Transcription
Hello, this is Adel Neme from DataCap, and welcome to DataFramed, a podcast covering all things data and its impact on organizations across the world. Something we mentioned earlier in the year in our 2021 Data Trends Report is the rise of data storytelling, and how it's not going away anytime soon. Data storytelling is often called the last mile of analytics, as it's the final hurdle before data solutions are adopted within the organization. This is why I'm excited to chat with Brent Dykes on today's episode.
Brent Dykes is the Senior Director of Insights and Data Storytelling at Blast Analytics. He is also the author of "Effective Data Storytelling: How to Drive Change with Data Narrative and Visuals." Brent has more than 17 years of enterprise analytics experience at Omniture, Adobe, and Domo. His passion for data strategy and data storytelling comes from consulting with many industry leaders, including Nike, Microsoft, Sony, and Comcast. He is a regular Forbes contributor and has written more than 40 articles on different data-related topics. In 2016, Brent received the Most Influential Industry Contributor Award from the Digital Analytics Association. He is a popular speaker at conferences such as Strata, Web Summit, AdTech, Adobe Summit, and more. Brent holds an MBA from Brigham Young University and a BBA in Marketing from Simon Fraser University.
Throughout the episode, Brent and I talk about his background, what made him write the book on effective data storytelling, how data storytelling is often misinterpreted and misused, the psychology of storytelling and how humans are shaped to resonate with it, the role of empathy when creating data stories, the blueprint of a successful data story, what data scientists can do to become data storytellers, the future of augmented analytics and data storytelling, and more. If you enjoy today's conversation with Brent, make sure to also register for our upcoming webinar with him, where he'll be visually introducing us to many of the concepts discussed in today's episode. The link is in the show notes. If you want to check out previous episodes of the podcast and show notes, make sure to go to www.datacap.com/community/podcast.
[Music]
Brent, it's great to have you on the show. You know, as many listeners know, and this is something that you've mentioned in your book, working with data and presenting data insights is an integral part of the present and future of work. And I think your book on effective data storytelling is one of the best out there. Before we deep dive into effective data storytelling, can you give us a brief background about yourself and what led you to write the book on it?
Yeah, absolutely. Yeah, so my background is in marketing analytics, specifically digital analytics. I joined an analytics startup called Omniture, and then we were about four years into that, we were acquired by Adobe. And so I spent eight years in consulting, working with Fortune 1000 companies. And then I was four years, I was an evangelist for Adobe's analytics platform. And then after that, a former manager recruited me over to Domo. And if you've heard of Domo, they're a cloud-based BI solution, and I was there for four years. And then in 2020, I joined Blast Analytics and was establishing a data storytelling practice. And so getting into why I wrote the book, back when I was at Adobe, I saw the challenges that a lot of, you know, data professionals would face in sharing their insights effectively. And so I pitched the concept of doing a breakout session on data storytelling back in 2014, which would have been a part of Adobe's Summit, which is a customer conference that they have. And the session was a big hit. You know, we had a great, great attendance, a lot of interest. And that was kind of my first signal that maybe I was onto something. And then I started to deliver it at different conferences. And a lot of times, people would come up to me after my session and say, "Great session, right? Do you have a book?" And I say, "No, I don't have a book," or at least not a book on this topic. And then another kind of signal came when I wrote an article. I'm a Forbes contributor and I've written lots of articles on data-related topics, but my all-time most popular article has been one where I focused on data storytelling being an essential data science skill that everyone must master. And so I think when I had all those signals, I was like, "Okay, I gotta write. I gotta write a book on this because it's something, honestly, I'm super passionate about, and it's something that I think there's a real need out there." And I think we're just seeing more and more that need, you know, today, even, you know, 10 years ago, I think it's got even more important today than ever.
That's great. I'm really excited to discuss the ins and outs of data storytelling with you. I think that the term data storytelling is often obfuscated as it becomes like, kind of a buzzword or it's in a hype cycle, especially in the data space. Do you mind sharing your thoughts on how you think the term can be misused or misinterpreted, and what you think data storytelling should be?
Yeah, that was another reason why I wrote my book, because I was seeing the term being used, misused by in the media and also by analytics vendors. Obviously, it's a sexy term. I think when people talk about storytelling, and then you latch on the term "data" on there, you know, lots of people started grabbing on to it without really understanding what it is or even what it isn't. And so three misconceptions that I've seen that have kind of bubbled up. The first one being that data storytelling is just a synonym for data visualization. Definitely, data visualization is a core element of data storytelling. Often, we're dealing with complex data sets, right? And we need to share the information in a way that people that aren't as familiar with the data can comprehend and follow. But I really just see it as a means to an end, right? We're trying to tell a story with the data, we're trying to convince somebody to take an action or do something. And so the visualizations are just a means for getting people to understand our insights. And I would even say that we don't always need to have visualizations as part of a data story. I mean, I'm sure there's lots of, you probably, many of your listeners have probably heard great audio-only podcasts talk about facts and figures and tell really compelling data stories. So I don't think that visualizations, we can't look at data storytelling as a synonym for data visualization work. So that's the first misconception I've seen.
