#042: Data Storytelling with Brent Dykes

The following is a straight-up machine translation. It has not been human-reviewed or human-corrected. However, we did replace the original transcription, produced in 2017, with an updated one produced using OpenAI’s WhisperX in 2025, which, trust us, is much, much better than the original. Still, we apologize on behalf of the machines for any text that winds up being incorrect, nonsensical, or offensive. We have asked the machine to do better, but it simply responds with, “I’m sorry, Dave. I’m afraid I can’t do that.”

00:00:04.00 [Announcer]: Welcome to the Digital Analytics Power Hour. Tim, Michael and the occasional guest discussing digital analytics issues of the day. Find them on Facebook at facebook.com forward slash analytics hour. And no, the Digital Analytics Power Hour.

00:00:24.28 [Michael Helbling]: Hi everyone, welcome to the Digital Analytics Power Hour. This is Episode 42. You know, a big old buzzword in our industry right now is storytelling. It’s really important. Further, using data to tell your story. It’s a critical skill for the analyst. Seems pretty easy, but so few people do it well. On this episode of The Power Hour, we’re going to talk about it. So take a seat by the fire and listen to us spin you a yarn and who you might ask could help. Tim Wilson, my co-host. Hey, Tim. Hey, Michael. And of course, me, Michael Hublin, hold forth on such a meaty topic. You might know him as the author of Web Analytics Action Hero. For 12 years, he was a leader in the consulting organization and in Analytics Evangelist at Amateur Slash Adobe. And today, he’s the director of data strategy at DOMA, where he helps customers with DOMA adoption and data usage. And he also writes for this little magazine called Forbes. Yeah, it’s Brent Dykes. Hey, thanks guys. Welcome to the podcast. We’re excited to have you. Thank you. So this topic around data storytelling, right? It’s, I think it’s near and dear to all of us. Certainly you’ve spent so many years championing this cause, but maybe to start us off, let’s talk about sort of some foundational or definitions around data storytelling, what it is and what it isn’t.

00:01:54.68 [Brent Dykes]: So I define data storytelling as a structured approach for communicating data insights more effectively to an audience using narrative elements and data visualizations.

00:02:05.56 [Michael Helbling]: Well, that’s awfully succinct. Yeah, great show. We did it. So if you just write that down, I think that’s probably what we should go with.

00:02:14.40 [Tim Wilson]: But how, I mean, do you feel like, I feel like there are people who say data storytelling, but aren’t really thinking through, I mean, that I guess maybe the narrative piece is where a lot of times I feel like people said, oh, I had 22 slides. So. Therefore, they were in a sequence and a narrative is a sequence of things. Therefore, it was a narrative and it very much was not. I mean, to me, it seems like storytelling is one of those things that is just really difficult to really have a narrative thread that is things linked together. And yet sometimes you put this label stuff is, sure, I’m doing data storytelling.

