“I’m starting it with a quote from the rhyme of the ancient mariner, which is: “Water, water everywhere, but not a drop to drink.” So it’s a guy who’s on a boat, and he’s in the ocean. He wants to have a drink of some water. But the problem is, all the water is all salty, right? Not good.

This is how I think about data. Data is a crazy stat, right? Every two years there’s more new data generated than all of human history before it. Data’s all around us. It’s everywhere. But how much of it can we actually use? How much of it can we actually absorb? There comes a point when there’s so much of it that it’s overwhelming that it’s creating problems for us. And this is actually a scientific concept called ‘information overload’.

And when there becomes too much data to be processed by your brain, eventually your decision making ability actually goes down. You’re trying to understand too much, and as a result of it, your decision quality drops.

Storytelling is the oldest form of human communication. It goes back tens of thousands of years. All this storytelling adds up and it makes really important differences in our lives. And it’s about how someone interprets that story.

So, Visual Capitalist is, it’s sort of our hallmark, is being able to take the world’s big trends and tell their stories through data, through what we call data storytelling.

So we’re the global leaders in data storytelling. We’ve created over 3000 different data visualizations. Last month we had 11 million visitors on our website. We have about 340,000 email subscribers and about a million social media followers. We have Elon Musk tweeting out our battery metals infographics. So we have some really good people following us. So these stories are resonating with the right people.

I’m going to start with something very contextual here. I’m going to talk about the data landscape broadly, what that means for how we interpret information and what that means in terms of trying to tell a story.

This is kind of our being. This is a quote from E.O. Wilson: ” We are drowning in information, while starving for wisdom.” There’s so much data, but what we really need is we want that nugget, that insight. We want to understand the world, and we don’t want to have all this conflicting data that is telling us different stories.

We want to have something that we understand, and that is true, and that is the wisdom. And then the other important implication of this, and you all fall into this category, which is: “The world will be run by synthesizers. People that are able to put together the right information at the right time, think critically about it and make important choices wisely”.

When I think about storytelling, I’m thinking about how do we take the most interesting and compelling data, and facts, and story, and how do I take it and make it stand out amongst all of this other information. How do I make it have that insight that connects with people?

And here’s a mental model for that. The upper leftmost panel is data is just a bunch of random dots. Information. You’re categorizing it, different colors. Knowledge is, you’re starting to connect these things together. Insight is you’re saying: “Hey, actually this thing leads to this thing.” And that pathway is you start to understand something. Wisdom is having gone on that path and knowing where it leads and having been there before and knowing that that insight is true based on your own actual experience. And then you get to the really hard part and the most interesting part, and a lot of what I’m going to be focusing on, which is the impact. How do you take this and convey it and make an impact. When we’re creating stories, we’re all starting from this standpoint of having this data, which is this upper leftmost panel, but that’s not good enough, right? There’s so much data out there. It’s telling us all kinds of different things. What you need to do is you need to create the story that makes the impact so that people remember what you’re talking about.

How do we do it? We do it through data visualization or through data storytelling. So we take the data that we find to be the most compelling and insightful, and we convert it into a visual format, either infographics, or data visualizations, or charts or interactive pieces, or videos. And we try and show that data in a way that’s memorable and a way that you can look at it and absorb that information fast, intuitively, and get something out of it, right? 65% of people are visual learners, so doing things this way you’re hitting the majority of people this way.

So data storytelling is simply the fusion of three different things. It’s narrative. So that’s the story, the arc. There’s visuals so that can take place in a number of ways. And then there’s of course the data itself, and all of these things work together to make the big impact here.

So the foundation, I think this is the hardest part, going from having data to having the sort of most compelling points, I think that’s the sort of the trickiest thing because once you have that, then it makes it a lot easier to build out the right narrative.

But just having data is not enough, you have to find the insights, and you have to go through that whole mental model of where can I make the impact with what I have.

The narrative. This is what we’ve been talking about already, which is storytelling. This has been around forever, and it’s worth noting that there are a bunch of typical conventions for storytelling. You have the Hero’s journey. If you ever watched Star Wars or any of these movies, they all follow this exact cycle. If you think about all these different conventions, there’re different ways that are tried and true, proven ways of telling stories and communicating a point to someone.

And these are not limited to just art and things like that. You can think of these in the context of investing in markets, how you tell your story to someone else. Can you apply these frameworks in different ways? We do it all the time.

Then the last part is simply visuals. There’s many different forms of visuals that you can use to convey your point for complex data or for simple data. It’s going to be a line chart or something like that. But these things can be quite complex as well. But these are all different ways of fusing information together in a way to understand a story.

