106: Want to Stand Out as a Data Analyst? Master Data Storytelling w/ Brent Dykes

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[00:00:53] I had stumbled across an interesting data point that showed our customers didn't really value our shipping policy as much as we thought they would.

[00:01:01] So fast forward a couple weeks later, I'm in the boardroom.

[00:01:04] I get to the data point and he looks at it a moment and then he says, bullshit.

[00:01:09] That wasn't what I was expecting to hear from her.

[00:01:11] I wasn't expecting outright denial and just shooting it down.

[00:01:15] I got out of the room relatively unscathed.

[00:01:17] But what died that day was nobody picked up that data point.

[00:01:20] Nobody touched it because he basically shot it down.

[00:01:23] Welcome to the Data Career Podcast, the podcast that helps aspiring data professionals land their next data job.

[00:01:30] Here's your host, Avery Smith.

[00:01:33] Welcome back to the Data Career Podcast everyone.

[00:01:35] I am here with a great guest.

[00:01:37] We have Brent Dykes who is the author of Effective Data Storytelling,

[00:01:42] a great book about how to tell stories with data.

[00:01:46] We're going to get into all of that conversation today.

[00:01:48] So Brent, thank you for joining us here.

[00:01:50] I happen to be here, Avery.

[00:01:52] Okay. I wanted to start with actually the way you start the book as well.

[00:01:55] So do you mind telling us the story of your, you know, an eager-eyed, bushy-tailed,

[00:02:01] a young, you know, whipper snapper professional ready to share,

[00:02:05] you know, earlier in your career, ready to share some amazing insights that you found

[00:02:10] and kind of what happened in that story?

[00:02:12] Yeah. I was an MBA intern.

[00:02:15] I was one of multiple MBA interns all working at a direct-to-retail catalog retailer.

[00:02:21] And so we all had to present to a SVP of e-commerce.

[00:02:27] And this wasn't your typical executive.

[00:02:30] He was actually a former Special Forces helicopter pilot

[00:02:33] and very intimidating, very stern, intelligent individual.

[00:02:37] He had reduced other MBA students to tears during their presentation.

[00:02:42] So I had a very tough, tough reputation.

[00:02:45] And so I was just preparing for a midpoint presentation to kind of give him an update

[00:02:50] on where we were or I was in the other MBAs as well.

[00:02:53] And I had stumbled across an interesting data point that showed that our customers,

[00:02:58] or at least the customer is filling out the survey,

[00:03:00] didn't really value our shipping policy as much as we thought they would.

[00:03:04] And we highly valued our shipping policy at this e-commerce site.

[00:03:08] And so I thought this was interesting.

[00:03:10] I thought, wow, this is something I should share.

[00:03:14] Now the caveat was it wasn't directly related to my project.

[00:03:18] So I debated whether I should even include it.

[00:03:20] And I said, you know what? I'm going to include it because it's something,

[00:03:23] you know, I need to provide this information to this executive

[00:03:27] and his other managers and directors.

[00:03:29] So I was like, okay, I'm going to include it.

[00:03:31] So fast forward a couple of weeks later, I'm in the boardroom.

[00:03:34] He's in the middle of their directors and managers around the table.

[00:03:38] I get to the data points about the customer feedback on our shipping policy.

[00:03:43] And he looks at it a moment and then he says bullshit.

[00:03:47] And I was that wasn't what I was expecting to hear from.

[00:03:50] I was kind of expecting, oh, that's interesting, Brad.

[00:03:52] Oh, where did you get that data point?

[00:03:54] Or, you know, is there any other data explanation or maybe we should

[00:03:58] take a closer look at that?

[00:03:59] Or, you know, that's kind of what I was expecting.

[00:04:01] I wasn't expecting, you know, outright denial and just shooting it down.

[00:04:06] So I was a little bit flustered at that point.

[00:04:08] Luckily I had a mentor who jumped in gave me some cover fire and I got out

[00:04:12] of the room relatively unscathed.

[00:04:14] But what died that day and what taught me a very valuable lesson

[00:04:17] was nobody picked up that data point.

[00:04:19] Nobody looked at that customer data.

[00:04:21] Nobody touched it because he basically shot it down.

[00:04:24] And it taught me that when I have something important to communicate,

[00:04:28] when I have a data point or an insight that needs to be shared and

[00:04:32] understood, I need to do a better job communicating it.

[00:04:35] And that kind of led me into this world of data storytelling because I felt,

[00:04:39] you know what? There's got to be a better way to communicate insights.

[00:04:42] There's got to be a way to do this more effectively so that our

[00:04:46] insights go places and do things rather than just dying on the

[00:04:50] boardworm floor.

[00:04:51] So if I'm understanding you correctly, if you want someone to have

[00:04:56] a takeaway or to understand or to be open to any sort of an idea or

[00:05:01] an insight you have, telling a story helps them kind of become more

[00:05:06] accepting to that idea.

[00:05:08] Yeah, no, there's psychology has shown that when you approach

[00:05:11] somebody with a data or fact, our defense mechanisms go up.

[00:05:15] We don't want to be truant.

[00:05:16] We don't want to be deceived or kind of more skeptical.

[00:05:20] It may not jive in that example.

[00:05:23] Right? It was counterintuitive to this executive.

[00:05:26] He was very proud of the shipping policy.

[00:05:28] There's no way that this information or data could be true.

[00:05:31] And so often what happens is when we approach people with facts or data,

[00:05:35] they're going to, you know, they're very defensive.

[00:05:37] Whereas if you approach somebody with a story, people are much more open-minded.

[00:05:41] They're kind of dropped their analytical guard.

[00:05:44] They're not going to nitpick on the details as much and they want to

[00:05:48] hear where the story goes.