00:02:49.92 [Brent Dykes]: Yeah, I would say a lot of people kind of look at data storytelling. I mean, a lot of work’s gone into it from the data journalism side. And so you’ll see a lot of that where a data visualization will be called a data story. And I think in some cases, a data visualization could qualify as a data story, but not always. And in many cases, it won’t. And there’s like five key elements that I see you need to have in order to have a data story. So first of all, you gotta have a main point, right? You gotta have a destination you’re taking your audience to. And sometimes as analysts, what we like to do is sometimes we, we find all these really cool things and we start like just, you know, as, as Avinash Kosh would like to say, data puking. And I don’t really like that term. I don’t like. think about it all the time but you just basically load up on all these really cool insights but really have a main point you’re trying to convey to your audience and i think that’s the first thing the next thing is you know the story itself has to take. not a descriptive approach, but a explanatory approach. And so, you know, when we’re describing things, we might say, well, you know, we’re really focused on the who, what, and when. But when we’re explaining something, we’re going a little bit deeper and we’re trying to help the audience understand what’s happening in the data and why and how. And I think that’s a key differentiator between a data story and maybe just a presentation of the visualization. So that’s a key thing. And then the third thing, you kind of mentioned it already, Tim, where it’s got to have a story has a kind of a linear sequence. So if you think of the Wizard of Oz, right? So first you have this tornado touches down, and then Dorothy gets caught up in that. The house lands on the things the Wicked Witch or the East. And then she gets the shoes, and then she goes on this yellow brick road path where she meets lots of interesting characters. She goes seeking the Wizard of Oz. And then he says, you know, if you want to go back to Kansas, you’re gonna have to go kill the witch. Now this is a sequence of things that occur. And I also think that, you know, as we’re telling a story, as we’re looking at our data and trying to get to our key point or our main point, there are some data points along the way that we need to share to either set the context or to connect the dots for the audience. And then the fourth element is all these narrative elements. If you think of the movie Up, this is an example I’d like to share. At the beginning of the movie, there’s this little segment. It’s only about four or five minutes long, very little dialogue at all. But through this interaction between Carl, the old man, and his wife, we kind of see this story. We learn something about him, and we learn about all his struggles and challenges that they went through and everything, and then his wife dying. And so if we cut that from the movie, we just think this guy is a jerk. We wouldn’t think of them as a lovable kind of character and understand some of that context. So if I take that to a data story, we as the storytellers of the data or insight, we have to kind of set that context. We have to introduce the characters. We have to set the plot and the setting and kind of set that up so that people that aren’t as familiar with the data, when they come in and they can appreciate the story, they can jump in and And we can give them exactly what they need to hear in order to make that decision and take that action. And then the last piece, and it’s the piece that gets all the attention, are the visuals. And the visuals are obviously, in a data story, data is very complicated. and it can be overwhelming for a lot of people that aren’t as familiar or aren’t in the data all the time. And so our job is to go in there and to take those data visualizations and actually look for how can we convey an insight in a meaningful way that a layperson who maybe understands the business domain but doesn’t understand all the facets of the data can quickly come to a conclusion make a quick comparison in the data visualization and appreciate the point that we’re trying to make. And, you know, those five elements are present in a data story and, you know, one of the questions you might have is, you know, does a dashboard convey a story? And if I think if it’s curated, yes, you could curate a dashboard so that it does convey a story, but an automated dashboard perhaps in many cases is just a collection of random data points is not a data story itself. It could be a platform for somebody to construct or create a data story or identify data points in the data and then go and build a data story.

00:07:37.34 [Tim Wilson]: Well, that’s where I feel like a dashboard is descriptive and it shouldn’t be random data points. I agree it’s not a story, but a dashboard should be, how is the weather today? Is it raining or is it sunny? It’s kind of a at-a-glance fact as opposed to a data story is more trying to get a level deeper into the what happened and what could we do about it? If I glance at a dashboard every day, I’m not going to remember unless I’m some, you know, strange savant of some sort. I’m not going to remember what the dashboard said, you know, 13 days ago. So it’s kind of meant to be at a glance or things good, bad or indifferent. Do I need to take action? Do I need to dig in? Whereas to me, part of with data storytelling is you’re trying to actually I think hook into other parts of the brain so that it gets retained. So that, yeah, when you said, hey, that data story that you were told 13 days ago, or that presentation you saw 13 days ago, I can actually play back, oh, this was the takeaway, and I’m playing into the other parts of the brain to actually retain it.

00:08:44.00 [Brent Dykes]: Yeah, absolutely. One of the things I frequently say is stories beat statistics. And there’s a couple of use cases that are fairly really well known out there. There’s the one that Chip and Dan Heath, the guys who both made to stick. They did a study, I think it was Stanford where they had a bunch of, they gave a bunch of students some data points and then told the students, okay, you got to argue a position on something like gun control or something. I don’t remember what topic it was, but they had these students get up and for five minutes they kind of do their pitch using data. And then afterwards, the students probably thought the task at hand was to see who was the most persuasive, but actually what they did is they asked the students, how many of you can remember any of the data points that were shared? And only 5% of the students could actually remember any of the data points. And then they asked them, how many of you could actually remember a story that was shared? and only 10% of the students actually shared a story as part of their five-minute pitch, and 63% of the students could actually remember one of the stories that were shared. So that’s from a memorability perspective. A story is going to outclass statistics on that front. And then from a persuasion perspective, you may have heard of a study that Wharton, and I think a couple other universities did where they basically had to save the children charity or basically funding African kids and trying to help them and they did a study where they took one version and they took all this data on all the struggles and challenges that people in Africa had and then built kind of like infographics, you know, like a brochure that kind of highlighted, you know, this many people in Angola are struggling, you know, these kids don’t have clean water. And then in Mali, you know, other kids are, you know, 37% of the kids, whatever the data points were. And then what they did on another group, they created a different kind of test where they just took one kid. And I think her name was Rukia. She was a little 11 year old in Mali and told her story. And they told her struggles and what she went through. And so they had two groups of students, you know, they divided them up, had them take these two or expose them to these two kind of formats. And then at the end, they asked the students, you know, you’ve heard about this charity, you know, we’re paying you $5, you know, $5, $1 bills for doing this survey. You know, would you like to contribute to the And then they found that the ones that had the infographic version, they donated $1.43. But the ones that saw the story of Rokia, they almost doubled the amount that they contributed. It was $2.38.