I am going to take a quick little detour and I’m going to talk about the evolution of media.

So the first wave of media, all of you’ll remember this, right? This is pre-social media. In order to get eyeballs, you had to advertise literally on a radio show, or a cable news network, or newspaper, or whatever. It’s a one-way form of communication. There’s many fewer channels, much bigger barriers to entry And times were much simpler back then, right? You tried to tell your story and you paid for the reach, and then you got it out there and people looked at your message and decided if it was worthy or not. But from some perspectives it was kind of nice, right?

Then social media came. And when you look at social media, there are some big pros to it. I mean, we have two-way communication. We can send out messages to people, but they can also respond back. Brands, companies can interact with people, as all of the companies that are seeking investments have found out. Right now you’re interacting one-on-one with investors in all these really unique ways that never happened before.

Some of the problems that occur because of this are really interesting as well. You have bias and filter bubbles that have showed up. When you start to search on Google or when you start to look at your social media feed, everything is reinforcing your confirmation bias. It’s all telling you the same thing that you believe and you’re not seeing varying points of view. This is what has fueled outrage culture, fake news was obviously an issue that cropped up a few years ago that a lot of people were talking about.

And then algorithms in general are kind of like a challenge, right? Social media and Google and all of these different tools are great, but they’re using their algorithms, and you have to build your stuff around the algorithms to try and compete with them, right? We’re into constant fight to create the most compelling content to get eyeballs. You’re not necessarily seeing the best things, you’re seeing the things that might be getting the most engagement. So, that’s important to understand in this second wave of media.

I’m advocating that there’s beginning to be a third wave of media though, and this is kind of the stuff that we’re about. I think people are tired of the algorithms, they’re tired of all of the opinion and outrage. They’re tired of the bias and all this kind of stuff. And what I see is people are migrating more to a data-driven type of system, of sharing information.

And this really took off during COVID. Because of COVID, and flattening the curve, and of showing how the pandemic was going, you had to use these exponential logarithmic charts and all of a sudden, these data visualizations became commonplace in our life. So I guess that’s one good thing to come out of it.

This is really how things are moving. People are communicating in data-driven ways. They want to show that their information is transparent. Web3 and all that stuff will come in here as well. You’ll be able to validate data sources instantaneously. It’s replicable so you can take that data and move it somewhere else. And it’s going to eventually be more decentralized.

And this is important because it’s going to change how information flows, and how people get information, and how it gets communicated.

So I think a lot about the Utility Equation. Basically it’s the amount of meaning that people get divided by the amount of time that it takes them to consume that. In the modern world, if you’re going to make something really long, it better have a lot of value, right? Otherwise people are going to go for the shorter 15-second TikTok clip or 140-character tweet that is funny. It’s short and it has some value, but it’s not crazy.

One another thing is that we all get different things out of data. So, everyone has unique experience, right? And so everybody is approaching data from having a different level of context going into that situation.

The same thing goes for experience. People have different personal and professional experiences and so that dictates how you think about data, that frames your way of thinking about seeing information for the first time.

People have different level of data literacy. It’s kind of funny because sometimes people are just not going to get something cuz they’re not data literate. And so how you present data is really important because some people just aren’t going to get it. And so depending on the type of crowd that you’re presenting it to, you have to know your audience obviously. What is the level of data literacy they have and what kind of charts can you presenting to them?

And then of course, cognitive biases also frame this stuff as well. My favorite cognitive bias, by the way, is cursive knowledge. We often forget that other people don’t have the same level of knowledge as us in certain areas. Some people might not have the same context that you have.

Data is not intuitive by default. Data is messy, and it’s complicated. And when you pass it from person to person, it means different things. And that’s why it’s so important to really hone down what the message of data is. Everybody has different experiences, different ways of interpreting data. And so the reaction that you can get from people can be very different, people can think of something in a very different ways, and to be honest, this is the case even with very refined data.

This is, I think, the most important thing in this section. This is Seth Godin, who is a marketing genius. He thinks about modern currency in terms of how people allocate their time and energy to things. He thinks of it in sort of two ways. He thinks there’s attention and there’s trust.

The two things are often sacrificed for each other. Because someone wants one or the other thing, right? So, if you want attention, you can get that by sacrificing trust. You can say something really crazy, and people will be like, “Okay, you got my attention.” But then if that thing that you say turns out to be not trustworthy, then all of a sudden you’ve lost all that trust, right? It’s the Warren Buffet quote: “You can build up trust your whole life, but in five seconds you can get rid of all of it”, right? So how do you balance these two things. It seems like a lot of the time you’re doing one at the expense of the other. So that’s really the big question in communicating anything, how do you balance getting people’s attention and keeping it with maintaining trust?

