When was the last time you had an important fact to share but nobody seemed to care? This is for you if you want to learn how to communicate insights effectively and make things happen. My guest today is a master data storyteller: Brent Dykes.
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When was the last time you had an important fact to share, but nobody seemed to care? Or the last time that you dealt with a very busy executive and they just didn't seem to have time to listen to you?
This episode is for folks who want to communicate insights and do it effectively so that the right people can act on them and make things happen. My guest today is Brent Dykes. Brent has spent over 15 years in the analytics industry consulting with large brands such as Microsoft and Amazon, he has a formal education in marketing and brings a unique perspective to data storytelling thanks to a mix of qualitative and quantitative skills.
Brent recently published a new book: Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals which I found it incredibly insightful.
The biggest takeaway from today is that effective storytelling might well be the most future-proof skills you'll ever learn. Time to learn from Brent Dykes.
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Loris Marini: When was the last time you had an important fact to share, but nobody seemed to care? The last time that you dealt with a very busy executive and they just didn't seem to have time to listen to you? Perhaps you attempted to provide a summary, something really short, but even that somehow doesn't cut it. This episode is for folks who want to communicate insights and do it effectively so that the right people can act on them and can initiate changes so the stuff actually happens.
The biggest takeaway from today is that while the world is constantly changing, our biological hardware hasn't changed much in the last thousand years or so. This means that the mechanics of our attention might be the only constant really today and once we understand it, we can use it to our advantage. Storytelling and the skills we'll cover today might well be the most future-proof skills you'll ever develop. That's why they deserve one full hour of conversation with my guest today.
Just a note that this is not the first time we talked about data storytelling. We did it in a couple of different contexts in episode nine, with Gilbert Eijkelenboom. We talked about people skills for analytical thinkers. We covered the insights we got from neuroscience, evolutionary theory, the brain’s system one, and system two. In episode 10, with Scott Taylor, we looked at storytelling about data in the context of getting the business to understand that data management is a strategic imperative for the company and that they need to invest to make sure that the right data reaches the right people are the right time.
Today, we focus on communicating insights and getting people to act on those insights. My guest today is Brent Dykes. Brent has spent over 15 years in the analytics industry consulting with large brands such as Microsoft and Amazon. Brent received formal education in marketing and has a mix of qualitative and quantitative skills that, I believe, really allow him to bring this unique perspective to data storytelling. Brent speaks regularly at top conferences and workshops around the world. In 2016, the Digital Analytics Association recognized his work with the Most Influential Industry Contributor award.
He writes for Forbes, and published two books on digital analytics. He recently published a new book: Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals, which I’ve read half of. I found it incredibly insightful.
Time to learn from Brent Dykes. Brent, welcome to the show, and thanks for joining me.
Brent Dykes: Thank you, glad to be here.
Loris Marini: Brent, let's start from the basics. How do we define insight? How should we think about it?
Brent Dykes: Yeah, that's an interesting question. When I was writing this book Effective Data Storytelling, I had a couple of reviewers come back to me and say, “Brent, you haven't defined what an insight is.” I realized as I was going through the chapter, I was like, “Oh, well I talked about insights a lot, but I hadn't really defined what it was.” I had to stop and pause and think, what is my definition of an insight? I think like many of us, we turned to dictionaries or something like that to get inspiration. When I looked at those definitions, I wasn't really happy with them. I was like, “This is not really what an insight is for me.”
I kept looking and then a friend of mine pointed me to a definition by Gary Klein, a psychologist. He said that you define insight as an unexpected shift in the way we understand something. For me, that was a big thing because I was like, “Ah, it's really about that unexpected shift in how we understand something.”
Let me give you an example of what would be an insight would be. We thought that our customers loved feature A of our product, and that was what we thought everybody loved. That's why they bought our product. Maybe we do some customer research. We do some surveys. We find out actually it's not feature A, it's feature B. all this time. We've been focused on feature A in terms of our marketing, in terms of our product engineering, and everything.
This new insight, this new discovery, completely changes how we view what we're doing. We're like, “Oh my gosh, we've been featuring the wrong product feature in our marketing. We've been throwing money at the wrong thing. Really, we should be developing this other feature that was under the radar that we hadn't thought of.” That's an example of an insight, and it's really that unexpected shift in our understanding.
I would say it's got to be meaningful as well. The definition doesn't capture that, but if we're focused on what's important to the business in terms of the goals and the objectives and what they're trying to achieve, we're going to have insights that are meaningful, that are going to have an impact on the business. I think that's the key thing.
A lot of time, I think sometimes we use the term insight very loosely in analytics. That's a problem because we start to say, “Oh, here's something interesting,” or “Here's something unusual in the data.” The big thing between an insight and an observation is an observation really just focuses on the what. Oh, sales are up 23%. Oh, that's higher than it was the previous the same time period last year. Okay. Well, that's interesting, but it doesn't explain what's going on or why that's occurred.
Looking at it deeper and doing the analysis and finding an insight and saying, “We are up 23% because our competitor just had their main core product recalled and they're going through a massive product recall. Now, customers are coming to us for our product as a replacement for this product that's been recalled.” That's an insight and now we can do something with that insight. Now, it's like, “Oh, we've got an opportunity here. We can seize on this unfortunate event with our main competitor and promote our product as a solution to that problem for people who are looking for a particular product in that area.”