00:11:20.72 [Tim Wilson]: And there was another group that just had Sally Struthers doing a voiceover, and those people demanded that they get more than $5 after they get more money back.

00:11:29.81 [Brent Dykes]: Yeah, so from a memorability and a persuasion perspective, storytelling is really powerful. And so if we just leave statistics on their own, they’re not going to work as well. When we combine the statistics with a story, that’s where we’re going to have an impact.

00:11:45.26 [Michael Helbling]: Yeah. And actually, you see this played out even in the political arena, right? politicians giving speeches will often pull one story out as an example of their broader policy or platform. So that’s pretty common. You know, it’s interesting because it’s it’s hope for all of us artistic types, right? That that’s such an important part of analytics, you know, and you think, oh, yeah, the data is so important in your ability to manipulate the data. But, you know, if you’re listening and you went to a liberal arts school that you can be a great analyst because of those things that you were just discussing.

00:12:21.28 [Tim Wilson]: But when we talk about a story, I guess I’ve got two different sort of things that I think challenge me. One is when you’re telling a story and the Save the Children is a good example where what you’re doing is using an anecdote and with the data, that could be a real customer or it could be an archetype of a customer or a persona or however you want to make them tangible, just like when you’re developing personas. I mean, that’s kind of one way to go, which is the data supports this. And now I’m going to kind of weave in a very personal, not personal sharing your feelings, but personal, like trying to make it very tangible and real with an example. Is there another type of story though that isn’t necessarily the It’s not the individual, it’s not the customer, it’s not the anecdote, it’s more, I don’t know, it can be larger portions of data, you know, that this thing happened and then because that happened, you know what are, I’m going to use a maybe a horrible example, revenue is down. That’s kind of the crisis. Well, we’ve dug into the data and we’ve realized that average order value is down. And we’ve dug in further and realized that our lines per order is down. Our revenue per line is fine. So it’s a classic analysis where you’re kind of trying to break down And that’s certainly not how you tell the story, because that’s in reverse, but then can you tell a story where you say, look, let’s talk about what goes into revenue? Well, it’s how many orders. And you know what? We’re getting the same number of orders. Is that a valid type of story as well? Or is that still kind of too clinical and data-y? Or is it really kind of in the presentation? If done well with appropriate visuals, you can kind of help people understand the business better because you’re illustrating the model and putting numbers behind it.

00:14:07.75 [Brent Dykes]: Yeah, I mean, I don’t think there’s necessarily one approach. And I would say the first rule of data storytelling is understand your audience, right? So if you know that your audience would respond to one type of story format and not to another, then you go with what works for your audience. that’s pretty important. But yeah, I mean, there’s different elements of storytelling. One of the classical is to have a hero or somebody who represents, you know, so Rokia was the hero of the story, essentially, and your cart-abandoner person that you build a data-driven persona around could be that hero of your story. But I don’t think you necessarily have to be locked into always having somebody represented as a hero in your story. I think There are elements of storytelling like surprise and suspense and different things that you can incorporate into a story, you know, and to get them engaged in the data, you know, it might be more of, you know, like to go back to your example, you know, how do you think revenue really comes together? You know, when we see a drop in revenue, what are all the different ways that that can happen? And maybe it’s part of your audience, like how deep do they know the data? Now, the one thing I think you have to be cautious about is what I call the analyst journey. So that’s going down the path of, well, first I looked here, and then I took the data and I ran a linear regression, and then that didn’t reveal anything. And then I got this other data set. When I combined it with this one, then that’s when, and it’s like, who cares? It’s the story of me. It’s a story. Yeah.

00:15:47.49 [Michael Helbling]: Yeah. So nobody cares about the story of you. Don’t give them the book. Give them the movie.