The other, another second misconception that I've seen is that dashboards tell data stories. I often, you know, I even worked for a vendor who would say that, "Oh, we'll help you to tell stories with your data using our dashboards." And that was kind of something that I struggled with because, you know, if we're talking about an automated dashboard, you know, you're building your dashboard, you're obviously pinpointing KPIs or key metrics that are really important, right? So you put those in the dashboard. But when I'm building that dashboard, I have no clue what's going to happen with that data. I don't know if it's going to spike up, spike down, completely flatline for forever. I don't know what the stories are that are going to come out of that dashboard. And so I think the key thing about a dashboard is it's an exploratory tool, really. I mean, it's more geared towards helping to share and disseminate information, but it's not really, dashboards aren't designed to really communicate a specific insight in the data. And so I think that's another misconception that I saw.
And then the third misconception that I see often is that, "Oh, we just put, you know, for the narrative part, to turn something into a data story, I would just slap some text in there that could be just, you know, to go along with the visuals. Oh, we're telling a story now." And I'm not bashing on annotations and commentary, those are very important to storytelling. But that's only a piece of storytelling. You know, I think we need to broaden our perspective to think about, you know, in terms of like the narrative structure, the flow of how we're sharing information, how we organize, and how we emphasize things in our findings and with our observations and insights. That's really how storytelling comes together. And there's lots of facets to that. But it's, you know, it's not just about some text with a data visualization. That's not storytelling by itself.
So I'm excited to define what data storytelling is with you. But first, I'd like to uncover the value of data storytelling and why it's so important. So one of the key notions mentioned throughout the book is how data storytelling can be used as an agent or tool for change. Often, when organizations or people are confronted with data that challenges a status quo of some sort, there is natural resistance to change. I'm sure many data scientists listening in have faced some of these resistance when talking with business units, business executives. How can data stories be used as a device to create organizational change?
Yeah, I think one of the key things that I think we overlook, often, is that what is an insight? An insight represents a potential change. I mean, an insight invites change. It's we're basically saying, "Stop doing this and do more of this," or "Let's invest less money here and invest more over here." Where basically, when we get an insight, and one of the things when I was writing my book, a reviewer mentioned, "Like, you talk a lot about insight, so you're going to define what an insight is." And that kind of caught me, and I was like, "Oh, I wonder what an insight actually is." And so I started looking at definitions, and there's a lot of useless dictionary definitions of insight, and so I was finding lots of those, and they weren't that helpful. And then I came across one from a psychologist by the name of Gary Klein. And he defined it in his book as, "It's an unexpected shift in our understanding." And for me, that's that really embodied what an insight is for me, because, you know, it catches our attention because it kind of shakes our understanding of the world as it is. And then what do we want to do? Like, if it's in our ability to act on that insight, because it's really just an insight for ourselves, our personal insight, for or maybe for our role at our company, we can act on it ourselves, and we don't need to tell a data story. However, when it's a situation where, "Okay, I've got an insight, it could change how our team operates or what our company's going to do or department or whatever it is," I'm going to have to get buy-in, I'm going to have to get maybe resources, budget, support to kind of make something happen. And that's when we need to tell data stories, because we're basically sharing an insight that's going to drive a change. And as we know, you know, people are always going to be resistant to change. And so that's why communicating our insights effectively is so critical. And the key thing here, you know, we've done a lot of work to find an insight. You know, all of the capturing of the data, processing of the data, organizing, analyzing it, would build a model, you know, and then we come away with this insight. All of that work will be for naught if we're unable to communicate it clearly to other people and have them consider it and act on it. And so it's kind of like, you know, often we talk about it being the last mile of analytics, that, you know, we do all this work, and then if we, you know, I've seen this happen all the time where an insight is not communicated clearly, and all of that work that was preceded it is undone. And so I think, I think a lot of data science, data scientists and analytics professionals are getting, you know, they're realizing that I can't squander all of my hard work by simply not communicating my ideas clearly and in an effective manner. So that's why data storytelling is going to help with organizational change, because if we can communicate our ideas and our insights clearly, it's going to help the organization to embrace that change and move forward.
Couldn't agree more. And in the second and third chapter of your book, you really deep dive into not only just the value of storytelling, but kind of the science and the psychology behind storytelling and how it resonates with humans. Do you mind sharing some of your thinking around how data storytelling resonates with people when influencing decision-making and creating organizational change?
