Love these examples, Evelina! Hans was the single person who made me shift my entire life toward dataviz!
Could you clarify the connection you’re drawing between “Once upon an time” which opens a traditional story, and these examples you give here? I mean, none of them use “once upon a time” or even explicitly show a character arc.
How do you define story? What would you say to someone who describes your examples simply as “effective data explanations”, not “stories”?
I feel like the term “story” gets overloaded so much, I’m curious when something goes from simply an explanation to a story. I’m sure you have a ton of great thoughts here :)
Hi David! Thanks so much for your comment. You're totally right, I could have explained the connection between stories and data stories better. I've actually just updated the post with some more in-depth material that I used in the talk last week :)
Now, on to your questions :) The data stories I showed as examples don't open with "once upon a time" but they do follow a narrative arc in its basic form: they go from setup, to insight, to conclusion. The characters are not really developed as you point out, but thanks to their sequencing, personnalisation and dynamic nature they still read as stories.
That being said, data stories are often much less elaborate than typical stories, but I think we can still call them stories, as long as they have an editorial angle and a structure. I do understand you questioning though, and I've written about it before (here, for example: https://plotting.substack.com/p/core-message)
One last note — I recently discovered the term "data trailer" (used by Brent Dykes), and I thought it was a nice description for the short and simple data stories!
Oooh, I like the term "data trailer" -- maybe there's another article there :)
Thanks for the detailed response & link, Evelina, and just for sharing your expertise online in general!
After reading both articles, I think I have clarity on maybe where I'm feeling confused (and maybe where an expert like you can help clarify!)
As someone working hard to learn how to tell visual & data-driven stories, I feel like there's confusion and frustration I've felt, not just in your articles, but online in general, when it comes to how we use the term "storytelling".
Educators / folks online seem to be obsessed with the idea of "storytelling": that it's powerful, that it's what changes people's minds, that it's what great communicators do.
Initially, this really resonates: I've experienced great stories, & they stick with me. From the film "The Half of It", to Natalie Wynn's "The Hunger", stories I've experienced follow vivid characters & change how I see the world. So, naturally, I'm enteiced when gurus promise to teach me how to recreate this experience with data, or charts, or social issues, or whatever else.
But what I find, in actuality, is that "storytelling" was really just a sexier way of saying "information conveyed in a compelling way", which, if you think about it, is really just good communication. Instead of actual storytelling theory & technique, what I'm given, in reality, is just a grab bag of tips & tricks about good communication, or good display of data, etc.
You yourself seem to take this approach of using story to mean a collection of techniques use to communicate clearly: "a story in the data design world includes a list of elements — charts, illustrations, text — that are sequenced in an engaging way".
Video journalist Johnny Harris says this, too: "storytelling is connecting information into a flow that lets viewers feel or understand something".
Mico Yuk also defines storytelling simply as what you do: "Data storytelling is what you say, what you write and what you draw."
Moritz Stefaner says story is anything that has a beginning, middle, and end.
And on and on...
In any of these cases, if you replaced "storytelling" with "good communication", the definitions not only still work, they're actually much more accurate!
Confusingly, though, people insist, not simply on using the term "storytelling", but on calling upon its elements in ways that (at least for a learner like me), seem completely disconnected from the "data story" they go on to claim uses it. For example in your article:
- we're shown a visual diagram of a narrative structure, but it's never again explained how it actually applies to the following 3 examples (where is the setup? where is the tension? when does the release happen? and where are the visuals showing this?)
- we're told "once upon a time" makes sleepy conference goers perk up, but we're not told *why* that phrase is inherent to so many story openings, or what role it plays in a narrative structure
- we're told we're being shown examples of "data stories", but what's actually called out is "simulation", "clearing up confusion", "reduced the cognitive load for his listeners". Ideas that aren't connected at all to what we experience or understand about traditional stories (or at least the connection isn't made clear in the article)
The result: we've diluted the rich & ancient art of "traditional stories", and also created confusion around good communication branded as "storytelling". And, as a learner, I'm confused how to generalize these ideas to my own work.