00:15:51.89 [Tim Wilson]: Right. Yeah. But take the Wizard of Oz. I feel like there are times, yeah, everybody’s seen that movie a few times where It feels like you are really just bouncing along with a bunch of adventure and excitement and events. But the real, I mean, you’re really kind of waiting for them to realize that, oh, people are getting self-realization about themselves. But the real kind of climax is that the wizard’s behind the curtain. Sorry. I didn’t say spoiler alert for anyone who hasn’t seen the Wizard of Oz. Oh, boy, we’re going to get letters. The tension between you know when do you want to have the reveal except like that maybe it’s it goes hand in hand with giving the analyst journey and telling yourself that’s a story if you have an hour scheduled and you’re 45 minutes in. You either have to really have them hooked and interested if you haven’t actually given them the answer. So how do you balance that? You want to tell a story. You want it to resonate. You want to have some emotion and tapping those parts of the brain, but you don’t want to not give them the answer so far into the meeting that might have gotten up and left for one reason or another.

00:16:58.92 [Brent Dykes]: So again, it goes back to knowing your audience, and I think this is based on a conversation we had maybe a year or so ago, where what do you do with that executive who’s just like, give me the numbers, give me the data, give me the insight, and they’re not necessarily patient for that story to unfold and do the big reveal. So in those cases, with a lot of executives, they’re time compressed, they’re give it to me kind of mentality, and I think that’s where you shift to more of a trailer as a movie trailer instead of the movie, right? So you give them the teaser or the trailer, you give them the information, you know, like I’ve done this analysis and I found that I’ve discovered a $2.2 million opportunity for marketing campaigns, you know, how we can optimize them. And, you know, give them a little bit of the setting, a little bit of the setup. And that’s where I kind of say, you know, what you’re asking for at that point is permission to tell a story. And if they want to hear that story, then they’re like, tell me more. How did you get to that number? Oh, OK. Well, let me show you the number. Now you’ve got their attention. They’ve given you permission to take more of a story approach. And then you haven’t got an engaged audience at that point.

00:18:10.23 [Tim Wilson]: Of course, that’s a tall order for somebody who’s two or three years into their career. And they finally get an audience with the executive. And they’ve sweated bullets. have put together 75 slides and what you’re, I mean, I agree with you that it’s, well, you need to be flexible and you need to be, you need to have your strongest opening and you need to have a couple of different ways that it can go. Like I know I’ve found myself doing that saying, well, this is fundamentally going to go one of two or three different ways. And let me kind of think through how I’d want to run with it because I sort of know what the destination should be regardless as opposed to, you know, no, damn it, I’m going to tell you my story or no, I gave you the punchline and You didn’t you were supposed to ask the tell me more question and you didn’t so I’m screwed I don’t know is it just come with with practice if you if you go in thinking you’re gonna try to do that and then it’s just basically critiquing after the fact guy looking into what you could have done differently

00:19:04.35 [Brent Dykes]: Yeah, I mean, if I was in that situation where I had that bit, you know, this is my first big moment, my big opportunity with that executive, there’s no way I would go in there guessing on anything. I would be talking to their, you know, I try and get an audience with their direct reports to kind of say, hey, I’m going to be meeting with Nancy and Nancy, you know, this is my first big presentation. And, you know, she asked me to do this analysis. how, you know, so you’re going to be talking to her reports and saying, you know, how do you think she’d want to see this or how does she typically take data, you know, maybe even crafting. Here’s what I was thinking of showing her the data and then, and then her reports might say, oh, yeah, no, she’s going to want to see this and that, you know, if it really did rest a lot of, lot to rest on that one opportunity and I’ve invested a lot of work, I wouldn’t want to just screw it up. I think the key thing is to understand your audience. I had a recent experience where I had an executive telling me how our CMO would want to see things. got his advice on how to structure the presentation and how to handle things and the presentation went great. So, you know, getting that insider information is really critical because, you know, I’ve also screwed up. I’ve also gone into presentations and I’ve taken the wrong approach and then I’m either backpedaling or maybe modifying on the fly or, you know, or doing the Homer Simpson. I just miss my window of opportunity and now it’s going to be that much harder the next time I try and present something.

00:20:37.81 [Michael Helbling]: Yeah, and for situations where you might get surprised, it’s always good to have sort of that elevator pitch, or I always tell people, have a 30 second, five minute, and full version.

00:20:48.40 [Charles Barkley]: Right.