Loris Marini: I find this fascinating for a number of reasons. I suppose one is that we’re creatures of habit and we tend to stick to our models of the world and changing our minds is perhaps one of the hardest things ever.
Brent Dykes: Oh, absolutely.
Loris Marini: This leads me to think that once we find a new way of thinking or something that might challenge our beliefs or our existing models of the world, then we need to communicate. We need to tell the story to someone so that they also care.
I found it interesting in your book when you made the distinction between informing and communicating. As a person of science, being involved with research in academic settings for a long time, you get told from the very beginning, from day one that you need to stick to the facts because facts are objectives and they're not polluted by emotions and stories.
The thing is, that doesn't really work in the real world. You get out of academia and there are no abstracts. There are no reviewers that think exactly like you. There are normal human beings and they couldn't care less about the facts, or at least not to begin with.
What makes communication better than information? What's the difference between informing and communicating?
Brent Dykes: Yeah. There's a quote by Sydney J. Harris. He was a journalist. He equated informing to giving out and communication is getting through. I think the crux of it is: what are we doing when we're doing reporting? We're informing people. We're basically delivering information to them and we're hands-off. We're like, “Interpret it for yourself. I've done my job. I've given you the information.”
Whereas what we're trying to do with the data story is really communicate where we want to have that two-way communication. We want to make sure that we get through. That they interpret the information the correct way. That they get the value out of that information that we see in it. We want them to also find and extract the same value that we see in the information that we're communicating.
I like that he called it “giving it out” which is informing and getting through to them, which is the communication. For me, that's a critical difference between those two forms.
Loris Marini: Perhaps it's time to tell the story that you tell in your book, which, I must confess it was really shocking. I heard of something similar, but the way that you presented the anecdote was really regional and just got me thinking.
You got through. That was an example of me reading the thing and being like, “Oh, my God, I can't stop thinking about it.” It was the example of that medical practitioner that was involved with giving birth to babies. Tell me the story.
Brent Dykes: Yeah. Ignaz Semmelweis. Maybe some of your listeners have heard of him, he was a Hungarian doctor back in the 1800s. This was before we knew about germ theory. He worked at a Vienna hospital that would train young doctors and midwives and he inherited a problem. He was basically the chief administrator at this hospital.
They had two clinics and each clinic had a problem with something called childbed fever. A lot of women would come to deliver. They would get sick from this childbed fever and then die two or three days later. The interesting thing that he found was for the past five years, the doctor's clinic had a mortality rate that was more than double that of the midwives.
A lot of times with this university hospital, they were treating poor women prostitutes, and different people. It was a free service, but he actually would see the women on the street begging to not go to the doctor's clinic. They started to hear that you had a higher chance of dying in that clinic, compared to the midwives.
They looked at the two clinics side-by-side and tried to figure out why there was this difference? Why is one clinic, the doctor's clinic, worse than the midwives? At the time they believed in the theory of miasma, which is where they would associate bad smells or foul smells with disease. That was one thing that they looked at. They looked at the temperature of the rooms being different. Was one room more overcrowded than the other? They even looked at the delivery techniques of the midwives and the doctors to see if that was the cause. They couldn't figure it out until one day when Ignaz was actually out of town and a fellow colleague of his, another doctor, was performing an autopsy.
One of these women had died of childbed fever, and he was using it as a teaching opportunity with student doctors. One of the student doctors accidentally cut his hand while he was performing this autopsy. Sure enough, that wound got infected and he died shortly thereafter. When Ignaz raced back to find out that his friend had died, he had the difficult job of performing the autopsy on him. That's when he discovered that the pathology of how this man died was very similar to how these women were dying.
All of a sudden, he had that “aha!” moment. Again, they didn't know about germ theory, but he was wondering if there were some particles or something on the cadavers that they were transferred to these women in the clinic. The standard practice of the day at this hospital and many other hospitals that were training doctors was to have the doctors perform autopsies in the morning and then do their rounds of examinations and deliveries the rest of the day.
He was like, “We probably aren't washing our hands properly. We're transferring the disease to them.” Anyways, he instituted a policy where they would wash their hands in a chlorine lime solution. Immediately they went from a 12.2% mortality rate down to 2.2%, an 82% drop. For the next several months, it remained relatively low. Though it did start to tick up a little bit. That's when he discovered his first point of resistance where these young doctors are like, “Come on, Ignaz is off his rocker. There's no way that our hands being dirty is causing any problems. This is ridiculous.” They weren't adhering to his hand-washing policy.
Imagine you're washing your hands in chlorine lime. It's going to be stinky on your hands. They weren't adhering to his policy. He found out, cracked down on them, and said, “No, you're not going to be a part of this program if you don't wash your hands properly.” For the next year or so, he was able to get their mortality rate in this one doctor's clinic down to zero, for a couple of months. They had nobody die, which is unheard of.