So now I’m going to go through 12 different examples of ways that you can stand out with data storytelling.

So comparing the Titanic with a modern cruise ship, I’m sure this is all a question that you’ve had at some point in your life. So the Titanic is so big, but how big is it actually? Well, here’s the Royal Caribbean Symphony of the Seas, which is the world’s biggest cruise ship. Here’s the Titanic and here’s the iceberg that the Titanic hit, and here’s the size of a Airbus plane and a school bus, and so on. So you have the exact context going all the way from the smallest thing to the biggest thing. And so I call this one Context is King. If you are putting together a data story, whether it’s about an investment or anything, the way that you create the context around it really frames the story.

Connect the dots for people. So this chart just literally shows countries by their largest trading partner. You can actually see Germany connected to all these different trade partners. You can see China, which is now the world’s largest trade partner, connected to many of these other countries in Asia, Africa, and so on. It’s a really simple concept, but by visualizing it and connecting the dots between these things, you can actually see really what’s going on in a way that it’s, if you were to look at the raw data, it’d be really much harder to grasp.

Do the unexpected. So this was a visualization we did at the start of COVID. It’s our most famous and well-known visualization. But basically what we did is we took the history of pandemics and we made each pandemic the size based on how many deaths occurred. We made it in the shape of a coronavirus, but not quite. And we put them on this weird timeline thing that goes from modern times all the way back to ancient times. This is not a typical data visualization technique. We made this up, we thought it was interesting, and we put it together, and it blew up. So when you do something unexpected, it captures people’s attention.

Answer the unanswered question. This is simply a donut chart that shows what the Earth’s surface is made of. I didn’t want to know that, but here it is. And you look at it and you’re like, “Oh, crap!”. You see Russia, and Canada, and China, and the United States, and of course you see the ocean, and we all know that ocean makes up the majority of the land mass. But when you look at it, you’re like, “Wow, that’s pretty wild!”. And it’s not something that you expect to knock people’s socks off, but you’re answering a question that people have that they don’t know that they have, which of course is a bit of an art to figure out.

Keep it simple, stupid. This is a hilarious visualization that we do every year. At the beginning of every year we take about 300 different reports from leading publications. We take all of their predictions for the next year, and then we aggregate them all, and then we summarize them with this stupid bingo card. And here’s a prediction for the next year. And then the number of dots that it has is the number of times that was predicted by all these different sources. But when you look at it, you can see a lot in a really small, condensed base.

Be impartial to buy credibility. I think that’s a big problem with the sort of modern information and media environment. I think people are too biased, and too drawn to talking about their own point of view. What we’re trying to do is the exact opposite of that. We’re trying to use data as a way of creating an even playing field, so that when you look at it, you understand that we can be trusted because we’re not trying to infuse it with the point of view. We’re just trying to show you what’s going on. So what is this? This is how long it would take to read different terms of service agreements for all these different platforms. We basically looked at how many words we’re in each agreement, and then we visualized all them. We could have turned this into a piece of why Facebook is the worst or whatever. But instead what we did is we just showed all of these platforms directly against each other. We didn’t have any prejudice as to what was what. We just wanted to show you, this is how long each term of service agreement is, and you can come to your own conclusions, because you are smart.

Colors can tell the story. Two things to note on this. One is that this is a really cool color palette we use. It’s called the Viridis Color Palette. And this palette is actually built for everybody including color deficient people. We use color in this instance to show the median age of different continents. This is a year or two old, but it’s really striking, right? When you look at Africa’s median age and you realize that it’s 18, literally the middle of the age group is 18, right? So half of people are younger than that, which is wild. But the color really makes it stand out. You can look at it right away and you understand the message, right? And so this is a really important aspect of any type of communication. You can use color to your benefit.

Every time there’s a new type of media, there becomes some sort of platform that is the dominant form of that media, right? So for mobile photography was Instagram, for short form video, it was TikTok or Snapchat. For live streaming, it was Twitch. And what we want to do is we want to be that for data storytelling.

We want to take all of our stories, some of the things that we were showing you there. But also the stories of the thousands, or tens of thousands, or maybe hundreds of thousands of other creators that do work like what we do, taking data and visualizing it in really cool ways, and putting it all in one place to cover every single niche.

And so that’s what we’re building as a platform to connect everything together. And with that, that’s. That’s a wrap.