[00:05:49] So there are certain advantages that we get from sharing stories.

[00:05:54] And it's in our DNA.

[00:05:55] We love to share stories and we love to hear stories.

[00:05:58] And so it's almost like, you know, in my book, I talk about having an express highway.

[00:06:03] You know, if you think about all the data that gets clogged up in our brains,

[00:06:06] you know, it's hard to process and stuff.

[00:06:09] A story is kind of that express lane, the HOV lane in our brains

[00:06:14] that gets the information much more quickly, much more effectively

[00:06:18] than other means which we have, which aren't around storytelling.

[00:06:22] And now everyone listening, you just experienced a great example of storytelling

[00:06:27] because Brent and I could have gotten on here, right?

[00:06:30] And, you know, I could have been like Brent, like what's up with storytelling

[00:06:34] and you could have said, oh yeah, storytelling is way better than just,

[00:06:37] you know, showing a graph or just saying some sort of an insight

[00:06:41] or something that you take away, it's way better.

[00:06:43] And that could have been your whole point.

[00:06:45] But to illustrate that point, you told a story.

[00:06:47] And so now I challenge everyone listening, try to forget that story.

[00:06:50] Try, you know, a day from now, try to not remember this military general

[00:06:56] standing in the middle of the boardroom yelling explicit things

[00:06:59] to poor intern Brent as he just tries to present his simple analysis

[00:07:06] about customer service.

[00:07:07] You can't forget it.

[00:07:08] But if we would have just said, yeah, storytelling is important.

[00:07:10] You could have forgotten it.

[00:07:11] The idea is stories really get their point across

[00:07:14] and you become memorable and stand out when you tell those types of stories.

[00:07:18] Yeah, absolutely.

[00:07:19] It's like, you know, there was a study done where they,

[00:07:22] so in a book called Mate to Stick, it was written by Chip and Dan Heath

[00:07:25] on communication.

[00:07:26] And one of the things they talk about, they share this example

[00:07:29] from Chip Heath.

[00:07:30] He teaches a course at Stanford University

[00:07:33] and it's on communication.

[00:07:34] And one of the activities that he puts the students through is

[00:07:37] he gives them a bunch of data on a topic and asks them to take a position

[00:07:40] and build a short presentation on it.

[00:07:43] And he asked them to include some of these data points or statistics

[00:07:47] that he's given them.

[00:07:48] And so on average, the students use about two and a half of the statistics,

[00:07:53] but then one in 10 of the students will actually incorporate an anecdote

[00:07:57] or example or story as part of their pitch.

[00:08:00] And so they divide up into groups of how many students,

[00:08:04] like four or five students each group.

[00:08:07] And they present to each other.

[00:08:08] They rate each other, give feedback.

[00:08:10] They think the exercise is over.

[00:08:12] And then 10 minutes later, the professor comes back to them and says,

[00:08:15] okay, how many of you remember any of the statistics that were shared?

[00:08:18] And then what they found was only 5% of them could remember any of the statistics.

[00:08:23] But then if there was a story shared, 63% of them could remember the story.

[00:08:28] And so if we think of our human brains and how we're constantly trying

[00:08:32] to make sense of the world around us, and it's really about packaging

[00:08:36] the information, making sense of it.

[00:08:38] And that's what a story does, right?

[00:08:40] It's pre-packaged.

[00:08:41] Here's everything you need to understand the meaning behind something.

[00:08:44] And so that's why we want to take our data points,

[00:08:47] our information that we have, package it as a story.

[00:08:50] And now you've got something that's memorable, that's persuasive.

[00:08:53] It's super powerful.

[00:08:55] I love what you said there, that the packaging matters

[00:08:57] because it's easy to think, well, my analysis is my analysis.

[00:09:01] It's true.

[00:09:02] I crunched the numbers, I pulled them straight from the SQL database.

[00:09:05] I made the tab low, graphic all by myself.

[00:09:09] This is fact.

[00:09:10] And it could be.

[00:09:11] I literally could be the fact.

[00:09:12] The thing that people often miss is facts and stats and lines of code

[00:09:19] don't actually make the world go round.

[00:09:21] Humans do.

[00:09:22] Humans are still running companies.

[00:09:23] There's not AI robots.

[00:09:25] You have to be able to convince humans that, hey, this is the stuff

[00:09:29] that matters.

[00:09:30] This is why it matters.

[00:09:31] This is why it's important.

[00:09:32] This is why it's true even though you don't maybe think it is

[00:09:35] or you don't want it to think it is, like you still have to be able

[00:09:38] to convince them, these humans that these things are correct.

[00:09:41] And so your packaging, like literally the insight stays the exact same.

[00:09:45] The truth is the truth.

[00:09:46] But you really have to like wrap a bow around it, put it in some Christmas paper

[00:09:51] around it at a wrapping paper and like present it with a bow

[00:09:54] to people and be like, Hey, here's my insight.

[00:09:57] The insight can be the exact, the insight inside.

[00:10:00] Wow.

[00:10:01] That's a tongue twister could literally be the exact same thing.

[00:10:04] And the outside could really be almost some technical people.

[00:10:07] Like I don't even know if this is the right word.

[00:10:09] I don't even know if this is a word, but like superficial, like it's,

[00:10:12] it doesn't seem like it adds any value, but the packaging adds

[00:10:15] a ton of value.

[00:10:16] Right?

[00:10:17] Like that's what you're saying is like, if you just get a Christmas

[00:10:19] present and a grocery bag, it doesn't feel as good if you

[00:10:21] get a Christmas present in a big box really neatly wrapped up together.

[00:10:25] Yeah.

[00:10:26] And it's much more than just decorations or different things that

[00:10:29] we're adding on to this.