The interesting thing at the end of this time period, for about 18 months, he had data showing that this hand-washing policy was very effective. Yet in the end when he was about to be renewed for his position, they wouldn't renew him. His superiors also didn't believe that hand-washing was the sole reason why these women were dying. He was basically rejected by the local medical community and he had to leave Vienna.
Loris Marini: Yeah. Knowing that he figured it out.
Brent Dykes: He knew. He figured it out, but he was a little bit passive. He waited 10 years before he published his findings. See, there was a statement from the book. I don't think I'll be able to quote verbatim, but he basically said he just expected his research or his findings to just take flight. That people, in time, would all figure out that hand-washing is a really good solution.
It never did. He waited 10 years before he published his research. He was a little bit bitter at that point. He called the other doctors murderers and ignoramuses. Not a great way to endear yourself to your audience. I’d never, never suggest that. In his lifetime, he never saw his ideas take off. That's the danger.
I put that example in the book as a danger. Let's check the criteria that we had: was it valid? Did he have evidence? Yes. He obviously couldn't prove germ theory, but he did have 18 months of data showing that his hand-washing policy was successful. Is it actionable? Yes, absolutely. All people have to do is wash their hands. Is it valuable? Yes, if not only at his hospital but around the world.
This is a practice that was introduced to a lot of other hospitals that were teaching their doctors to use after autopsies. I don't have an estimate of how many lives could have been saved, but it could have saved the thousands of lives. Just women being saved from dying from this needless illness. Yet he wasn't able to communicate it effectively.
In the book, I talk about other examples of people from the same time period. Most notably Florence Nightingale, and then Dr. John Snow, as examples of people who used visualizations and data stories to convince people to actually take action and make a difference. It's not just because he didn't have PowerPoint and he didn't have Excel or Tableau or Power BI. It's because he didn't follow a data storytelling approach that worked well for these other people in the same time period, the same error.
It was fascinating. Hopefully, it resonates with readers and other people when they hear it, but it's a cautionary tale. It doesn't matter if we have a good insight, if we can't communicate it effectively, it will go nowhere.
Loris Marini: Yeah. He's a very simple example of a thing that you can do: just wash your hands better and save thousands of women. The data is there. Its rock-solid. Experiments have been done, but the community somehow does not want to hear. They don't seem to be receptive.
The difference between machines and humans is that we are a lot more nuanced and complicated. Machines, you can code it and you can optimize the information transfer because all that matters is information. Between two human beings, there's a lot more at play.
I want to dive deeper into what is it then that gets us to care. How can we open that door to attention, and at the same time, not abuse it? We want to maximize our chances of delivering the message. We don't want necessarily to play with the numbers and tell a story that is not rooted in evidence, right?
Before becoming familiar with the term storytelling, I thought that it was some sort of this, not a distortion of reality, but it was a practice that didn't have say, too many problems with enriching the crude facts with some flashy stuff that people can resonate with. That's not what we're talking about. I had to relearn that. One of my first insights was a couple of years ago was when I relearned the term storytelling.
Why do we need to tell a story? What could that doctor have done differently? How does the sole mechanism work?
Brent Dykes: Yeah. I guess for me, when I look at data storytelling, it's comprised of three things; most of the time it's going to involve, but should always involve data. Data is the foundation of every data story. You have the narrative component, and then you have the visuals.
In a Forbes article that I wrote on this, I use a Venn diagram. You have these bubbles representing each of these components. If we look at the intersection between data and narrative, really what's going on there is if I took a spreadsheet of data and I said, “Hey, Loris, I've got this great insight. Here's the spreadsheet. Have a look at it. Tell me what you think,” there's a good chance that you haven't spent as much time on it as I have. You may not have the full context. You may not know the meaning behind different metrics that are in that spreadsheet.
What do I need to do? I need to combine not just the data itself, but also the narrative with it to help explain to you what it means. For me, that’s the first connection there. Now, the reason why visuals are important is that again, if we go back at that spreadsheet, it's just a table of data. I'm expecting you to pull out anomalies, patterns, and trends in the data. It's going to be very hard for you to do that without a visualization to help you, especially with the more complex the numbers, the more likely you're going to need visualizations to help you interpret that data.
When we have a really good visualization to go with our numbers, that often is very enlightening to the audience. It helps you to see things and the numbers that you would miss without the help of that visualization.
The last is a combination of those two bubbles between the narrative and the visuals. A lot of the reason we're up late at night, watching TV shows is that as human beings in our DNA, we are storytelling creatures. We love that combination, especially visual stories where we can see things and explore that. It really engages us at a very deep level.
The power of data storytelling is if we have the right data, that's going to resonate with the audience. We combine that with the right narrative structure and how we organize that information. The visuals are the scenes of the data story, and it helps bring that data to life and really connect with our audience. Then we have something powerful. We have something that can change the way people view the world, their behaviors, their attitudes, and their actions can be affected by a really well-designed data story.
There are many benefits to storytelling. In the book, I share a couple of examples of how stories beat statistics in terms of they're more memorable and they're more persuasive. That's a key selling feature. Who wouldn't want their insights to be more memorable and more persuasive? Not persuasive because we're trying to say, “Oh, we're right and everybody else is wrong.” No, we're being persuasive because we want to take action on the data.