Yeah, no, I think it is a key factor in influencing decision-making. And one of the key things is that I don't think we fully appreciate how much we rely on storytelling in our daily lives. You know, it's deeply seated in who we are as human beings. I mean, from all the books that we read, to all the movies and the media, and then even, you know, as we interact with colleagues at work, we're sharing stories. You know, "What happened on the weekend?" or "Why isn't that team working?" "Oh, let me tell you why." You know, or different things. I mean, with their family, with their kids, sharing stories. "Oh, you know, oh, that sucks. What happened to you with your friend there?" "Let me tell you, you know, I had something similar happening with one of my friends." You know, and we're constantly storytelling. Even when we turn out the lights, you know, our brain doesn't shut off, it then starts to formulate stories in our dreams. And so it's a big part of who we are as human beings. And even going back, there was a study done in the early '70s where researchers studied one of the last nomadic tribes in Botswana, and they discovered that 80% of the campfire discussions centered around storytelling. And that's really how they taught the next generation. You know, like, "Oh, when you're, you know, by when you're looking at that plant when it's yellow, don't eat it. Wait until it turns purple, then it's going to be edible and not poisonous." You know, and they'll tell the story of that. And that's really how we form a community, how we connect with other people. And there's two reasons why stories beat statistics: and they are because two things: one, because they're more memorable, and two, because they're more persuasive. And there's a couple of examples of this that I borrow from Chip and Dan Heath's book, "Made to Stick." I don't know if you're familiar with it, excellent book on communication. But in one of their examples, Chip Heath is actually a professor at Stanford University, and he actually teaches a communications class. And he puts them through an exercise where he gives the students a bunch of data points around a particular topic. He asks them to take a position on it and then create a short presentation where they incorporate these data points into their presentation. Now, one in 10 of these students will actually also include an anecdote or a short story or something that they weave into their presentations. And so the students present to each other, they kind of rate each other on how they did, give feedback. And then they think that the exercise is over. But about 10 minutes later, Chip Heath comes back to them and says, "Okay, how many of you remember any of the statistics?" And only about 5% of them could remember any of the statistics. But in terms of those stories that were shared, 63% of them could remember those stories. And so that's just an example of how stories can be more memorable than statistics.
Now, on the persuasion side, there's another story that they share in the book that I also share in my book, which comes from Carnegie Mellon University, where they did a study with a bunch of students. They had them take a technology survey. The students complete that survey, and then that's where the experiment begins because they give them five one-dollar bills and they say, "Hey, here's this charity, it's a real charity, Save the Children." And they had a brochure for this, and they shared the brochure with them. And now there's two versions of the brochure. One version of the brochure had a bunch of data around the suffering of children in Africa due to famine or war or illnesses and different things, a lot of statistics and data. And then another version of that just talked about Rokia, a seven-year-old girl from Mali, talked about the suffering of her family. And so they gave them this brochure, these two versions, and they asked them, "Would you like to donate to this charity from the five dollars that we've given you?" And more than double, the story version generated more than double that of the statistical version. Even though we're talking about just one person in her family, it was relatable, it connected, it connected with these people, and it encouraged them to donate at a much higher rate. So, you know, storytelling is super powerful. We need to tap into it so we can get the memorability and the persuasion benefits.
I think this also speaks to the testament of the power of empathy and really zeroing in as well on the concept of how stories meet statistics. One of the things that you mentioned throughout the book is how, and this is something I think data scientists will find a bit counter-intuitive, since they tend to be more analytical and much more evidence-based in their discussions. Do you mind highlighting the differences, especially when it comes to the psychology of storytelling, between how people are geared to react to facts versus how they are geared to react to stories?
Yeah, I mean, when I started in my career, I didn't realize how much I thought that if I had the logic, I had the facts, I had the data, bring that to a decision-maker, they're going to make a decision that's logical and well-reasoned. Well, as you get into your career, you realize, man, a lot of irrational and kind of disappointing decisions are being made. Not based on data. What is it being based on? Well, it's being based on emotion. And I think I underappreciated earlier in my career how much emotion plays a role in our decision-making. And it's interesting that, you know, if we look at how people react to facts, you know, one of the things they do when you approach somebody with facts, you know, they go on guard, they, you know, they're more skeptical, they don't want to be seen as deceived or tricked by the data. However, if we approach them with a story, all of a sudden, that intellectual guard goes down, and people become much more open-minded. They're not going to nitpick on the details as much because they want to see where the story is going. And even some psychologists have found that we can almost enter into a trance-like state called narrative transportation. And this is, and if anybody's familiar with the book by Daniel Kahneman, his great book "Thinking, Fast and Slow," he talks about two systems, System 1 and System 2. And really, that System 1 is the emotion, it's our intuitive system that kind of pre-processes information unconsciously before it's even passed to System 2, which is our analytical, conscious mind. And so really, what System 1 is always trying to do is trying to make sense of the world around us. It's trying to make sense of the data and the information. And when we present our data and facts with a narrative, all of a sudden, it's easier for the System 1 to process and to make sense of.