Ok, so that's been my struggle as a learner, which I thought my be helpful to you as an expert seeking to teach people like me! :)
Let me, on a more constructive note, say the sort of information I yearn to consume as a learner. Here's the article I've yet to find but am looking for:
1. Begin with a clear theory of what story is: eg "story a fractal made of arcs of human desire: birth, pursuit, & resolution of desire. At any scale -- a single story scene, to a full narrative arc -- a human desire is awakened, confronts obstacles with clear stakes, & comes to some close. Storytelling, then, is the art of constructing a world in which the audience can empathize with a clear desire, lean in as it faces clear obstacles, & find a resolution that leaves them with a deeper understanding of the world." (this is just an example definition I stole from Scott McCloud; Robert McKee, Shonda Rhimes, etc could all provide their more expert definitions)
2. Clearly present how you will re-appropriate parts of storytelling into explaining data: what's needed? what can be dialed up or down? what differs & why? eg "The art of 'data storytelling', in larger part, is simply the art of 'curiosity-driven explanation'. In this context, the desire awakened is curiosity about the central question your data will answer. The conflict may be a number of things: the inherent challenge of answering that question, the events which might lead to a bad resolution of the topic, etc. The resolution is the payoff from carefully facing the conflict with data, and finding something that changes our worldview. This pattern, like traditional story, is also factal: within your larger point, you create sub-points that follow the arc of curiosity, as well."
3. Then, that data storytelling framework is clearly (and visually!) applied through examples. In the case of your 3 examples, I'd say only Hans would count as a data story under this example definition of data storytelling, and even then it takes a good bit of teasing it out to see: "[Present a Visual showing a timeline of his talk, with different sections highlighted and annotated] Hans shows that even highly educated people had a wildly incorrect view of the world in this portion of his talk. Which sets up his central curiosity articulated here in the timeline: how has the world actually changed? And the conflict here: people negatively stereotype other nations. And the climax: Hans shows us the true progression with an amazing chart. But, true to the fractal nature of story, if you zoom into this region of the timeline where he presents the chart, you find yet another desire/curiosity-arc (story). Hans sets up a clear desire: to see humanity progress to a healthy state (which is represented delightfully by dots flocking to the opposite corner). The conflict is the actual events of history: AIDS, wars, resistance to reproductive rights. And the resolution is spectacular: a world much further progressed than any one of us thought."
This is a much harder article to write, principally because it requires coherent theory both of story and of "data story". But it's one that moves us away from simply passing out grab bags of communication tips, toward deeply understanding (and thus reproducing) great data storytelling.
And, for that, I think it'd be worth the time & effort.
____
As you can see, I'm really passionate about learning from folks like you how to tell great visual stories, and actually I've been collecting a list of advice from great visual storytellers, I'd love to get your thoughts on it!
Haha, you'll notice the first video is one I made analyzing Johnny Harris, and he commented on it saying it was accurate, which is why I included it :).
There's also a brilliant talk by Nicky Case (who I'm sure you're very familiar with!) which I feel like is pretty related to what you write about. Curious your thought on it!
Thank you for receiving these thoughts & questions so graciously, and thank you for the larger work you do!
Hi David, I agree with you that the term storytelling is used a lot in the world of data, and not always in a way that is appropriate. The times when we use it to say that "data visualisation = data storytelling" are the ones I've got issue with. The remaining cases are much less black-and-white.
As a fan of ancient rhetoric and Aristotle's work, I agree with you that the "real" storytelling is much deeper and much better than what we often call data stories. That being said, I would't go as far as to say that it's *not* storytelling at all. I think we can introduce little elements of storytelling in our data work to make communication flow better, be clearer and more human. Those can be a core message, some design elements that draw your eyes to key takeways, etc. We won't always have the space for character development, for instance, so it may be more implicit than explicit.
In each edition of my newsletter, my goal is more to plant seeds and encourage people to think about things (or even discuss and disagree like ;) ), then go in full depth and explain every single detail of every example I share. It's also a living and breathing collection of thoughts -- what I'm thinking and saying today will evolve and I may write something different next year. And that's OK.
To really go in full depth and learn how to build a narrative arc for a data story, one needs to attend a training with me. It seems like you've given a lot of thought to the topic, so perhaps you should write a book about it. :)
In short, I think it's OK to use the term storytelling for something that is not a masterpiece written by Shonda Rhimes. Perhaps there's a hierarchy to be created here: a split between *long-form data stories* that have most traditional story elements in them, and *lighter data stories* (these need a better name).
Thanks for pointing out that your posts are just little gems you share with the world along your journey, and that the full treasure is in your workshops & working with you. I believe it! :) Haha, sorry if I got too fixated on one post! But, you're right that the posts are definitely working: I've appreciated reading them, & they've definitely got me thinking! Thank you!