Having worked in analytics for such a long time, I know that we get caught up in all of the work that goes into analytics. After all the data collection, the data preparation, the data visualization to reports and dashboards. If after all of that effort, we're not analyzing the data and taking those insights and acting on them, this whole ecosystem of data really falls apart. At the end of the day, it's only as valuable as the insights and the action that we can take on that data.
Loris Marini: It's crazy. If you think about how much work goes into data management, it's just insane. I saw recently one of the many posts that you put out on LinkedIn; I think it was connected to a Forbes article you wrote recently. There was a horizontal line that started from data ingestion and ended up with action and it was a nice curve that showed the ROI, how much you need to spend in terms of energy and time before you get something out of it.
It's crazy that from just into analysis, you're still spending money. From analysis slightly you start going up, but really the magic happens when someone acts on it. The whole investment in data X, whatever X is that you're doing, is pointless if we don't get someone to act on it. We can't screw that up. It's crucial.
Brent Dykes: Crucial. Yeah. I talk about it as being the last mile, comparing analytics to a marathon – that last mile is crucial. If we cannot finish the race and get across the finish line by taking action and getting value from all this work, then who would be crazy enough to run a marathon and quit in the last mile? That sounds horrible. You don't get the medal and you do all the training and all the suffering to get so close and then end up with nothing.
Unfortunately, that happens at a lot of companies. As I’ve mentioned in the Forbes article, it's not that they're just doing one race and that's it. No, they'll start a new race. They'll start the first stages of that race again with the stuff that they're comfortable with, but never get the finisher medal for completing an actual race. That's a real problem when we're not driving value from these investments.
Loris Marini: Yeah. Obviously, I'm sure that storytelling is not a cure for all of the dysfunctions that can happen along the chain of communicating insights. There are sometimes, and maybe I'm using this word a bit hand waving it, politics. We refer to this mega umbrella term that we sometimes use to indicate everything that is complicated. Unspoken relationships within people, incentives that are maybe not structured in the right way. Even if it would be obvious that taking action A would be beneficial for everyone, some people will oppose that.
For one second, I'm going to take that because I think we could unpack then spend two hours on just what is politics to fix those relationships. Let's take that for one second to the side of the conversation and imagine we work in an organization where the incentives and the culture are such that people on average act. They want to see stuff happen. They don't want to stick their head in the sand and wait for the stone to be turned over. They actually want to make things happen.
Even in that case, we're just surrounded by so many priorities that it can be hard to spend our attention in the most effective way. It's on us, really, the analysts, the people that analyze the data and find the insights. We need to own that aspect. It's our responsibility to tell the story and make sure that the people that receive it actually get it and not just receive the information.
When you told the story of that doctor, something strange happened. I think I was daydreaming and I sort of lost my sense of time. I have a clock here that tells me how long we've been recording, but I sort of felt I lost sense of time for those three or four minutes. This is to me, hard evidence that stories work, right? They can really get my attention. Why do they work? What's the psychology of storytelling?
Brent Dykes: Yeah. As I was preparing the book and looking at this, I started to see what research is out there from a psychological or from a neuroscience perspective. I won't harp on system one, system two that you already talked about, but for those who may not have heard that other session, our brains basically process information through two systems. This comes from Daniel Goleman’s work, Nobel prize-winning behavioral economist. He published a book called Thinking Fast and Slow, a very famous book. I'm sure many of your listeners have heard of it
Basically, system one is that intuitive side of our brain that processes information unconsciously and is constantly looking at patterns and heuristics. Once it's preprocessed that information, whenever there's any information that needs further processing or thinking or analytical processing, it goes to system two. That's the traditional brain that we think of when we're thinking of consciously processing information and thinking logically about it.
That system one is a big part of the reason why we like stories because at the end of the day, what are we trying to do as human beings? We're trying to make sense of the world around us. What do we do? We form things into narratives. We create narratives related to politics, our careers, and how we interpret the world as it is. Everything fits into these narratives that we create.
A lot of the time we're looking at the world, we're processing information through narratives. The interesting thing was neuroscientists looked at this and they said we have a fight or flight mechanism built into us. If we were hiking along a trail and we ran into a dangerous predator: a lion, a tiger, or a bear, we would have this reaction within our brains of, “Oh my gosh. We're in danger.” When neuroscientists looked at the brain and how people process information, if you're presented with a fact that maybe conflicts with your viewpoint, the brain's response to that fact is the same that it has to these predators.
Actually, we are designed to protect our viewpoints and we're very defensive that way. We go on the defensive with facts and data but the interesting thing is that when psychologists look at how people process stories, they noted that our guard goes down. We're more open to new ideas. We want to hear where the story goes. We don't nitpick on the details as we do with the data. Even the psychologists found that we can enter into a narrative. They call it narrative transportation where we're almost like a hypnotic state. I don't know if I achieved that with you with that story, but we enter into that narrative transportation.
Loris Marini: I was imagining those hospitals and these women and the struggle. Yeah. I went with the flow. I totally forgot I was even here in my study, you know?
Brent Dykes: That happens and that opens us up to new ideas and it's very powerful. In the book, I talk about how we have logic and data; our goal is to then pair it with a story to reach the emotional part of decision-making.