00:20:48.76 [Michael Helbling]: Because then you, you know, if they’ve got, oh, hey, what’s going on with this, blah, blah, blah. Oh, well, do you have a couple minutes, and then you can give them the five minute version, or if they’re, you know, you’re just in the elevator, and you’ve got until the door opens, you do the 30 second version. But yeah. So there’s a lot of times when the story that needs to be told isn’t just one person or one group’s data and story to tell. In fact, probably most large companies, that’s true. So what have you seen working well? And if I’m accidentally teeing up like a softball for a domo pitch, just avoid that. What about when your story is only part of the bigger story, What have you seen or what have you what do you advise people to do in those scenarios to craft sort of the the broader story or the I don’t know if it’s a meta narrative but the overarching narrative of the of the business and what should be done.

00:21:46.37 [Brent Dykes]: Yeah, I mean, I think it’s important, you know, you may have found an interesting insight in the data that you have access to. And if your spider senses are tingling that this is bigger than just, you know, the day that you’re currently accessing, then that would be where I think you got to be careful sometimes because you can get really excited about an insight that you see in the data. which can then be easily countered or better explained in another data set. And so before I even craft the story at that point, you know, I’m really still exploring the story. Do I have a story? Can I stand on the story? Is this insight that I have really something that we can run with? And it also goes back to action. You know, if you think about, you know, if you want to take action on this insight, Is that across multiple teams working collaboratively on taking action on that insight or is it isolated to the one team that the data that I have is influential on them. So I think those are some of the factors that I think of is do I have a sense that maybe this is bigger than just. or I need to confirm or bring in additional data to enrich the story I’m telling. And then obviously, what level am I telling the story? And if I’m looking at marketing data and I’m trying to influence marketers to do something, then that may be sufficient. If I’m trying to change a process that the company has in terms of how we operate, which touches marketing, but also maybe touches sales or touches customer service, then obviously I’m going to need to get help maybe from other people where I don’t know that data set. I’m going to have to go to an expert for the Salesforce data. I might have to go to an expert for Eloqua or some of these other systems where I don’t have the expertise in and at that point it may become more of a team effort than just me as the as the single data story tell it. Maybe we’re going to have to collaborate on this and tell a unified story across these different data sets that hopefully then influences whatever change or action you want to occur.

00:23:48.85 [Tim Wilson]: The upside of that is that you actually then probably wind up getting some cross-functional collaboration and therefore probably You start turning the ship a little bit in some small way with people from different departments who are working together. The flip side is it may be six months before you can actually get everybody on the boat to actually leave the dock.

00:24:11.02 [Michael Helbling]: It does take time. I used to actually run a weekly meeting in my analyst career called Storytime where I invited specific people from different groups and we would all bring our story or data, what we’re seeing and work together to sort of say, what’s the overarching message. And it was, it took a long time for us to realize that’s what we needed to do and that who the right people were from all the different teams.

00:24:40.13 [Tim Wilson]: Was that a, that was a team of analysts who were kind of validating that you’re not chasing something prematurely, you’re thinking it through and you’re collectively building the story or was this, you weren’t trying to convince, you weren’t telling the story.

00:24:51.40 [Michael Helbling]: There’s a lot of things that can influence outcomes, right? It could be our brilliant marketing campaign. It could be, you know, we just got a great product and frankly, no matter how you market it, it’s going to sell like hotcakes. It could be that unbeknownst to you, it’s over there going viral on, you know, Snapchat. So those kinds of things are the things that you want to gather people together to let you know, well, what are you seeing from how you look at this? And incorporate that all into the overarching story. And so it worked really well, because then when we would go into business review meetings or presentations, we would have that perspective from that other team to say, and what we’re hearing from other people is this is also You know, so on and so forth and then we can kind of bring a more holistic message together, which I think a lot of times certainly in digital can be lacking, you know, digital data and that’s, you know, me and Tim and probably most people who listen to this. podcast, that’s our specialty. But digital data always has to, for most companies, has to be translated back in. And so our ability to be effective with insights has a lot to do with how we translate our stories back into the business metrics. So I think those things are really important and useful.

00:26:17.16 [Brent Dykes]: Any opportunity to get additional context, especially when you see something weird in the data that you can’t explain, you know, being able to reach out to other people who may have additional context that can really, oh, here’s why, you know, an example of this was I was working with a customer and they had noticed a drop in their traffic on their site and this is a high-tech company and I went in and I was able to isolate it down to search traffic back in the days when we had keywords. Oh, a moment every those days.