Okay, that's so great. And I think this marks a great segue. Did you really discuss the anatomy of a data story, of a data story, and the blueprint towards data storytelling that you describe in your book? Can you walk us through the key elements of a data story that you found, and how they interplay between each other to create an effective data story?
Yeah, so there's, you know, at a high level, there's a couple versions that I have, but let me choose the high-level version because really, there's three elements that come together in a data story. There's, there's, you know, no surprise here, I mean, everybody's gonna kind of get this, but I think it's interesting to look at the interconnections between them. So the first one is data, obviously, you know, we can't tell data stories without data. And then the next one, no surprise, visuals and narrative, right? Those three things. Now, the interesting thing is when we look at the intersections of these, we start to see the power of data storytelling. So we take data, right? If I gave you a bunch of raw data, gave it to a decision-maker and asked them to make a decision or kind of evaluate that data, there's a good chance they may not fully comprehend the data in front of them. They may interpret it in the wrong way, or they may get lost or come away very confused. And so what do we do? You know, if you think of it as like, kind of like a Venn diagram where these bubbles, I have this diagram in my book, but when we overlap narrative with visuals with the data, what are we doing? We're helping to explain the numbers, we're adding in that extra context, we're adding in some explanation to help them interpret the data the right way and fully comprehend the significance of what we're showing.
Now, the next thing that we want to do, obviously, going back to, if we just had a data table, dumped it on somebody's lap, there may be a good chance that they may not fully see the anomalies in the data, they may not see the patterns and the trends. And so that's why we visualize the data, and that enlightens people to things in the data that they would miss otherwise if it was just in a tabular format. And then the last connection between these bubbles is between the narrative and the visuals. And if you think about, you know, probably all of us are binge-watching some show on Netflix or whatever your local streaming services. And where it engages us, that combination of visual and narrative, you know, that's why we watch movies, that's why we watch shows on TV. It's very compelling for us as human beings. Now, what I like to say about the power of data storytelling now is, if we combine the right data with the right narrative and the right visuals, all of a sudden, we have something that's very powerful, something super potent that can really drive change and can change people's perspectives. And so, you know, those are the three key elements that come together in a data story.
So obviously, narrative is a key component of data storytelling. It's often the least emphasized when you talk about data visualization, data storytelling in general. And it's obviously one of the more difficult elements to master. What are some of the best practices you think data scientists can develop when weaving narratives into their data insights?
I totally agree that narrative is often the least emphasized and the most difficult to master. I actually was on a webinar and we did a poll of about 100 analysts, and two-thirds of the respondents indicated that it was of the three that I just mentioned, data, narrative, and visuals, narrative, two-thirds of them agreed that narrative was the hardest to kind of really work with and master. And so in my book, that was a key emphasis, and I devote an entire chapter to looking at narrative and how to focus on a narrative structure. You know, and I think there's lots of narrative models and frameworks out there that different people have interpreted different ways. But I basically break it down into four key steps. And the first step is to kind of establish the setting, the context of whatever you're looking at. You know, you might be looking at retail sales, and so you're showing what the trends are, you know, what's typically expected. And then you then you have a hook as part of that first section where you're saying, "But look what happened here. Sales went down," or "Sales spiked up." And so then there's it's kind of like an observation that you're making in the data that will intrigue the audience and get them interested in, "Oh, well, what happened?" or "What caused that?" or "Do we know what led to that?" And so now you're hooking your audience, getting them interested in your analysis.
Now, then you start to pull apart the issue of the observation to better understand it. Well, what could be causing this spike in this metric or this decrease in this metric? And like an onion, we're peeling the layers of the problem or the opportunity. And really diving into, I call this the rising insights. We're starting to share a little bit more insights into the business that the audience can appreciate. Now, we're building up to what I call our aha moment. You can think of that as the climax of your data story. It's your big takeaway. You know, if the audience doesn't remember anything else, you want them to remember your aha moment. And so that's your big conclusion. And typically, you know, depending on the situation, it might be something that we've monetized. We might have said, "Here's a problem, and it's going to cost us $3.5 million if we don't fix it," or "Here's an opportunity, and it looks like it's a half-million-dollar opportunity for this product line." Oh, wow. You get the attention of the audience.
Now, the fourth and final step of this is the solution and next steps, right? So we want to help the audience understand the urgency, but then also know what to do with it. You know, and then that's where maybe some additional analysis is necessary to kind of, "Hey, we've looked at the three options here. There's three options. Some have pros and cons, or maybe this one is going to generate the most revenue for the least amount of cost." You know, we've helped position the audience to make a decision and feel fully empowered to do so. And so I think having a narrative framework like this can weed out information that isn't essential to your data story. One of the biggest problems that I think analytics professionals and data scientists have is sharing too much information and overloading the audience. And so having a narrative structure like this to kind of guide you and kind of say, "You know, what is this really part of the flow of the story? Which of these areas does it make sense? Is it absolutely necessary?" Or maybe, "No, it's supplemental information. If I got a random question from somebody, I'd want to have this available." So what do I do? I put it in the appendix. It doesn't need to go into the story. And I think that's one of the benefits that people don't realize about having a narrative structure or flow in mind when you're sharing your insights is that it's going to streamline the information you're actually sharing.
