helping people learn how to be better communicators. So just as we kind of wrap this up, I'm curious, it's hard to distill an entire book for 31 years of experience down into a couple of sentences, but for our audience, are there any kind of parting words you would use in terms of advice or if they were to move from inaction to just taking a first step towards like getting better at this, what would that be?

Nancy:  I mean I think obviously lead with empathy and to me story is the gateway drug to empathy. So I do think becoming a student of story, it is a structure. It's almost mathematical in nature. I mean, it's proven. So I would say really understand empathy, become a bit of a student of story, and when you start to apply these, you'll see a lot of traction around your ideas.

Brian:  Cool. Well thank you for sharing that. Oh, and by the way, I wanted to ask you... I was watching your TED talk, do you know what a square wave is in music?

Nancy:  A square wave?

Brian:  Yeah. There are different audio profiles of waves. There's like a sine wave and they translate to a certain kind of sound. But anyhow, the story arc and your TED talk that you draw, which is like flat line and it vertically goes up, then flat line vertically down, that's called square waves. And I was just curious if you'd heard about that?

Nancy:  Oh my God. You know what I did with my son in the last section of the actual book, I called it a coda, which is a musical term, and in their online you can find, I had my son analyze classical music, a classical composer. So we analyzed it and actually took the Sonata form from Mozart and Beethoven and analyzed it to the shape of that form. It's really beautiful.

Brian:  Oh, that's awesome.

Nancy:  A lot of people see a musical music in it, so that's cool. I love that.

Brian:  Yeah. The Sonata form would definitely translate well. So that's great. That's very cool. Well, how can people find out about your work and where should they find you? Are you in LinkedIn or Twitter? Where do you hang out online?

Nancy:  Our company website is duarte.com. I'm up on Twitter, Facebook, pretty active on LinkedIn. I connect to anyone who connects me up there. And then I think you can find me on almost all the social channels except I'm way behind on Instagram, but I'll come out on that soon.

Brian:  Cool. All right. Well, I will definitely put a link to those things.

Nancy:  Thank you.

Brian:  And your new book Data Story, I definitely recommend people check that out. If you're doing anything to do with presentations, either visually producing handwritten assets to give out debriefs, things like this, definitely a good read. So thanks for coming on Experiencing Data and sharing with us.

Nancy: Awesome. Thanks for having me.

Brian:  All right, cheers.


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