00:26:48.45 [Tim Wilson]: Wait, wait, can we take a moment of silence for the yeah, hold on. We’re all pouring out a little bit of our drink.

00:26:57.11 [Brent Dykes]: And so the interesting thing was, as I was analyzing, I was noticing that a lot of this traffic was coming from MSN. And so this MSN traffic had no keywords associated with it, which was kind of weird. And so I was like, oh, OK, well, all of the drop in your traffic to your site is from this decrease in traffic coming from MSN with no search terms. And I thought that’s kind of weird. And then as soon as I brought that up to the customer, they were like, oh, oh, now I remember. Yeah, like MSN told us that we had a free trial promotional placement on their portal site for their small business center. And we got some traffic from them from that article or that placement or whatever. And then that expired recently. They have the context on their side I was just the external analyst doing this but I didn’t have the full context for what was going on within their business and they did. And just you know sometimes we don’t we if we don’t have the full context and it may it may be you’re. At the company even and you didn’t know that PR is doing something on their side with some kind of initiative or. Page search just rolled out a new vendor and they’re trying some new things and you never know unless you talk to people and you’re maybe even sharing the insight. I don’t know why this spiked or there’s this dip or why things changed this month. Any idea that you have, and then once they tell you, it’s like, oh, now you’ve got a story, you’ve got more color to the story that you can share with your stakeholders.

00:28:31.91 [Michael Helbling]: Yeah, no, that’s absolutely right. Context is so important for analysis.

00:28:37.47 [Tim Wilson]: And I guess this almost brings up the double-edged sword. If you tell a fantastic story that’s super memorable and then you realize how excited that you’re missing some context, then out.

00:28:47.87 [Michael Helbling]: Unwinding that there’s like well you don’t want it to be you want to be memorable enough that if you reinforce it it sticks but you know in case you have to walk it back well and actually though that’s another challenges that businesses face is influential people and good storytellers not necessarily great analysts. Grab a hold of data and decide they’re gonna. tell the story and they start moving people in a certain direction and you’re like, no, stop. What do you guys think about how do you manage that? It’s sort of a governance question, but it’s sort of a soft one, right? Who’s allowed to go in and give us insights and data stories?

00:29:32.34 [Brent Dykes]: I mean, I mean mistakes can be made and if somebody’s running with data that not necessarily the right data, you know, it’s a tricky situation. You have to look again at who those individuals are, what are the repercussions of changing or shedding the true light on something. I mean, there was a recent example that I heard of where a vendor was providing some metrics to one of our customers at Domo. And then we replicated essentially the rules or the way that they are tracking that data in Domo, and the numbers weren’t the same. And at that point, it’s like, oh, well, I guess Domo’s got it wrong. And then they went double checked again, verified that no, no, no, the way we’re tracking this information is correct. So the customer then went back to the vendor and said, you know, are you why we’re seeing different numbers here? Why what’s going on? And what happened was they had some rules in place that a few of those rules were actually not updated. And so actually the data that we saw in DOMO actually shed light on the spender hadn’t really updated their rules and actually they need to actually go and fix it on their side, which is interesting. But I guess the thing you’ve got to do when you’re looking at the data is maybe share the insights that you have with that individual or that team. and see if you can have them talk about the sources, how they get in the data. At the end of the day, hopefully these are data driven people and they respect the data. And sometimes it’s just an assumption. An example would be there’s this customer in Japan and they were seeing their revenue go down year for year. And everybody in the Japanese division of this e-commerce company were like, oh, that’s all because of the currency fluctuation that’s occurring. And basically, the executive in this org was basically watching people wash their hands of this drop in revenue. And so he actually had an analyst go in. and actually do the statistical analysis to verify, you know, is it the currency that’s the factor here? And the analyst was able to determine, no, it’s not. It’s got nothing to do with the currency. And once the executive had that information, he was able to go to his team and say, it is not about the currency at this point. And so that took away a, And maybe it was a mental blocker in their minds that they were powerless, you know, obviously you can’t influence currency to affect what you’re doing. But once he removed that, then they were like, oh, okay, well, let’s see what’s going on. And they were actually able to identify a significant enhancement to their marketing performance once they had removed the currency element from it. So, I mean, data has got to be the solution. And obviously you’re going to have different data sets and different opinions and things and and hopefully you know you can influence change or acceptance of the right data by showing you know revealing you know what’s going on and and it’s you know can be challenging it’s it’s hard to it’s hard for people you know obviously then it becomes personal where people feel embarrassed or maybe at fault for for using or trusting or making decisions on bad data I mean it it can be a delicate situation. And so I would recommend that you don’t start wielding this sort of data and the truth of data through lots of people. Remember that budgets could be shifted based on the new data you have. People can be embarrassed. Power can decrease. I mean, these are all interpersonal, emotional responses that can be generated to this. So I would be careful. And again, it goes back to your audience. Know your audience.