[00:10:30] I mean, one of the critical mistakes that I see a lot of people

[00:10:33] make is that, you know, if you've spent a lot of time analyzing

[00:10:36] the data, you're very familiar with what's going on in the

[00:10:38] numbers, the numbers speak to you, right?

[00:10:41] And it feels like the story is coming out so clearly.

[00:10:44] But what we forget is that we've spent a lot of time

[00:10:47] analyzing the data spent days or weeks or months analyzing

[00:10:51] these numbers and we're very intimately knowledgeable of

[00:10:54] the context of where this data came from, what it means,

[00:10:57] what the metrics mean, everything.

[00:10:59] And when we put out a dashboard or we put out a report or

[00:11:02] graph art and we put it in an email or send it to somebody,

[00:11:08] we're just like, this makes complete sense to us as the

[00:11:12] originators of this analysis.

[00:11:15] But on the receiving end to them, it's like, okay,

[00:11:19] what is it I'm looking at?

[00:11:21] You know, I don't have the context.

[00:11:23] I don't know what these numbers are.

[00:11:25] I don't know all the ins and outs of what I'm looking at.

[00:11:29] And we make that assumption that it's so clear to us that

[00:11:32] it's clear to everybody else.

[00:11:34] And that's the key thing.

[00:11:36] We need to transition from that analysis, you know,

[00:11:39] phase that we go through.

[00:11:41] And then when we make the decision that we have to share

[00:11:44] this information with other people, we then have to

[00:11:47] shift gears, change our approach and say, okay,

[00:11:50] how do we explain this to other people?

[00:11:52] How do we make this content readily understandable to

[00:11:56] other people who haven't, you know,

[00:11:58] if you're presenting to an executive,

[00:12:00] we can't expect them to spend days in the data like we did.

[00:12:04] No, they've, you've got five minutes,

[00:12:06] you've got 10 minutes, 20 minutes with them to kind of

[00:12:09] quickly get to a point and explain something to them.

[00:12:12] And yeah, you're not going to go through all the weeds.

[00:12:14] You're going to give the key elements that they need to

[00:12:17] understand the problem or the opportunity with the data

[00:12:20] you're providing wrapped in a story in,

[00:12:23] and that's the magic.

[00:12:24] You know, we're packaging up the insights in a way that

[00:12:27] are going to be understood.

[00:12:29] They're memorable.

[00:12:30] They're engaging.

[00:12:31] I mean, they're persuasive.

[00:12:32] They're going to persuade people to act.

[00:12:34] I mean, those, that's why we take this extra effort

[00:12:37] to really, you know, tell stories with our data.

[00:12:39] And I think that's such a good point because there's

[00:12:41] even people who may be listening to this that

[00:12:44] aren't quite a data analyst yet, but they're on their

[00:12:46] way.

[00:12:47] They're aspiring.

[00:12:48] They're trying to break into the data world.

[00:12:50] And a lot of the times I see, you know, their, their

[00:12:53] resume or I see their portfolio or a project they've made.

[00:12:56] And it's like a lot of the times they'll just send me

[00:12:58] like a get repo link or even just like a GitHub profile

[00:13:01] link.

[00:13:02] And I'm like, man, I have to click like seven times

[00:13:05] before I see anything like that I actually really care

[00:13:09] about.

[00:13:10] And then when I click on that seven thing, it's like,

[00:13:12] it's like 150 lines of, of sequel code.

[00:13:15] And it's, this is where I think you talk about in your

[00:13:17] book, knowing your audience is really important where

[00:13:19] it's like a recruiter or a hiring manager in this

[00:13:22] case or in real life, like a CEO or a manager, they're

[00:13:25] not going to, they don't care about the 150 lines that

[00:13:28] you wrote, that you wrote in sequel.

[00:13:30] They care about the results.

[00:13:31] So if you can, you know, give them a brief

[00:13:33] introduction to the data, right?

[00:13:35] If you can kind of give away, give your main point

[00:13:38] then kind of show a little bit about what you did

[00:13:40] and the results, that's really what they care

[00:13:42] about, but you have to craft that in a certain

[00:13:44] way.

[00:13:45] And I think if I'm not mistaken, you have like a,

[00:13:47] I don't know the right phrase, like a framework that

[00:13:49] you kind of do with this sort of data storytelling.

[00:13:52] I think it's, let me see, six essential elements

[00:13:55] of a data story.

[00:13:56] Does that sound familiar?

[00:13:57] Do you want me to go through them or?

[00:13:59] Yeah, I think, I think so.

[00:14:00] I think I have them written down.

[00:14:02] You can tell me if this is right or wrong, but I

[00:14:04] have it as the data foundation, the main point,

[00:14:07] the explanatory or no, explanatory focus,

[00:14:10] linear sequence, dramatic elements and visual anchors.

[00:14:13] Can you break that down a little bit?

[00:14:15] Yeah.

[00:14:16] Yeah, let me go through those, pull out my books here

[00:14:18] and just make sure I hit them in order here.

[00:14:20] So the first one being data foundation, right?

[00:14:22] So these are kind of like the essential elements

[00:14:24] that you'll find at every data story

[00:14:26] and what makes the data story special.

[00:14:28] So the first thing being that the data foundation

[00:14:31] and obviously we're not telling fictional stories.

[00:14:33] We're not making it.

[00:14:34] It's not a creative exercise where, you know,

[00:14:36] our foundation is in data.

[00:14:38] We're being inspired to create a story from

[00:14:41] facts and data that we found.

[00:14:43] So that, that's kind of like the first thing, right?

[00:14:45] And when we think about a data story,

[00:14:47] I use the analogy of chocolate.