I really like the points you make in your last comment, they make me think about things in a few different ways:
1. Rather than being seen as binary "is this truly a Shonda Rhimes-level narrative?", data storytelling might be more helpfully seen as existing on a 2D spectrum:
- from short <--> long form. You suggested the language of "trailer <--> story", which I think is great!
- from pure quantitative description <--> full-on story (ie with characters, emotional arcs, etc)
It seems like practitioners like you focus on nudging people from "data speaks for itself" side of the spectrum toward "how do I guide my audience through the core ideas in the data, and create a human connection?" side. And do so by drawing comparisons to story, which people already value & recognize. Which is really effective, I suspect!
2. I originally protested the use of "storytelling" to mean "good communication/explanation", perhaps because, after spending a long time deep in the weeds of both data & story & explanation... I've become a bit of a snob, haha. I think: why call it a "data story" when it's more aptly called "a well-labeled, interactive chart with some examples that help you see how it relates to you"?
But I'm suspecting now that "storytelling", in the wild, means something much more basic than I initially assumed from my tower in Snob-land: maybe it simply describes guiding others through data, rather than assuming the data "speaks for itself."
Perhaps what is meant is not so much that one should specifically utilize actual mechanics of storytelling, but more that we should *act more like storytellers* when presenting data -- ie we should skillfully capture our audience's attention, & guide them carefully through the data.
When someone says, "look at this data story!", what they're often really saying is, "look at the skill with which the author guides us through the data & its implications!"
Defining data storytelling as "guiding someone through data with a level of care comparable to a great storyteller" makes a lot of sense to me! (Even if it doesn't quite roll off the tongue, haha)
I think the term "data storytelling" catches on because we're so familiar with great stories, & so unfamiliar with even the *concept* of clearly explained data. We notice an incoherent story instantly -- an incoherent data visualization is glossed over without note, or perhaps with the belief we lack the expertise to parse it. Often, we're so impressed when a complex, data-based concept is explained clearly, the best way we can describe the feeling is "storytelling", because that's the last place we felt such clarity.
This is why things like the WaPo COVID simulation get labeled as storytelling: not because they correspond in any way to actual stories, but because they bring a level of clarity & connection we typically only experience in stories.
3. Perhaps the other aspect I'm realizing rubs me the wrong way about "data storytelling" is that there's already an entire emergent practice dedicated to explaining complex ideas digitally, one that's perhaps more relevant than the catch-all art of "storytelling." (Also one that I've been laboring to try to grasp for years, haha, which is maybe why I'm so intent on this distinction! Please forgive me :)
Here are quick examples of how the forms are clearly distinct:
in story, we ask "How is the character's inner world transformed?"
in explanation, we ask, "How is the audience's understanding transformed?"
Another:
in story, a single character typically guides our attention & grounds us in their vivid experience (there's an entire art around how to do this effectively, the book Save the Cat itself is a reference to one such technique)
in explanation, we move "up and down the ladder of abstraction", an incredibly challenging & distinct task, and one with major theoretical contributions from folks like
The fact that it's so helpful to contrast good explanation with story, in my view, isn't an argument for calling effective dataviz "storytelling." It's an argument that there's genuine value in distinguishing the techniques of visual explanation from that of storytelling.
4. Despite all this, I think the phrase "storytelling with X" will, by default, win out over other phraseology. This is because it promises a feeling that we all long for: the experience of human connection & intuitive understanding in a world that feels scattered & irresolute. In short, clarity & humanity. And who wouldn't want that feeling when dealing with something as challenging and often overwhelming as data?
There's a deeper idea here, I think:
Marshall McLuhan describes all media as merely extensions of existing human faculties. Clothing is an extension of skin, the wheel is an extension of the foot, digital media an extension of our ability to participate in a village. (it's not a perfect metaphor, haha, but bear with our man Marshall for a moment here...)
Most striking, he sees electronic circuitry as an extension of our nervous system. In this view, our modern-day nerves extend out around the world, pulsing with wars in other countries & the heated thoughts of someone we've never physically met.
Data, in this view, is one way for our digital "nervous system" to feel the world around us. Dataviz, then, is gathering that frenetic stimulus & finding coherence in it. It is not to say our sense of coherence will necessarily reflect the real world, just that it will give us respite from the chaos of our petabytes of nerve endings.