00:33:39.02 [Tim Wilson]: interesting is we’re talking and the whole I mean it is time-consuming right to say if I want to tell a good story I’ve got a one be damn sure that I’m that it’s an accurate story I have to craft the story I have to know my audience I may have to do some trial runs of telling the story because I have to edit it one of those things on the accuracy I’ve got my system, I mean the classic one, take media, display media, and they’ve got their pixels, and we’ve got our web analytics, and we’re looking at it. Nobody’s expecting them to match up. But the fact is, to me, I kind of want to collaborate and say, if we can come up with a, I don’t know, if we’re off by 20%, that’s really not that bad. If it’s trending in the same direction, these are totally different data collection mechanisms, I always feel much better when I have triangulated and sometimes it’s within the web analytics tool say well. I’m gonna look at look at the data this way now in theory I can make a segment and look at something totally different and it should sort of show the same thing okay once I’ve seen it twice and it’s showing directionally the same thing I’ve got a lot more confidence I tend to be. The bigger the insight or the aha, the more I want to go find another person to find the same thing with their data. It’s kind of the opposite of a single version of the truth. I want to find three different versions of the truth and say, you know what, they’re all close enough that we can get on the same page. And that’s a good way, I think, to usually loop those people in. And if they say, well, we’re seeing the exact opposite, well, then you’re probably uncovering a data issue, and there’s value in that too. I die a little bit. We can’t really count that as a business when at the end of the day that we found messed up data in our system. And we’ve been making decisions on that data for a while. But the fact is finding it is a hell of a lot better than not finding it and proceeding for another year or two. So I don’t know. It just took me down tangent of when people are like, no, my data is right. We’re not arguing about which data is right. We’re trying to figure out how do we reconcile it so that we’re comfortable that both are right enough that we’re all comfortable with the story that we’re telling.

00:35:46.38 [Michael Helbling]: No, I found that even combining data from different places, so I mean, the classic example is leveraging sort of like a voice of customer data set with your digital data. to provide a qualitative backbone to a quantitative data set is really helpful for kind of getting unity around that as well. Yeah, this is a great discussion. And I feel like we just don’t, I mean, we never have enough time to really cover it, but I feel like I could talk about this for about two more days, but we can’t. So let’s wrap up. But before we do that, there’s a segment we like to do on the show called Last Call, where we go around and talk about something we found that’s interesting or fun or cool that maybe people might want to check out.

00:36:30.45 [Brent Dykes]: Well, if you’re interested in this topic of data storytelling, I actually published a really comprehensive article on the subject on March 31st on Forbes, and the title of the article is Data Storytelling, the Essential Data Science Skill Everyone Needs. But yeah, it goes into a lot of the ideas that I have around data storytelling and and how powerful it can be for anyone, really, that uses data. And people who are using data today is expanding. It’s not just the analysts, not just the data scientists, but it’s everybody down to frontline employees sometimes who are trying to use data and tell a story.

00:37:11.48 [Tim Wilson]: You’re not by any, I mean, you’re, I don’t know the answer to this. You’re not slated for any upcoming conferences where you’re going to be conferences or webinars where you’ll be talking about data storytelling. I feel like I’ve seen you speak on it a few times and it’s very compelling and with visual and audio as opposed to just audio.

00:37:28.25 [Brent Dykes]: Right. Yeah. No, I don’t have anything planned, but I’m always open to people, you know, if they’re looking for somebody to talk on the subject, I really enjoy presenting on it and would love that opportunity.

00:37:39.84 [Tim Wilson]: Cool. Tim? So I’ll go a little more whimsical, a little older school. So the story of maths, I kind of stumbled across this on Netflix. And my youngest, my daughter, we watched them when she was 10. She’s now 11. And it’s kind of fascinating. It goes all the way back to the discovery of zero, how long we went without having zero and kind of early trigonometry and the need to be able to measure non-square areas to figure out land and taxes back, you know, way back when. It’s a four-episode series. If you’ve got a burgeoning analyst as a child, it’s appropriate to watch with them. The guy who does it is a British mathematician. So as John Albert would say, he sounds British, so it even sounds smarter and it’s fun to listen to. But it’s a neat little nerd Netflix time. Nice. What’s your last call, Mr. Helbling?