[00:14:49] Like so obviously you want to have good quality

[00:14:52] chocolate, right?

[00:14:53] There's probably many of you have had really bad

[00:14:56] brown colored chocolate that's not,

[00:14:58] doesn't feel like it came from a cow.

[00:15:00] You know, it's highly manufactured and pretty gross.

[00:15:04] And the same thing with our data storage, right?

[00:15:06] We have to have a good foundation of good quality.

[00:15:09] We want to have, we want to don't,

[00:15:11] don't want to just have sprinkles of chocolate

[00:15:13] in our chocolate chip because we want to have

[00:15:15] lots big chunks of chocolate, right?

[00:15:17] So that's, I mean, I'm using an analogy here,

[00:15:19] but basically we want to have good,

[00:15:21] a good portion of data in our stories.

[00:15:24] And we want to make sure that it's trust,

[00:15:26] trustworthy, reliable, relevant, you know,

[00:15:29] those are key things.

[00:15:30] And then the next point is you got to have

[00:15:32] a main point, right?

[00:15:33] What is that main point you're trying

[00:15:35] to make with your story?

[00:15:36] If there's one thing that your audience remembers

[00:15:38] from your story, what is it?

[00:15:40] You know, and usually in my terminology that I use,

[00:15:43] I talk about the aha moment being that one key insight

[00:15:46] that you want people to remember.

[00:15:48] So that's important with a data store.

[00:15:50] You got to have a direction.

[00:15:51] There's got to be a destination,

[00:15:52] not just just spouting off random facts

[00:15:55] and interesting data points, you know,

[00:15:57] no, there's got to be a purpose.

[00:15:59] There's a, there's a flow.

[00:16:00] There's a destination that we're taking people on

[00:16:02] the explanatory focus.

[00:16:04] That's the next thing.

[00:16:05] So I've already talked about how when you're exploring the data,

[00:16:09] you know, you're looking at data a certain way,

[00:16:11] but then once we transition to storytelling,

[00:16:14] it's really about explaining the information.

[00:16:16] And so we're very big on making sure that things are clear

[00:16:20] and understandable to the audience.

[00:16:21] The next thing is a linear sequence is another key element.

[00:16:25] Right?

[00:16:26] So there's a kind of a sequencing that we take people through.

[00:16:29] We start with maybe a set a establishing the status quo.

[00:16:32] This is what we typically see in our data.

[00:16:35] And then, then there's a hook and for me that's where maybe

[00:16:39] there's a key metric that goes up or a key metric that goes down.

[00:16:42] And that gets the audience interested.

[00:16:44] So going from the setting to the hook.

[00:16:47] And then we, we start to connect the dots for the audience

[00:16:51] going all the way to our big insight.

[00:16:53] And then we're not done at that point because we want to help

[00:16:56] drive action.

[00:16:58] So that's where we want to make sure that they know what to do.

[00:17:01] You know, here we've found like, Hey, there's a $5 million savings

[00:17:05] that we can potentially get, you know, by making some changes

[00:17:08] to our processes or whatever.

[00:17:10] And then it's like, well, what are the options?

[00:17:12] How do we, you know, what are the steps we need to take

[00:17:15] to get this $5 million opportunity?

[00:17:18] So there's a, there's a sequencing and that to keep part

[00:17:21] of a story I think sometimes gets lost.

[00:17:23] Right.

[00:17:24] So some people say, Oh, dashboards tell data storage.

[00:17:26] Well, that sequence is really difficult with a data store or

[00:17:29] with a dashboard unless you're actually laying it out in a

[00:17:32] certain manner in which, okay, look here, then here, then here,

[00:17:36] then here.

[00:17:37] Often with a dashboard, we kind of look over there and then

[00:17:39] there's different, you know, things going and there's really

[00:17:42] not that, that linear sequencing that's important.

[00:17:44] And then the next thing is the, the dramatic elements.

[00:17:48] And so I really believe that with a storytelling, you got

[00:17:51] to have that narrative arc, right?

[00:17:53] So there's this narrative arc that's a part of all the, the

[00:17:56] films and this TV shows and the, the movies and the novels.

[00:18:00] I mean, obviously there's variations in terms of what we'll

[00:18:03] do with that narrative arc, but a traditional narrative arc is,

[00:18:06] you know, you kind of introduce the characters.

[00:18:08] There's some kind of inciting incident that occurs and then

[00:18:12] tension builds is that, that hero or the main character

[00:18:16] starts to go on some kind of journey or transformation.

[00:18:19] And then they reach a climax, like, you know, where

[00:18:21] they're battling the foe or the villain and then, and then

[00:18:25] there's kind of a resolution at the end where everything's

[00:18:28] kind of made finalized and made whole.

[00:18:31] So definitely bringing in those, those narrative dramatic

[00:18:34] elements into storytelling.

[00:18:36] There's a lot of parallels that we can, we can borrow

[00:18:39] certain things from other forms of storytelling that, you

[00:18:43] know, you typically wouldn't associate them with data,

[00:18:47] but, but we can, we can definitely lead into those

[00:18:50] elements.

[00:18:51] So that's the dramatic elements.

[00:18:52] And then there's the visual anchors.

[00:18:54] And a lot of times we are relying on charts and, and

[00:18:58] graphics to kind of show movements and trends and patterns

[00:19:02] and anomalies in the data.

[00:19:04] If, if we just talked about them, we can, but often when

[00:19:08] we visualize them, they come across to an audience much

[00:19:11] more powerfully than if we just said, here's the data or

[00:19:14] here's a table with all the numbers go through it

[00:19:18] yourself.

[00:19:19] No, like if we plot those out, we actually visualize the

[00:19:22] trends.