I long for that coherence, sitting in California, eating chicken tzatziki in my pajamas at 4pm as my cats sleep nearby. I think we all do. I am not sure it is always a story. I'm not sure what it all means, or that I will believe it if I do find it. But I know we will keep looking.
Love these examples, Evelina! Hans was the single person who made me shift my entire life toward dataviz!
Could you clarify the connection you’re drawing between “Once upon an time” which opens a traditional story, and these examples you give here? I mean, none of them use “once upon a time” or even explicitly show a character arc.
How do you define story? What would you say to someone who describes your examples simply as “effective data explanations”, not “stories”?
I feel like the term “story” gets overloaded so much, I’m curious when something goes from simply an explanation to a story. I’m sure you have a ton of great thoughts here :)
Thanks so much!
Hi David! Thanks so much for your comment. You're totally right, I could have explained the connection between stories and data stories better. I've actually just updated the post with some more in-depth material that I used in the talk last week :)
Now, on to your questions :) The data stories I showed as examples don't open with "once upon a time" but they do follow a narrative arc in its basic form: they go from setup, to insight, to conclusion. The characters are not really developed as you point out, but thanks to their sequencing, personnalisation and dynamic nature they still read as stories.
That being said, data stories are often much less elaborate than typical stories, but I think we can still call them stories, as long as they have an editorial angle and a structure. I do understand you questioning though, and I've written about it before (here, for example: https://plotting.substack.com/p/core-message)
One last note — I recently discovered the term "data trailer" (used by Brent Dykes), and I thought it was a nice description for the short and simple data stories!
Oooh, I like the term "data trailer" -- maybe there's another article there :)
Thanks for the detailed response & link, Evelina, and just for sharing your expertise online in general!
After reading both articles, I think I have clarity on maybe where I'm feeling confused (and maybe where an expert like you can help clarify!)
As someone working hard to learn how to tell visual & data-driven stories, I feel like there's confusion and frustration I've felt, not just in your articles, but online in general, when it comes to how we use the term "storytelling".
Educators / folks online seem to be obsessed with the idea of "storytelling": that it's powerful, that it's what changes people's minds, that it's what great communicators do.
Initially, this really resonates: I've experienced great stories, & they stick with me. From the film "The Half of It", to Natalie Wynn's "The Hunger", stories I've experienced follow vivid characters & change how I see the world. So, naturally, I'm enteiced when gurus promise to teach me how to recreate this experience with data, or charts, or social issues, or whatever else.
But what I find, in actuality, is that "storytelling" was really just a sexier way of saying "information conveyed in a compelling way", which, if you think about it, is really just good communication. Instead of actual storytelling theory & technique, what I'm given, in reality, is just a grab bag of tips & tricks about good communication, or good display of data, etc.
You yourself seem to take this approach of using story to mean a collection of techniques use to communicate clearly: "a story in the data design world includes a list of elements — charts, illustrations, text — that are sequenced in an engaging way".
Video journalist Johnny Harris says this, too: "storytelling is connecting information into a flow that lets viewers feel or understand something".
Mico Yuk also defines storytelling simply as what you do: "Data storytelling is what you say, what you write and what you draw."
Moritz Stefaner says story is anything that has a beginning, middle, and end.
And on and on...
In any of these cases, if you replaced "storytelling" with "good communication", the definitions not only still work, they're actually much more accurate!
Confusingly, though, people insist, not simply on using the term "storytelling", but on calling upon its elements in ways that (at least for a learner like me), seem completely disconnected from the "data story" they go on to claim uses it. For example in your article:
- we're shown a visual diagram of a narrative structure, but it's never again explained how it actually applies to the following 3 examples (where is the setup? where is the tension? when does the release happen? and where are the visuals showing this?)
- we're told "once upon a time" makes sleepy conference goers perk up, but we're not told *why* that phrase is inherent to so many story openings, or what role it plays in a narrative structure
- we're told we're being shown examples of "data stories", but what's actually called out is "simulation", "clearing up confusion", "reduced the cognitive load for his listeners". Ideas that aren't connected at all to what we experience or understand about traditional stories (or at least the connection isn't made clear in the article)
The result: we've diluted the rich & ancient art of "traditional stories", and also created confusion around good communication branded as "storytelling". And, as a learner, I'm confused how to generalize these ideas to my own work.