When I started in analytics, I came out of MBA school and I felt like if I could just provide data and the right insights to the decision-makers, they could make the right decision and that's all they needed. I completely dismissed the role of emotion in decision-making. When neuroscientists look at the role of emotion, it actually plays a bigger role than facts or data do.
There's a neuroscientist by the name of Antonio Damasio. He's from the University of Southern California in the neuroscience area. He worked with patients that had damage to the emotional centers of their brains. They basically were emotionless. They would look like you or I but they didn't have emotion. There was an interesting thing he found when he would try and schedule a lunch appointment with these patients. He started to observe that they really struggled with making a decision on where to eat. A decision that you or I, if I came to Sydney, we could make in a minute or two. Whereas these individuals, would vacillate back and forth between the different options. They would say, “Well, if we go to the sushi place, they have the special I like on Tuesday but then again, the Italian place is easier for parking. The servers are friendlier,” and they go back and forth between the different options.
Twenty, 30 minutes would go by and they're still trying to make a decision on where to eat. Antonio Damasio realized that emotion does play a key role in decision-making. That's why, again, storytelling is important because it gives us a bridge to connect with that emotional side of the decision-making to help form a narrative and help people make better decisions. It really is a critical part of how we should be communicating insights.
Loris Marini: It's funny, it's almost a feature reduction processing for the folks that are used to analyzing data. Sometimes you have these large tables filled with columns, 20, 30, and 100 columns. One of the first things you do is understand which columns matter more so you go through this reduction phase and you eliminate the noise. It's a very mathematical, solid process, but here it almost feels like our emotions are acting as a sort of filter. They create these patterns and shortcuts to help us focus on the things that matter more to us, as opposed to getting lost in the infinite details.
Brent Dykes: Right.
Loris Marini: I just had never thought that insight was like a lion. I think this image is going to stay with me, Brent. You have an insight, something that's going to challenge your belief. Think about that like a lion.
Brent Dykes: Yeah.
Loris Marini: It's so useful. How do we let these people take that guard down? How do we gently introduce it? What can we do in practice when we are on the job? We're stressed and there are so many priorities, but we know we have this nugget that we validated. We run tests. The math is solid. We only need to get the right people to act on it. Do we need to change our mindset? Do we need to make a shift? Is there a framework we can follow?
Brent Dykes: Yeah. One of the things that a lot of people like to do is the data dump. Here's all the information. Here's everything that I looked at. What we don't realize is we're looking through that through our lens. That’s the curse of knowledge, right? We look at that information. We've been blessed with insight and we see it so clearly in the information that we're sharing, but we don't step away and look at it from their perspective. They haven't spent hours or days or weeks in the data as we have. They may not have a full context of everything that we've looked at and the full meaning behind the numbers.
I always say we need to shift from that exploratory approach to an explanatory one. When I get asked, what is the biggest challenge that you see with data storytelling? It really comes back to: people don't make that shift. They don't navigate that chasm between exploratory looking at the numbers, analyzing it, exploring the data, and then figuring out, “I need to change how I communicate that information to others because they haven't spent all the time. They don't have all the contexts as we have. How can I bring them up to speed and help them understand a problem in way less time than it took me or you to analyze the problem and come up with?”
It's really about picking and choosing what goes into this story. How can I make it into following a structure, so that I can structure my ideas, and structure the findings in a way that's going to connect with people and help them to understand it? Bring out the key insights using visuals and charts that are going to help people to really see what I see.
That's one of the challenges. When people look at the charts and the data, after they've spotted an insight, they can look past the noise in the charts. They can look past things in the charts that other people can't. They won't see past that noise. They will see the noise and it would be hard for them to see the signal. Really our job as data storytellers is to go through and really bring out the signal, the strongest possible one, and help the audience to easily and clearly see what's going on in the numbers and then position them to make a decision.
Loris Marini: Yeah. I suppose it requires a lot of knowing who you're talking to at different levels. A startup is typically a horizontal organization. Everybody can talk to anyone. Below 50 people typically is the unit. You get to have a coffee with the CTO and CEO. If you've crossed that 200 lines, it starts getting a little bit complicated as you get to have one level of management.
If we scale that up all the way to an enterprise with 1,000 or more employees, across many countries, and many regions, then communicating an insight becomes very difficult. As an analyst now, you don't just need to know the facts and how to analyze it, you need to have access to good data, which is another huge chapter. Assuming that you have good data, you also need to communicate it to your line manager, but also help or liaise that communication process all the way to the decision-maker. Who knows where they are in the hierarchy?
I'm wondering, is there a clear winner when it comes to culture and organizational structure to ensure that we become organizations, collections of people trying to achieve the same goal? We become more flexible, more malleable, more resilient, and more easily able to adapt and change what we're doing? It's just a matter of, again telling a story that cuts through all of those layers of abstractions so that we build enterprises and get to the very top?
Brent Dykes: Yeah. Data stories are going to work in any culture because at the end of the day, we're all human beings. I still go back to the human beings are not data storytelling creatures. We consume and tell stories all the time. Now in certain cultures, I think data storytelling will flourish more than in others.