[00:19:23] It's going to be much more powerful, much more meaningful

[00:19:26] and much more accessible too.

[00:19:28] You think about people having to go through rows and

[00:19:31] rows of data to kind of interpret what's going on,

[00:19:33] whereas we can plot those out in a graph of some

[00:19:36] kind.

[00:19:37] And then almost instantly people can see, oh my gosh,

[00:19:41] you know, we've got a huge gap here or we've got a

[00:19:44] huge spike going on or whatever it is and the

[00:19:47] data, they can see it and it can enlighten them to

[00:19:50] things and the numbers that they would otherwise miss.

[00:19:52] That's so interesting.

[00:19:53] And I love, I love those six elements that it's like,

[00:19:55] you got to be truthful.

[00:19:56] You got to have your main point up front.

[00:19:58] And then you got to, you know, tell craft a unique

[00:20:01] story.

[00:20:02] And I was going to ask, like, well, what's an example

[00:20:05] of a bad data story?

[00:20:07] But, but to me in that answer, you almost hit it

[00:20:10] and it's not so much, there's probably a lot less

[00:20:13] bad data stories rather than people just not telling

[00:20:17] the story at all and just kind of giving here's my

[00:20:21] insight.

[00:20:22] Here's my dashboard.

[00:20:23] Is that true that that's probably the mistake that a

[00:20:25] lot of people end up making?

[00:20:26] Yeah, because of data dump.

[00:20:28] It's like, oh, I found something interesting.

[00:20:30] Here's, here's something and here's something and

[00:20:32] here's more, you know, and it's, and they just

[00:20:34] want to make sure that people have enough

[00:20:36] information to make a decision, but there's

[00:20:38] really no, you know, you've got to kind of like

[00:20:41] alter out some of the information.

[00:20:43] If it's just a fire hose of here's, here's all the

[00:20:46] data that I looked at, all the data, interesting

[00:20:49] things that I found and then leave it up to your

[00:20:51] audience to kind of make connect the dots.

[00:20:54] That's, that's really hard for them to do.

[00:20:57] And so we need to make that effort to kind of do

[00:21:01] the dot connecting the interpretation, helping

[00:21:04] them to see and make sense of the number, you

[00:21:06] know, and when we do a bad job of that,

[00:21:08] that's because we haven't communicated it clearly.

[00:21:11] Maybe we've, we haven't visualized the information

[00:21:13] in a way that's helpful to the audience.

[00:21:15] We maybe were including information that's not

[00:21:18] related or relevant.

[00:21:19] You know, again, we're trying to be focused

[00:21:22] with the data story.

[00:21:23] We're trying to be very focused on what's going

[00:21:25] to be meaningful.

[00:21:26] What's going to, you know, we talk about actionable

[00:21:28] insights.

[00:21:29] We really want to help people take action

[00:21:31] from the data and use it to make better

[00:21:34] decisions or smarter actions.

[00:21:36] And if we're not taking the time to go through

[00:21:39] those numbers and really still down whatever

[00:21:42] the critical team, what are the action, what's

[00:21:44] the value?

[00:21:45] I mean, one of the biggest questions you're

[00:21:47] going to get when you're presenting data to

[00:21:50] an executive is so what, right?

[00:21:53] You found this insight, but there's also

[00:21:55] another element.

[00:21:56] So what, you know, and then that's okay.

[00:21:59] Well, it seems like we do have a problem with,

[00:22:01] you know, whatever it is, maybe with our,

[00:22:03] our product rollouts, right?

[00:22:05] You've identified a gap in our product rollouts.

[00:22:08] So what, you know, what do we, what's,

[00:22:11] what's the impact of this problem?

[00:22:13] And then we can then say if we've done our analysis

[00:22:16] and we've built a story around, we probably

[00:22:18] explored, well, if we don't fix this product

[00:22:21] rollout problem, we're going to, we're

[00:22:23] going to miss, you know, we're going to

[00:22:24] undersell, you know, maybe we do some kind

[00:22:26] of like predictive analysis or we've done

[00:22:28] some kind of forecasting of, you know,

[00:22:30] we have five more product rollouts this year.

[00:22:33] If each of them underperforms like this last one did,

[00:22:36] because we didn't have a good process, that means

[00:22:38] we're going to miss our targets by 25%, by 40%.

[00:22:41] Whatever it is, that means, you know, hey,

[00:22:44] that, that means $20 million.

[00:22:45] So that means $30 million.

[00:22:46] I mean, these are big numbers I'm talking about here,

[00:22:49] but it can be much smaller or bigger depending

[00:22:51] on what we're, where you're focused.

[00:22:53] But at the end of the day, people are going

[00:22:55] to care about this so what, not just the,

[00:22:58] the numbers of the data and, and we need to put

[00:23:01] our insights in context.

[00:23:03] Such a good point because a lot of the times

[00:23:06] when I see student people ask me to look

[00:23:09] at their projects, you know, and I'm, I'm a busy guy.

[00:23:12] Like, I mean, I definitely try to keep busy, right?

[00:23:15] And so it's not like I have all day to be looking

[00:23:17] at projects when I open up a project.

[00:23:19] And this could be what project for people who

[00:23:21] maybe haven't landed a job yet or at work

[00:23:23] if you're presenting some sort of project

[00:23:25] you've done, like the person you're

[00:23:27] presenting to is probably busy.

[00:23:29] When they open that up, it's like, okay,

[00:23:31] tell me why I should care.

[00:23:32] It's kind of like the thumbnail on a YouTube video

[00:23:35] or the movie poster or the trailer for, for a movie.

[00:23:39] It's like prove to me why I should invest three hours

[00:23:42] of my time in this movie.