That's great. And how, how strict do you think data scientists should be when adhering to the narrative structure or a framework? Do you think that this is something that's more of a plug-and-play and free around depending on the problem at hand, or is this something that is much more rigid to a certain extent when working with data and presenting data stories?
I mean, I don't like to have a super rigid. I kind of look at my framework as training wheels. You know, once you've kind of mastered that, you can take off the training wheels because you know how to ride your bike. And I've had pushback from different people on my narrative model. They'll say, some people say, "Well, maybe I don't have the aha moment yet. Maybe I'm still, I'm still in the rising insights where we're finding some information and we're targeting an aha moment in a particular area." But I'm like, "Yeah, like let's share that information. Let's share that with the business, get them on board. Maybe they redirect you in a different direction to a new aha moment." Another example is, I've had some people push back and say, "I don't know what the next steps are. I don't know what the solution is. I don't have that. I don't even feel comfortable proposing a solution to the business team." Well, in that case, you share everything up to the aha moment and then turn it into a discussion. And you get them to talk through things. And then maybe you can then, once they start to gravitate to a particular solution, then you can say, "Let me dig into that. Maybe I can do some additional analysis, verify. Let's do some tests or let me gather some more information or data now that I have a clear picture of what you think the potential solution is." So this is all just, I view it as not hard and fast. And there's even the situation where I've had people say, "This is great. Having a story like this is great. It's not going to work for my executives because they're going to say, they're not going to be patient to wait for a whole story to unfold. They're going to say, 'Well, what's the number? What's the result?'" And so then what I did is, and I talk about this in the book, I talk about having not a data story, but a data trailer. So it's almost like a movie trailer version of your. And it's like the worst movie trailer because it gives away the climax, right? It gives away that so-and-so dies. But what you're doing with that data trailer, and really what we do is we just take the hook and we take the aha, we combine that into a shorter version of our presentation. And what are we doing? We are asking permission for them to listen to the full story. And so we're giving them the sound, by giving them the information, they can make a quick decision, say, "Oh, tell me more," or "How did you get to that conclusion?" or "How do you know it's that?" Oh, okay, well, let me walk you through the rest of the story. And now we have permission to take them into the story. So, yeah, there's, you know, that's kind of a, you know, three scenarios where maybe the model doesn't necessarily work, but we can modify, we can adapt. And I view it as training wheels.
That's great. And you've hinted at something here related to audience. Like the executive, my executives require a data trailer rather than a story. So audiences are also very important. What makes an effective data story? The priorities of a data science leader, for example, and the way they respond to information is not necessarily the same as a business executive or a leader and so on and so forth. How can data storytellers adjust their data stories to fit the audience's problems and their expectations?
Yeah, I mean, I always think that our data story should be about the audience's problems. You know, we should go into this. And in my book, I talk about a simple framework that I like to use based on understanding four key dimensions of a particular audience. So if you know who you're going to be presenting to, or you know who you're going to be doing some work for, analysis work for, there's four key areas you want to understand in depth. And that's what is the problem they're trying to solve? You know, there may be a top-of-mind problem that they're trying to address. "We are losing customers." "We're not retaining our customers." Or "We're struggling to generate leads for our sales team." Whatever that problem. And maybe there's more than one problem, but really digging into understanding, you know, what is keeping them awake at night, you know, the key stakeholders. And then the flip side of that is, okay, so we know what the problem is, what is the desired outcome? Meaning, okay, so you're struggling to generate leads for the sales team, well, what do you want to achieve? "Oh, we want to double the number of leads that we're generating for our sales team this quarter." Oh, awesome. Okay, so now I know the magnitude of what you're targeting, what you're trying to achieve. That is super important.