One of those cultures that I think really works well with data storytelling is a test and learn the culture. People understand that we're constantly learning from the data. We run tests all the time to see what the results are. There are probably many stories that come out of that testing that can then be shared throughout the organization.
You're going to have a culture in which people want to hear those stories. They want to hear what's new, what's happening. Did we learn anything new about our customers? Did we learn anything new about our business processes? Did we learn anything new about our competitors? Those are the things in that culture. I think you're going to see an appetite for data stories.
I also think many organizations aren't there yet. They don't have that culture there. Again, I think that’s one of the powers of data storytelling and having data stories shared and having a forum. That's the minimum you have to do. You have to set up a forum, you have to set up a way in which data stories can be shared, but then what does data storytelling do? It does a couple of other things. It raises the data literacy of everybody.
if you think too with your children, when they were little and we read them bedtime stories, the goal was to get them interested in reading themselves and furthering their literacy level. I think the same thing applies to data storytelling that as we tell stories within the organization, we're actually elevating everybody's data literacy. They're learning data stories, right? They're hearing data in a context, meaning and they’re experiencing that.
Once we have that data literacy hand-in-hand with that also goes curiosity. As we're sharing data stories within an organization, people start to get curious. It's like, “Oh, that was an interesting story about that customer segment. I wonder what stories there are about our customer segment that we focus on?” It gets people curious within the business about data and what's happening.
Here's an example: I had a client that I work with at a very large organization and my contact at that organization said, “Brent, we don't really have a data culture here at our company. We've got lots of data, but we're not a data culture.” He made an interesting observation. He said, “What we do have is an anecdotal culture. We do rely on a lot of stories within our culture that are based on our founders and our customers and how we've grown as a business.”
That really is a powerful part of who we are. This is him speaking, not me. He said, “I think that instilling a data storytelling culture will get us to be more like a data culture,” if that makes sense. A bridge to becoming a data culture for them, he felt, would be to establish a data storytelling culture, because they already had the storytelling actively being done and what they were missing was the data side. He was like, “I see this as a step towards getting to a more data-driven culture by getting stories told and sharing them within the business.”
Loris Marini: Perhaps even going through the process of grounding those anecdotes in evidence. Sometimes, Mary said something in marketing and you're like, “Okay, is it true or not?” There's always going to be some sort of process where you face the evidence and that evidence might agree with what you already believe or not. It could become an insight in itself, the process of validating the story to be a better, solid story. Otherwise, people would run away and they're like, “Nah, I don't want to hear that.”
Let's jump back to the scenario we talked about before, where imagine I have this really important insight. I want to share it and I need to talk to an executive-level leader; they are extremely busy. Their attention span is probably shorter than anyone else. I’d be tempted to create a short summary, like two or three lines that start from the problem in one line and then provide the solution as a second line. Would that work? Yes? No? What can we do to hook them and get them to care?
Brent Dykes: Yeah. I think one of the reasons why a lot of executives expect to have executive summaries is because I think we've done a poor job in communicating insights in the past. We've done the data dump approach. Executives are like, “Are you kidding me? I have to find the insight and everything? No, give me the executive summary.” Now, we have this executive summary approach.
Now, if we think of a data story or of just a story, and I use this example in the book, what are we doing with an executive summary? We're jumping, we're seizing, we're just taking the climax or the insight of the story and giving that to somebody and saying, “Here's what you need. Do with it what you please.”
Think of a movie, Empire Strikes Back. Spoiler alert, I'm going to spoil the movie for you. I figure if you haven't seen it by now, you probably don't care. Anyways, what is the major climax of that story? It's when Luke Skywalker learns that Darth Vader is his father. That's it. I've just taken that whole movie and summarized it down to the climax moment and given you the executive summary. Now, does that have the same power with you like its story as the entire watching the movie? No, it doesn't. It's efficient, but it's not effective.
What I want to see is organizations using stories. Let me give an overview of my view of how a story should be structured and then I'll come back to a modification that I think can work in those situations where we have an executive who just wants the insight.
Let me tell you how a story should be told. This comes from me looking at how people think of storytelling. Most of us have heard, oh, stories have a beginning, middle, and end. That comes to us from Aristotle more than 2,300 years ago when he looked at Greek tragedies. He observed that they had this three-act structure where there's some event where a knot is tied. There's a problem, there's a conflict or something, a knot is tied. The back end of the story is untying that knot and the resolution of everything.
For me, I started with that definition. I was like, “Ah, it just doesn't work for me because a report has a beginning, middle, and end.” I don't think of many reports as stories. I kept looking and then I came across a guy by the name of Gustav Freytag. He was a German playwright who studied Shakespearian plays. He looked at Greek tragedies as well, and he identified what he called a story arc or the Freytag pyramid, and very similar to Aristotle's, but just a little bit more defined. I found that that was perfect for what I wanted to do from a business perspective with story data storytelling.
I call it the data storytelling arc, and it starts with a traditional story. You have the exposition, which is basically establishing the characters, the setting, the scene, and introducing the beginning of the story. We're doing the same thing in a data story. We're establishing the setting and a hook. In a data story, what we'll do is we'll establish the status quo. What typical patterns do we see in this data? What data are we looking at? What timeframe? We're basically introducing what we expect to see.