[00:23:44] And that's the same thing with a data project.

[00:23:46] It's like, why should I spend the next 25 minutes,

[00:23:48] you know, reading all this stuff,

[00:23:50] looking at these slides, looking at your code?

[00:23:52] You kind of have to prove it to me.

[00:23:54] And so there's this, there's this framework

[00:23:56] that I've stolen.

[00:23:57] I can't remember who I stole it from, so I apologize to them.

[00:23:59] But it's called the 10-1-10 rule.

[00:24:01] And basically it means when you, whenever you're presenting

[00:24:04] anything, you have 10 seconds to make an impression.

[00:24:07] If you've made an impression after 10 seconds,

[00:24:09] they will give a minute of their time.

[00:24:11] If you make a good impression after that minute,

[00:24:13] they're going to spend 10 more minutes on that.

[00:24:15] So I really talk about that mostly with resumes.

[00:24:17] But it's the same thing with a data project.

[00:24:19] It's like, if you want me to sit here,

[00:24:22] you have to like entice me.

[00:24:23] And so I think the thing you kind of talked about

[00:24:25] was the same point being that aha moment.

[00:24:27] I think that's a great name for it.

[00:24:29] Sometimes I hear people name it bluff, bottom line up front,

[00:24:32] or the TLDR, which I think stands for too long.

[00:24:36] Did it read?

[00:24:37] Like it's like usually a one-sentence summary.

[00:24:39] It's like if I didn't read this, what's my main takeaway?

[00:24:42] And really if you don't have that,

[00:24:44] it doesn't matter how good the analysis is.

[00:24:46] People might not get to it.

[00:24:48] And that's problematic because then they won't take action.

[00:24:50] And then there's really no impact.

[00:24:52] Does that make sense?

[00:24:53] Yeah, yeah, yeah.

[00:24:54] Yeah, there's there, there is.

[00:24:55] I mean, there's, there's a couple of communication approaches

[00:24:58] that can come in handy.

[00:25:00] And obviously an executive summary,

[00:25:03] a summarization of your project can be very valuable.

[00:25:06] And that's really where you're not really telling a story.

[00:25:09] You're basically saying here are the highlights.

[00:25:11] Here are the things that you will get out of this project.

[00:25:14] And then people can then decide if they want to see more information.

[00:25:17] A storytelling approach is a little different where you're not giving away

[00:25:21] maybe the climax at the beginning of the movie, right?

[00:25:24] You're going to be like building up to the key thing to kind of get people

[00:25:28] interested in hearing your story as a hook.

[00:25:30] And that's where you're like, Hey, here's a problem I've identified.

[00:25:34] You know, you know, here's a, you know,

[00:25:36] I found that many students are not doing their homework or whatever it is,

[00:25:41] you know, and it's 50% of students are not doing their, oh my gosh,

[00:25:44] I didn't realize it was that high, you know,

[00:25:46] and now you've got the attention of the audience because they're like,

[00:25:49] well, what's going on? What's costing that?

[00:25:51] And then you dig into, well, here are the three reasons why students are not

[00:25:55] doing their homework or what's contributing to their inability to complete

[00:25:59] their assignments or whatever.

[00:26:01] And then you build up to your aha moment.

[00:26:04] So there's a difference.

[00:26:06] I think a lot of, there's a lot of,

[00:26:08] there are use cases for where a summary is where you put out,

[00:26:12] you know, you put your main point first upfront and then it's up to

[00:26:16] the audience to decide whether they want to hear more or not.

[00:26:19] And then the storytelling approach is more building up to that main point.

[00:26:23] So on one hand, a summary is very efficient, right?

[00:26:27] It gets to the point very quickly.

[00:26:29] And it probably in job scenarios and different things you have to do that,

[00:26:32] that manner because you're not going to get more than 10 seconds

[00:26:36] or a minute.

[00:26:37] And so you've got to kind of summarize very quickly.

[00:26:39] If you have more time,

[00:26:41] the benefit of a story and I would say probably stories are happening.

[00:26:45] What you've got the interview, right?

[00:26:47] Cause you, what are you going to be doing in an interview?

[00:26:50] You're going to be telling stories.

[00:26:51] You're going to be telling stories about, you know,

[00:26:53] whether this is your first job and you were working on projects

[00:26:56] in college or whatever, or in whatever bootcamp or whatever you've been doing,

[00:27:01] you're going to be talking about those assignments, those projects,

[00:27:04] telling stories about what you did and, and what, you know,

[00:27:08] what interesting things you learned and how they could be beneficial to

[00:27:11] this client or to this company.

[00:27:14] And so the power of your storytelling will be very critical for you to get

[00:27:19] a job because, you know, they're going to be listening to your story,

[00:27:23] your stories of what your skills are and your interests and all that.

[00:27:27] So, you know, summarization in storytelling go together,

[00:27:32] but they summarization is about efficiency.

[00:27:35] Storytelling is about effectiveness.

[00:27:38] I love it.

[00:27:39] I love the interview aspect you brought up because I think a lot

[00:27:43] of people wouldn't think of an interview as a, as a chance to tell your story.

[00:27:48] But really at the end of the day, like an interview's job is to

[00:27:52] understand who you are as a person and, and also like if you look at

[00:27:56] from the, from the inter perspective is to stand out.

[00:28:00] And we just talked about like if you want,

[00:28:02] if you want something to be memorable, tell a story.

[00:28:05] And if you want someone to know about you, like telling a story.

[00:28:08] So I just think that's such a brilliant way to look at an interview of like,

[00:28:12] in this interview, of course I'm going to be answering their questions.

[00:28:14] We also know that like a lot of the times you're going to get asked behavioral

[00:28:17] questions.