The third dimension is, okay, what activities or actions or initiatives do you have in place already to move you from this problem state to the desired future state? So in other words, you know, if it's lead generation that's the issue, then they might say, "Well, we're completely revamping. We're really investing more in digital. Traditionally, we've done a lot of traditional marketing, print, radio, media, whatever. And now we're switching to digital." Oh, okay. So in terms of the actions and activities that are really important to them, in terms of their spending budget, they're really looking at these things on a frequent basis, they're top of mind, they've got resources assigned. These are the areas that we want to investigate with their analysis. We want to probe and see, okay, let's see how their digital campaigns are working. And then lastly, the last thing, fourth key dimension, is the measures, you know, the metrics, the KPIs. You know, what are you really basing, why is the problem a problem? Well, it's because we're underperforming on this metric. Or what's the target that you have for your outcome? Oh, it's based on this metric. Or how are you evaluating your different initiatives? Oh, we're measuring them by these, whether we're achieving these success with these metrics. So I think if you can go into any situation to kind of work, try to tell data stories, it's going to depend on your understanding of these four dimensions. And if you understand these really well, you're going to have meaningful data stories to share with this audience. You're not going to have to say, "Hey, you guys haven't been thinking of this, but here's something over in left field that nobody's thought of." I mean, they might say, "Well, you know, our priorities are these, and this isn't related to one of our priorities." So thank you for that interesting information, but we're going to get back to work on these priorities that we have. And so we need to be grounded in what's top of mind, what's important to them, and then base our data stories on something that's aligned with those priorities.
So given that nailing a message that fits directly to the audience at hand is so important to the success and viability of any given data story, do you think that data scientists should work hand in hand in an agile and iterative manner with their audience on building a data story that fits their expectations and their needs, or do you think a siloed approach is better off?
Yeah, I would say rather than working in a silo or a vacuum, I would always advocate to work hand-in-hand with the business. You know, unless you have significant domain expertise and knowledge that that business team has, it's going to be difficult for us to deliver impactful data stories without working with the business teams and engaging them on some level. Now, obviously, you're not going to want to drag them through the weeds with you, but check-ins and sharing kind of progress and kind of brainstorming problems or issues, I think that's great. Because at the end of the day, you're going to end up with a product, or something that's, when I say product, I mean data story, you're going to end up with something that's going to be super relevant, super impactful, and right on target. You know, the worst thing we can do is kind of go off on our own and kind of come up, spend hours and hours, come back with something that's not directly relevant to what they're focused on. And then what does that do? That impacts our credibility, because they're like, "You're not in tune. You don't feel the pain that we're feeling right now. We need help, and you're not providing it. You're just doing whatever you want to do." So I think it's really important to be as aligned with the business teams as you can.
I think going on top of that as well, it hurts the credibility of data science as a practice within any organization as well when it doesn't solve the business problems or communicate the solution of data science. So one thing that's important as well that we discussed slightly earlier in our discussion is that you mentioned clearly in your book is how data storytelling is this ubiquitous skill that will define the future of work. So with that in mind, this is a skill that everyone needs to adopt, not just data scientists. How important do you view the role then of organizational data literacy when creating and consuming effective data stories?
Yeah, I think it's super important that organizations invest in establishing a basic level of data literacy, primarily so that you can have data storytellers. I mean, obviously, data scientists are in a great position to tell data stories, but other people within the business that are savvy with the data, or at least have a knowledge of the data, can be put into a position to share insights and start to tell data stories. And I view data storytelling as a great way to cultivate data literacy skills across the organization, because as more people are sharing and telling data stories, then more people are being exposed to data on a regular basis, and they're being helped with how to interpret it correctly. I view data storytellers acting as guides. They're walking people through the numbers. And the more people that have these guides helping them to interpret the data the right way, it's going to only enhance and develop their own data skills in general. So I see data storytelling going hand in hand with data literacy, both in terms of we need a basic level of data literacy to be a data storyteller, but then also as we have data storytellers telling data stories, that's going to foster an environment where the data literacy skills are just going to naturally be enhanced and helped.
I couldn't agree more. And I think this marks a great segue to discuss just how practitioners, data scientists, or even business subject matter experts can become better data storytellers today. You know, the most widely known element of data storytelling is something that we alluded to as well earlier in our chat, is data visualization. What do you think are some of the best practices you can offer data storytellers when crafting data visualizations that help them anchor better data stories?
Yeah, I think the biggest thing that I point out to data professionals and data scientists is that you need to recognize the difference between exploratory data visualizations and explanatory ones. And I've been saying for many years that the chart that helped you discover an insight may not be the best one to communicate that same insight to others. And so you may need to either edit your visualization to make it more palatable to others or completely shift it to a different visualization that perhaps communicates the data more effectively and clearly. You may have to remove some of the noise, right? When we're exploring the data, we're going to have all 10 categories that we're looking at. But then when we isolate that there's a problem with a particular category or two, do we need all 10? No, maybe we just need the two that we found the problem in, and maybe a couple others for context. So those are the decisions. And I would say probably one of the most powerful tools that you have in your visual storytelling toolbox is color. And I would say, for example, when we're highlighting key points in our visualizations, we can use a bold color to really bring out a key data point to the foreground. We put it, we bring it to the foreground. And then what do we do with the rest of the data? Well, we might use grayscale to push it to the background. And so we can focus people's attention. We can, color is a super powerful tool for telling better data visuals, data stories. And so that would be my one recommendation: definitely explore how you can leverage color more in your explanatory data visualizations.