We have our hook. The hook is the inciting incident of a story where something major happens to the character. In this case, our hook could be that a metric increased significantly or decreased significantly, something memorable. A key observation happened in the data that was interesting or unusual. That gets the audience curious, it's our hook into the rest of the story. “Hey, we saw revenue increase 43% last quarter. We don't know why. Oh, okay. Well, let's dive into that.”
What we start to do is we start to peel the layers of the onion. We start to dig into what may have increased that revenue that quarter. What were the contributing factors? We start to look at it from different product categories and we see, “Oh, well, we had a massive increase in this one product category,” and we dig, dig, dig, dig, and then we build up to our “aha!” moment. Our “aha!” moment is our insight, our major insight that we talked about at the beginning. This is the big insight, “Oh my gosh, we've completely missed this target market. It's worth millions of dollars and we didn't know about it.”
Unexpectedly, we shifted our understanding. We didn't think there was any money in this market, but there is. Now, what do we do about that? We've gone from the hook. We built up to the “aha!” moment and we're not done with our data story there. Again, going back to my emphasis on taking action, we want to make sure that we do something with that insight. That may mean that we need to flesh it out.
We may need to perform other analyses on the options. Okay, well, there are three options. We've analyzed the cons and pros of each. This is a $2.4 million opportunity. This is a $1.1 million opportunity. This is a $900,000 opportunity, whatever it is. We flesh it out and we help the decision-makers to come to a conclusion and establish the next steps. That's the arc that I take people on.
Now, going back to the original question about what we do with somebody who just wants the insights. What I do is I take from that data storytelling arc; I take the hook and I take the “aha!” moment and I put those into what I call a data trailer. Think of the world's worst movie trailer, where you're giving away the climax of the movie. That's what we're doing with the data trailer. There's this big opportunity and we saw that there was a big increase in revenue. At this point, we've got our hook. That's what we share with the executive who just wants the insights. Our goal is not to stop there. Our goal is not to just give that. Our goal is to pique their curiosity and their interest in hearing the rest of the story.
That's my workaround for that situation where you have many executives who are very limited on time and probably have been constantly bombarded with data. If we can share a data trailer with them and it piques their interest and then get their permission. Now they're saying, “Okay, this is worth another 10 minutes, 20 minutes of my time to hear the rest of the story.” We can tell them the rest of the story as we originally envisioned. That's what I wanted to share to answer your question.
Loris Marini: That tension, you mentioned in the book, acts as an amplifier to attention and curiosity the thing that you just mentioned before. We thought there wasn’t a market opportunity there but now we start thinking that. The reason is an example of establishing a bit of tension and piquing that curiosity. How would you actually do it?
Brent Dykes: Yeah. The tension and the curiosity grow, it starts right from that baseline where we say, “Here's how the status quo is.” This is all stuff that they know, this is stuff they're comfortable with, but then we share that hook. We say, “But something happened on this day. This metric went down. This metric went up.” That's the first piece of tension. We're like, “Oh my gosh, what's going on? Why did that go up? Why did that go down? I don't understand.”
We start to unpack it. We start to say, “Well, the first thing we looked at was the countries. We did an analysis of the country. We found this region right here had a higher concentration of blah, blah, blah.” We start to unpack it and we go there. They're following us along on this journey, we're holding their hand.
The cool thing about data storytelling is obviously our goal is to drive action. Even if we don't drive action, they're still learning something about the business. Our audience is learning about the business. That could be insights into the customer, our products, our markets, and our competitors. They're learning even if they apply it and take an action on whatever insight that we're sharing. They're still growing and learning. Through this tension, we build-up to the “aha!” moment. That's the climax. It's a journey that we're taking them on.
I like to compare data storytelling to being a tour guide. One of the experiences that I had, many years ago, before COVID was when you could actually travel places. My wife had always wanted to go to Italy. We went to the Amalfi coast and, yeah, beautiful. We had arranged to see Pompei. When we got to Pompei, we had a tour guide and he was an archeology student at the local Naples university. We only had a very limited window of time to go through that site. I don't know if you've ever been to Pompei, but you could spend a day or more there to see everything, but we didn't have a day. We had like two and a half hours, three hours and that's it.
He basically tailored our experience to what we could see in the time that we had. He learned about us, what we wanted to see, and then took us to the place. Now, I see that the same way with the data storyteller. What we're doing is: we know all the data, we know everything in and out, and as we understand who the audience is, we then guide them through that maze of data. We pick out and choose what we share with them, doing so in a way that's going to be of maximum benefit to that audience. That's what I like to think of as data storytelling. You're the tour guide through your analysis and helping them to come away with a tangible insight and action that they can take after you've taken them on that tour.
One of the challenges that you can run into with a data scientist or with an analyst is what I call the endless journey where it's like, "Oh, first I looked at this, and then I looked at this, and then I did some regression analysis here and that didn't turn up anything.” No, that's not what I'm talking about. This is geared towards the audience. What do they want to see and how can I take them through meaningful data that's going to be beneficial to them, not recount the steps that I took in the analysis? That's not storytelling in my mind.