[00:28:18] The best way to answer behavioral questions is with the star method,

[00:28:21] situation task action result, which is really just the story method.

[00:28:25] It's like, tell me a story when you actually did this and how did it go?

[00:28:29] So there's kind of like,

[00:28:30] you'll be answering the behavioral questions with stories,

[00:28:32] but then also in the first question that you're going to be asked,

[00:28:34] literally every single interview, you know, tell me about yourself.

[00:28:37] That's basically, in other words, they could be saying,

[00:28:40] tell me your story.

[00:28:41] And that's probably how you should be answering it if you,

[00:28:44] if you want to stand out, which I think is so captivating and so much better

[00:28:48] than, oh, I'm Avery, I've worked as a data analyst for this company

[00:28:52] and this company.

[00:28:53] Yeah, that's me.

[00:28:54] Right?

[00:28:55] I don't think that's very exciting.

[00:28:56] It's not going to make you stand out.

[00:28:57] Right?

[00:28:58] I mean, though, that's the great thing about stories is they're memorable.

[00:29:01] You're, you're, there's a stack of resumes on this recruiter's desk,

[00:29:05] you know, or on their hard drive and you want to stand out.

[00:29:09] And so I would say 100% if you invested in thinking about what stories,

[00:29:15] you know, it's not, not about like, okay, what were all the,

[00:29:18] you know, how many, how many lines of code did I write and SQL or how many,

[00:29:23] you know, all of these, you know, data, data related statistics about

[00:29:28] your capabilities or skills and how many certifications you have and

[00:29:33] blah, blah, blah.

[00:29:34] No, like think about how can I show, do I have a story that illustrates

[00:29:38] my curiosity?

[00:29:39] Do I have a story that illustrates my ability to attention to detail?

[00:29:44] Do I have a story that highlights my tenacity?

[00:29:48] You know, that's what you need to come prepared and you need to have those

[00:29:52] in your brain ready to go.

[00:29:54] And when, when the recruiter or the hiring manager gives you an opportunity

[00:29:57] to, Hey, can you tell me about, you know, this project, you know,

[00:30:01] or give me a, give me a project that you worked on recently.

[00:30:04] And then not only do you give them project or her project, you package it

[00:30:08] up as selling one of your core features, one of your, one of your

[00:30:12] distinguishing capabilities or differentiating, you know, personality

[00:30:17] or characteristics that's going to, you know, it's going to, that's

[00:30:20] going to, you're going to sink hook into them.

[00:30:22] They're going to, you know, if you tell the right story and you

[00:30:25] all of a sudden, you know, you're going to stand out, you're going

[00:30:28] to be that much more relatable.

[00:30:29] I would say try and tell as many of these short stories.

[00:30:33] Obviously you can't take 15 minutes to tell the story.

[00:30:36] You have in this, you know, if you have a half hour interview,

[00:30:39] you're going to want to hit as many of these succincts one or two

[00:30:43] minute stories, you know, maybe up to three minutes and it packing

[00:30:47] in as many as you can.

[00:30:48] And you're going to be that memorable candidate that gets the,

[00:30:51] gets it to the second round or gets, you know, gets the job.

[00:30:54] It can really make the difference.

[00:30:56] I feel like which is, which is quite impressive.

[00:30:59] The other thing I wanted to talk to you about was the three pillars

[00:31:02] of, you know, data storytelling.

[00:31:05] You have the data, you have the narrative and you have the

[00:31:08] visuals, which is what you, what you talk about in your book.

[00:31:11] And when we're kind of talking before we started to record,

[00:31:14] I was, I was mentioning that a lot of people mistake your

[00:31:16] book for a data visualization book, probably at least in part

[00:31:20] because of due to another really popular data book called

[00:31:23] storytelling with data.

[00:31:24] That is, I would say 90% visualization based 10% story

[00:31:29] base. And then I would say your book is much closer to a 50,

[00:31:32] 50 split.

[00:31:33] And so we were kind of having this discussion and one of the

[00:31:35] things that you had mentioned was like you, you, you definitely

[00:31:39] want to have all three.

[00:31:40] You want to have data.

[00:31:41] You want to have narrative.

[00:31:42] You want to have visuals, but like I could tell a data

[00:31:45] story without the visuals.

[00:31:46] And so I just thought that'd be an interesting, an

[00:31:49] interesting challenge.

[00:31:50] Maybe we'll have to have you back on the podcast someday.

[00:31:53] Well, we'll do a whole, a whole Brent episode with telling

[00:31:56] a data story with no visuals, just, just audio.

[00:31:59] But do you think it's possible to tell stories without,

[00:32:01] without the visuals?

[00:32:02] Absolutely.

[00:32:03] Yeah.

[00:32:04] I mean, I don't know if I'd be expert on audio data

[00:32:07] stories without visualizations.

[00:32:08] I am very dependent on using visuals, but I recognize the

[00:32:12] power of storytelling or data storytelling without visuals.

[00:32:16] And I'll give you an example.

[00:32:18] If you've listened to any of the, like the daily from

[00:32:21] New York times or many of these, these podcasts out

[00:32:24] there, they're telling very rich data stories without any

[00:32:29] visualizations.

[00:32:30] You know, and it's, they're giving facts and data and

[00:32:33] weaving in, you know, humanizing the numbers and

[00:32:36] telling stories and examples and dropping facts at the

[00:32:40] same time, you know, and probably facts are part of a

[00:32:43] core spine within those stories, but there are no

[00:32:47] visuals being shared.

[00:32:48] There is no chart.

[00:32:49] It's just all data points and narrative mixed together on

[00:32:53] these podcasts.

[00:32:54] And so I, again, the one that comes to mind is the daily by

[00:32:59] the New York times.