Couldn't agree more. I think really the power of color and the power of just using PowerPoint and presentation is so underrated in data science, even across any profession that involves any form of public speaking. A key theme that cuts across as well any effective data storytelling is trust in data and credibility. I think often times when we talk about data storytelling as a persuasion tool, there is a risk of falling into the trap of finding data that supports a story or an agenda, and not letting the data inform a story or an agenda. What are some of the pitfalls you think folks can run into when maintaining credibility of their data stories, and how do you alleviate some of these pitfalls?
Yeah, I mean, being credible as a data storyteller is super important. You know, we want people, because if audiences don't trust us, it doesn't matter how good our data story is. It's going to fall apart because they're going to say, "Well, you know, so-and-so doesn't really validate their data very well," or "We remember that other story they told us where there's a bunch of errors in the data, and we had a really bad..." That can happen, and we want to avoid that. Now, on the flip side of that, one of the things that I see is that sometimes on the analytics and data science side is where we're worried that we're, we want to be perceived as credible. We want, we don't want to, I would say we're almost too careful in the sense that we want to share with them, "Okay, let me show you all the steps I took to reach this conclusion or find this insight." You know, and so we're walking, and I compare this analogy to, if we baked a cake, and we're like, "Okay, let me tell you how I sifted the flour two times before I, and I only used organic butter, and I, and all these little steps that we go through." Nobody cares at the end of the day. The audience typically is more focused on, "Hey, can I have a slice of that cake? And does it taste good? And hopefully it doesn't make me sick?" But they don't care about all these steps that you took to bake the cake. And I think the same thing goes into some of our analytics projects and data science projects is that we over-index on explaining all the details and trying to make sure that people are confident in our numbers. And really, potentially what that can do is we can lose our audience, because they view a lot of this information as, you know, kind of Charlie Brown's teacher, and this is just noise to them. Whereas we want to make sure that we are always, you know, being, using integrity with how we approach our numbers and, and obviously being diligent on our checking things and making sure that we're double-checking things. But I don't think that necessarily means that we have to bombard them with extraneous information that's not going to help them to understand the insight.
Okay, that's great. And obviously, data literacy, statistical thinking, data.
Visualization skills are really important when creating data stories. Uh, what do you think are the most important skill sets, out of the outside of those that data scientists or even data practitioners of all kind that need to become, need to learn to become effective data storytellers?
Yeah, I mean, all of what you've mentioned are really important. Uh, and then you, you also mentioned the importance of empathy, you know, having empathy for our audiences. I think that's really important. I would also add critical thinking skills and curiosity. Obviously, you know, to have good insights, you, you know, sometimes to find a good insight, you have to be very, uh, you know, passionately curious. As I've talked about, I mean, I think that was, uh, Albert Einstein mentioned that being passionately curious. You know, he said he, you know, probably being super humble, but he said, I don't feel like I'm, you know, I'm paraphrasing what he said, but he says, I don't think I'm more intelligent than anybody else, but what I am, I am passionately curious. You know, and I think that's a key skill for a data storyteller. And again, going back to the critical thinking skills. Last fall, I taught a course on data storytelling, data visualization, and I noticed there was a student that was really struggling in my class, and she was becoming increasingly frustrated with some of the, the real-world assignments that I was giving them because they were a little less defined. And as I stepped back and looked at the difference between her approach and and the other students that seemed to be performing well with the assignments, what was the critical thinking skills? I, I think she lacked a really well-developed critical thinking skills to really help us excel on those assignments. And so, I, I think that's also a key, um, thing that we need as data storytellers.
How do you address this lack of critical thinking skills in general? Uh, whether like, how do, what are the best practices that you think could be could be integrated within educational systems or within data science programs, really instill these skills?
Yeah, I mean, I think one of the best courses I took in college was a critical thinking class, which was part of the philosophy program. And, you know, I'll, I'll come clean on something. The first time I took it, I actually got one of my worst grades in my entire academic career. I got, I got a C minus. And what happened was, I was at the time, it was like my second semester, and I could, I was taking it up, I was traveling to this university that was a long commute for me, and I was doing it after work. And so I'd work, you know, full-time almost the whole week, and then I would have a couple of night classes. And so this is a night class that I had, and I was so exhausted after taking, after working all day and then going to this class until like eight or nine o'clock. I didn't hang around to do the office hours. I didn't ask questions of like, hey, I didn't totally understand this concept. And I, I just kind of, I'm done, I'm just going back. And so I got a C minus, and that was really frustrating for me. So I took the class again later. You know, I wasn't working full-time. I took the class, thoroughly enjoyed it, invested the time to really understand the logic and the reasoning. And for me, that was a phenomenal class that really opened my eyes to how, you know, especially in today's world with, you know, the arguments and kind of how people form arguments and the logic and all that. Um, it was just a fantastic course, and I did get an A in it. So I, I, I went from a C minus to an A in that same class.