Loris Marini: Apart from the fact that you just did it again with Pompei because I was there imagining the ruins. This is really working. Almost to me, if there's one thing that I really bring home out of this conversation is that allowing people to get curious and broaden their horizons around what they do, that our analysts, our data scientists become more familiar with what people care about around them, their line managers, their peers, their people that are above them. How can we talk to them in a meaningful way?
It really feels like unless we have an organization that helps us do that, we will always be stuck in our cubicles doing our modeling and learning about the new, deep learning and adversarial network stuff that is hot at the moment. Maybe even learn how to deploy them and put them into production and become really more software developer person like an engineer focused on solving the problem, which is good. We need to add that but if we then don't communicate it, it just dies there.
High salaries, and high hourly rates go completely into the bin with the added frustration of the people that work on them. I know many data scientists that are really brilliant. They know how to talk to numbers, but for some reason, they either lack the time to care about how to talk to people. They're just stuck in organizations that are so hierarchical and cubicle-ish that they just can't even if they wanted to. They would have to invest their time on Saturdays and Sundays to reach out to those folks because there isn’t an opportunity to network and have those casual conversations and have those “aha!” moments.
Brent Dykes: Yeah.
Loris Marini: How do we build better systems structuring our organization so that those stories can emerge, and bubble up?
Brent Dykes: Yeah. I think part of the challenge is finding meaningful stories. We can find all kinds of insights in the data. We've got more data than we need and it's really being very targeted. One of the things I talk about in the data chapter of my book, is something I call the 4D framework, and it's an audience 4D framework and it's useful for many reasons.
I compare data to a labyrinth where you could go into the data and get lost. Maybe not, you could emerge from that labyrinth with nothing to show for your efforts. You could lose track of time and then you're just worried about getting out and surviving the labyrinth.
One of the things I talk about in the book is based on all my consulting experience, working with different clients over the years. I came down with these four dimensions. If you can understand these four dimensions, it's going to help you to find insights that are meaningful to your audience.
The first thing is understanding what the audience's main problems are right now. What are they trying to solve? Let's pretend that we're working with a marketing department and they're struggling with generating leads for their sales team. That's the problem. They're not generating enough leads to help the sales team close business. That's the problem.
The next thing is, what's the outcome you're trying to drive towards? If we think of your present state as struggling with generating leads, what does the future state look like that you want to achieve? You want to get there and that could be, well, we want to double the number of leads that we're generating on a quarterly basis. Okay. Now I know a little bit more, I know that you have a problem with generating leads. I know that you're highly focused on doubling the number of leads that you're generating for the sales team.
The next thing is, what are you currently doing that's tied to this outcome? How are you trying to shift from the present to the future? That's really focused on the actions. What actions or activities are you engaged in today, or will be engaged in to close that gap? They might say, “Well, from a marketing perspective, we're trying to do more digital, online events to try and generate more leads that way. We're looking at generating some new content that's going to be more compelling to generate leads. We're looking at shifting our marketing agency because we're not happy with the ad agency that we have, and we're doing a review.”
Now as an analyst, okay, I understand where you are, where you're trying to get to, and understand where you're focused. You're focused on these areas. You're looking at digital marketing, you're looking at your content and you're looking at your ad agency. If I'm investing my analysis time, I'm going to be focused on those areas and digging into them.
The fourth dimension that I focused on is measures or metrics. Why are measures important? They help us to understand the problem. They help us to understand what they're usually associated with the target. In this case, it might be leads being the main metric, but there might be also the cost of acquisition. There may be other things involved that are also used to analyze these activities. Which ones are driving more leads or had the potential to drive more leads?
I think if you can understand those four dimensions for any audience that you're targeting, and working with, that's going to help you to focus your time. You're not going to get sidetracked by worthless data or data that are not important to your audience. It's going to help you to identify insights that can help you have a real impact on the business.
Data storytelling is not just about the visuals or just the narrative. It's also about really targeting the data that's going to be impactful to your audience, impactful to the business. That's equally important as those two other aspects.
Loris Marini: Yeah. Ultimately make sure that your message gets heard, or at least you get an interest from your audience. If you speak their language, if you talk about their KPI, if you frame it through the problem, you're already halfway there. Maybe not half, but it's a good start.
Brent Dykes: Yeah.
Loris Marini: Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals by Brent Dykes. Get the book. If you haven't done it, I'll have links everywhere in the description, the show notes. It's always there. Let's increase data literacy together.
Brent, what's the best way to follow you?
Brent Dykes: Yeah, I'm on LinkedIn. I do a lot of posts there, so definitely connect with me on LinkedIn. You can connect with me on effectivedatastorytelling.com. That's where I have a lot of content related to my book. If you want to reach me, email me at email@example.com. I'd love to work with companies in terms of workshops or training, other forms of training and coaching, and speaking engagements. All those things are things I love to do.
Loris Marini: Amazing. Get the full Brent experience at effectivedatastorytelling.com. Brent, thank you so much for being with me. This has been really insightful and I really enjoyed the first half of the book. I'm really looking forward to the coming weekend so I can dive into the second half and increase my own data literacy when it comes to visuals. Thank you very much for sharing your knowledge.
Brent Dykes: Thank you, Loris.