[00:33:00] Excellent.

[00:33:01] They do lots of very investigative narrative journalists kind

[00:33:04] of approach.

[00:33:05] And so they are rich in data and narrative, but because of

[00:33:11] the media, there are no visuals.

[00:33:13] And so, you know, they're, they're doing it in a

[00:33:16] certain way.

[00:33:17] So I, when I look at the visuals, I mean, obviously

[00:33:19] visualization has been synonymous with data storytelling

[00:33:23] for a long time.

[00:33:24] And I felt like that wasn't really the best characterization

[00:33:29] of what the storytelling is all about, right?

[00:33:31] So we look at the three pillars, data, absolutely.

[00:33:34] You know, like we're not making up fictional stories

[00:33:37] here kind of like with my six, you know, the first one

[00:33:40] being data foundations, the same thing it's data is at

[00:33:42] the core of a data story without data.

[00:33:45] It's just a fictional story.

[00:33:46] It's not, it's not based on facts.

[00:33:48] So absolutely data is critical.

[00:33:50] And in fact, it's again, it's not like we've created, like

[00:33:54] we want to tell us we've got a narrative in mind and we're

[00:33:57] like, okay, I want to cherry pick this data point, this

[00:34:00] data point to that form of story.

[00:34:02] No, it's, we're starting with data.

[00:34:04] We're doing analysis, we're finding something and then

[00:34:07] we're building a story on that.

[00:34:09] Now the next thing, a data story can't be a data

[00:34:11] story without a narrative, right?

[00:34:13] So the narrative component, you know, and we have to

[00:34:15] think about what makes a story a story, right?

[00:34:18] There's a certain arc to it.

[00:34:20] There's a certain, you know, I talked about some of those

[00:34:22] principles, the linear sequencing and different

[00:34:25] things and obviously narrative touches us in a

[00:34:28] different way on an emotional level that, you know,

[00:34:32] facts and data just really connect with this from

[00:34:34] a logical perspective or reasoning perspective, but

[00:34:37] the emotional part of that comes through on the

[00:34:40] storytelling on the narrative.

[00:34:42] Now the third element, visuals in my mind, visuals

[00:34:45] are there to help us tell our story with the data.

[00:34:50] So it's almost like I almost feel like that visualization

[00:34:53] is a supporting mechanism for, because we are sharing

[00:34:56] data and often a lot of times it's super beneficial

[00:35:00] to be able to visualize the data with charts and

[00:35:03] graphs.

[00:35:04] That's why it's there because it's a facilitating

[00:35:06] supporting element, but often, you know, like

[00:35:10] again, I shared the audio podcast as a way that

[00:35:13] you can tell data stories without visuals, but

[00:35:15] often, you know, like in business and then

[00:35:18] different things we're going to be relying on charts

[00:35:21] and visualizations to really communicate the

[00:35:24] very complex datasets that we're working with

[00:35:26] and trends and patterns and, you know,

[00:35:29] different things and anomalies that we're

[00:35:31] seeing in the data are going to be, you're

[00:35:33] going to be able to see them with the charts

[00:35:35] and with the visualizations.

[00:35:37] But again, I see them as a means to an end and

[00:35:40] that's probably different than the current, you know,

[00:35:44] beliefs out there about data storytelling.

[00:35:47] And, you know, I place a lot of emphasis on the

[00:35:50] narrative for reasons because I feel like when

[00:35:52] I do surveys of different audiences that I speak

[00:35:55] to and I ask them which of the three, you

[00:35:58] know, which of the three pillars data narrative

[00:36:00] of visuals is the most challenging for you.

[00:36:03] And quite often it's almost like two thirds of

[00:36:07] the people believe that narrative is the area

[00:36:10] that they struggle with the most.

[00:36:12] And so for me, that's great to hear because

[00:36:15] that's what I wrote my book for.

[00:36:17] Obviously, I believe in the importance of the data.

[00:36:19] I believe in the importance of the visuals,

[00:36:21] but for me, I felt like the gap or the

[00:36:23] thing that there was being, the element

[00:36:25] that was being overlooked was the narrative.

[00:36:27] And that's purely where a lot of the power comes

[00:36:30] from. You know, like obviously I believe in the

[00:36:33] information that we get from the data, but

[00:36:35] definitely the emotional power, you know,

[00:36:38] that the engaging, memorable, persuasive

[00:36:41] component of data stories comes from the narrative.

[00:36:44] And so that's why it was definitely an intentional

[00:36:47] core focus of my book.

[00:36:49] You guys can find Brent's book, Effective

[00:36:51] Data Storytelling.

[00:36:53] I have a copy.

[00:36:54] I think it's absolutely great.

[00:36:55] We'll have a link to it in the show notes

[00:36:57] down below.

[00:36:58] And then Brent, where can people go to learn more

[00:37:00] about you?

[00:37:01] Yeah, you can go to effectivedatastorytelling.com

[00:37:04] and so I'm there.

[00:37:06] You can learn more about the book if you're

[00:37:08] interested in learning more.

[00:37:09] Also, I'm on LinkedIn, so look at me on LinkedIn.

[00:37:11] I do three posts a week.

[00:37:13] Basically every week I do three posts.

[00:37:16] I've also got a newsletter so you can go

[00:37:18] to my corporate website, hero.com

[00:37:21] and sign up for my newsletter.

[00:37:23] And that's where I share tips and tricks

[00:37:25] on data storytelling, talking about analytics

[00:37:28] and data culture.

[00:37:30] Those are some of the topics I'd like to write about.

[00:37:32] Awesome.

[00:37:33] Well thanks Brent.

[00:37:34] We appreciate your time.

[00:37:35] Thanks, Avery.