So that, that's awesome. You redeemed that.
I redeemed myself.
You redeemed yourself. That's great to hear. So, uh, you know, another important aspect of storytelling is communication and presenting skills, right? Uh, what are some of the best practices there that you can offer, uh, practitioners to become better communicators in general? What are some of the skills or the ways you can improve that skill set?
Yeah, I, I think this is important. I mean, practice makes perfect, right? The more you do it, the more. And so it may be looking for opportunities to present your findings to people and and seizing on those opportunities. And and I think one of the, I, I did, I recently did a like a LinkedIn post about this, and I said, the one speaking, the one differentiator that I've seen that has the biggest impact is just practice. Like, like practicing something. You know, in some cases, you know, some cases a presentation is not going to require you to practice multiple times. You know, it's not as critical. But, you know, if you're presenting externally at a conference or maybe it's a big executive meeting, you know, where a lot could ride on it, you know, really going through your presentation over and over and over to the point where you feel super comfortable with what to say on each slide, that's going to be huge for you because then you can focus on other things like eye contact. You can focus on, um, you know, the movements of, you know, your hand gestures. And and and sometimes you'll see when you, when you're looking at conferences, you'll see people pacing a lot, you know, and so it almost becomes an annoying pacing because they, they're like, uh, you know, because they're not paying attention to themselves because they're not realizing that they're nervously pacing. So when you've really got your presentation down where you don't have to worry about it, you could do it cold, then you could then you can kind of focus on some of these other things. And and you just, you know, you're just gonna have a way better experience as a presenter. And your audiences, you know, when they see you confident in in what you're talking about, it's gonna make a huge difference for them too, because they're gonna believe in your numbers, they're gonna believe in what you're presenting. And and they're gonna have a higher, um, you know, uh, what's what's the word, appreciation for who you are as a presenter and kind of the, you know, how committed you are to to presenting the information effectively.
That is awesome. Um, so before we wrap up, uh, Brent, what do you think are some of the trends that you see in data storytelling? What do you see the future of data storytelling and how it fits into the data scientist's skill set?
Yeah, I mean, I see it as an evolving skill that still needs attention. You know, obviously, we're still working on data literacy, right? So a lot of organizations are still struggling with illiteracy. But I see, you know, data storytelling coming shortly after that. As as organizations are starting to establish a certain level of data literacy, the next step will be, okay, so everybody has a basic or most people have a basic understanding of data, and we've got, we've democratized the data, we've, you know, most people have data in their hands now. How do we build a culture around, uh, you know, a culture where people are are confident and capable of sharing compelling data stories? You know, and and so I see, you know, one trend will be more education in this area. Um, you know, obviously having a book in this area is great because, you know, I can, I can help contribute to that education process that that companies and individuals are going through. Uh, the other thing that I see, you know, is how much is technology going to dip into the data storytelling world? We're starting to see that a little bit where you'll see some types of reporting are now automated, you know, with with a certain level of descriptive text and things. But, you know, I see technologies that augment the abilities of of storytellers to tell their stories is going to be really important, rather than full automation of data storytelling where robots and algorithms are going to be telling all the stories. I don't see that necessarily anytime soon. Um, in a couple of areas where, you know, algorithms and and, uh, automated approaches are going to fail is is having the full context of what's going on. Sometimes the data or the context is not reflected in the numbers. You know, it lives outside of the numbers. And so, you know, automating that will be challenging. And then the other thing is tailoring to the audience, right? The the presentation or the data story I tell to the IT team who or the technical team that's going to be implementing a change, it's going to be much different than the story I tell to the CEO or CXO or or or even the middle manager in between those. So, you know, often we need to tell a, you know, even with the same insights and data, we need to customize that to the audience. So that that's another that's going to be another kind of challenge for an automated approach. But, but I'm not going to say never. And and I and I definitely think that, you know, augmentation of of what we're doing as data storytellers can be aided by technology. AI, all of that can be, you know, hugely beneficial.
That is very exciting. Brent, thank you so much. Finally, Brent, do you have any final call to actions before we wrap up today's podcast?
Well, if you're interested in data storytelling, go check out my book, Effective Data Storytelling: How to Drive Change with Data Narrative and Visuals. Um, it's, yeah, should be available at a bookstore near you.
That is awesome. Thank you so much, Brent. We'll make sure to include it in the show notes.
Thanks, Adele. Thanks for the opportunity. It's been great.
That's it for today's episode of Data Framed. Thanks for being with us. I really enjoyed Brent's insights around effective data storytelling and all of the angles by which he can cover it. If you enjoyed this podcast, make sure to leave a review on iTunes. Our next episode will be with Rick Scavetta and Boyan Angelov on their new book, Python and R for Modern Data Scientists, and how it draws a path to the fall of the language wars. I hope it will be useful for you, and we hope to catch you next time on Data Framed.
[Music]