00:38:31.85 [Michael Helbling]: Well, I ran across something, and I feel like I’ve known about it, but ran across it again recently, and I just find it constantly amazing and useful. It’s a website where you can grab mock data for anything. It’s called mockaroo.com. But you just put in a couple simple things, hit the button, and it’ll spit out like, you know, a CSV file or something with just all that data, which is super great for mocking stuff up or, you know, getting some stuff because from time to time you want to do stuff like that. I mean, you know, work with work with real data most of the time.

00:39:08.13 [Tim Wilson]: Well, if you’re mocking up the dashboard, I mean, you’re right. Like you actually was credibility when you say, oh, every one of my little sparklines is exactly the same. And you try to tell them that no, that’s just the mockup. If you can throw some or throw some. It’s cool. Yeah, it’s cool.

00:39:21.95 [Michael Helbling]: Fun little tool. I can’t remember where I heard about it, but somebody told me about it. Is there a DOMO connector for Makaru?

00:39:29.00 [Tim Wilson]: That would be awesome.

00:39:30.06 [Brent Dykes]: We actually use Makaru on somewhere. Yeah, mockups and stuff. Oh, nice. I don’t know if there’s a connector. There might be.

00:39:35.77 [Tim Wilson]: Well, if you want to just share random data within your organization.

00:39:39.61 [Michael Helbling]: That’s awesome. Look at this amazing chart. Yeah, this is all random. What’s the story of this data? Chaos. No, but it does spit it out in a CSV, which would be very easy to import into a tool like DOMA. Or R. Or Tableau. Are we allowed to say that? Or Excel. Or Excel. Well, just open. Anyway, so this has been great. Brent, thank you so much for coming on the show. It’s been a great conversation. As people have been listening, as you’ve been listening, if you’ve got comments, if you’ve got questions, feel free to ask them or reach out to us on our Facebook page, also on the Measure Slack. We’d love to hear from you. And if you happen to be on iTunes near our page and you wanted to rate this podcast, we would love that. It does something for us. I think we win prizes.

00:40:45.90 [Tim Wilson]: We hear other podcasts really ask people to do that, so we feel like to put on our big boy pants, we should do the same.

00:40:51.93 [Michael Helbling]: We copy those podcasts and asking for the same thing. So at some point, there’s a winner and we just want to be in the running. Alright, thanks for listening. For my co-host Tim Wilson, thank you Brent Dykes. And remember, keep telling data stories.

00:41:12.31 [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. Facebook.com forward slash analytics hour or at analytics hour on Twitter.

00:41:27.33 [Charles Barkley]: So smart guys want to fit in, so they made up a term called analytic. Analytics don’t work.

00:41:35.50 [Michael Helbling]: Let’s get it on. Hi, everyone. I still hear background noise, Tim. What? What? What? Coming out on my door. We’ve been waiting for you.

00:41:58.47 [Tim Wilson]: I can’t hear any of that. I did not hear any of that. Well, if you did turn the fan down, so if you get too warm, you’re just going to take your shirt off. Yeah, JR Smith style.

00:42:09.90 [Michael Helbling]: What’s up? We’ve been natives. For the greater glory of, you know, analytics and something. I don’t know that about you, by the way. In fact, have we ever actually met in person before? Well, that’s woe to me, because I did not remember it. No. OK. I’m a professional. OK. I suffer for this podcast. So Brent, what is it?

00:42:43.62 [Tim Wilson]: Our moderator is not on his game tonight.

00:42:47.78 [Michael Helbling]: I’m getting there.

00:42:51.29 [Tim Wilson]: Well, and I think there’s also the, no, I agree. Well played. It’s rare, I think, for me to see where somebody’s gotten their hands on data. Well, I take it back. I’ve seen people just go on the thing. Oh, yeah. I’ll walk that one back.

00:43:08.83 [Michael Helbling]: I speak with pictures, mostly internet memes. Yeah. This could be a pain in the butt to edit.

00:43:18.93 [Michael Helbling]: I’m sorry.

00:43:21.57 [Tim Wilson]: Rock flag and data storytelling.

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