Ok, so that's been my struggle as a learner, which I thought my be helpful to you as an expert seeking to teach people like me! :)
Let me, on a more constructive note, say the sort of information I yearn to consume as a learner. Here's the article I've yet to find but am looking for:
1. Begin with a clear theory of what story is: eg "story a fractal made of arcs of human desire: birth, pursuit, & resolution of desire. At any scale -- a single story scene, to a full narrative arc -- a human desire is awakened, confronts obstacles with clear stakes, & comes to some close. Storytelling, then, is the art of constructing a world in which the audience can empathize with a clear desire, lean in as it faces clear obstacles, & find a resolution that leaves them with a deeper understanding of the world." (this is just an example definition I stole from Scott McCloud; Robert McKee, Shonda Rhimes, etc could all provide their more expert definitions)
2. Clearly present how you will re-appropriate parts of storytelling into explaining data: what's needed? what can be dialed up or down? what differs & why? eg "The art of 'data storytelling', in larger part, is simply the art of 'curiosity-driven explanation'. In this context, the desire awakened is curiosity about the central question your data will answer. The conflict may be a number of things: the inherent challenge of answering that question, the events which might lead to a bad resolution of the topic, etc. The resolution is the payoff from carefully facing the conflict with data, and finding something that changes our worldview. This pattern, like traditional story, is also factal: within your larger point, you create sub-points that follow the arc of curiosity, as well."
3. Then, that data storytelling framework is clearly (and visually!) applied through examples. In the case of your 3 examples, I'd say only Hans would count as a data story under this example definition of data storytelling, and even then it takes a good bit of teasing it out to see: "[Present a Visual showing a timeline of his talk, with different sections highlighted and annotated] Hans shows that even highly educated people had a wildly incorrect view of the world in this portion of his talk. Which sets up his central curiosity articulated here in the timeline: how has the world actually changed? And the conflict here: people negatively stereotype other nations. And the climax: Hans shows us the true progression with an amazing chart. But, true to the fractal nature of story, if you zoom into this region of the timeline where he presents the chart, you find yet another desire/curiosity-arc (story). Hans sets up a clear desire: to see humanity progress to a healthy state (which is represented delightfully by dots flocking to the opposite corner). The conflict is the actual events of history: AIDS, wars, resistance to reproductive rights. And the resolution is spectacular: a world much further progressed than any one of us thought."
This is a much harder article to write, principally because it requires coherent theory both of story and of "data story". But it's one that moves us away from simply passing out grab bags of communication tips, toward deeply understanding (and thus reproducing) great data storytelling.
And, for that, I think it'd be worth the time & effort.
____
As you can see, I'm really passionate about learning from folks like you how to tell great visual stories, and actually I've been collecting a list of advice from great visual storytellers, I'd love to get your thoughts on it!
https://www.youtube.com/playlist?list=PL6oJ6o8OuPTZTvbxKm8REwZY39El4CbU5
Haha, you'll notice the first video is one I made analyzing Johnny Harris, and he commented on it saying it was accurate, which is why I included it :).
There's also a brilliant talk by Nicky Case (who I'm sure you're very familiar with!) which I feel like is pretty related to what you write about. Curious your thought on it!
Thank you for receiving these thoughts & questions so graciously, and thank you for the larger work you do!
Hi David, I agree with you that the term storytelling is used a lot in the world of data, and not always in a way that is appropriate. The times when we use it to say that "data visualisation = data storytelling" are the ones I've got issue with. The remaining cases are much less black-and-white.
As a fan of ancient rhetoric and Aristotle's work, I agree with you that the "real" storytelling is much deeper and much better than what we often call data stories. That being said, I would't go as far as to say that it's *not* storytelling at all. I think we can introduce little elements of storytelling in our data work to make communication flow better, be clearer and more human. Those can be a core message, some design elements that draw your eyes to key takeways, etc. We won't always have the space for character development, for instance, so it may be more implicit than explicit.
In each edition of my newsletter, my goal is more to plant seeds and encourage people to think about things (or even discuss and disagree like ;) ), then go in full depth and explain every single detail of every example I share. It's also a living and breathing collection of thoughts -- what I'm thinking and saying today will evolve and I may write something different next year. And that's OK.
To really go in full depth and learn how to build a narrative arc for a data story, one needs to attend a training with me. It seems like you've given a lot of thought to the topic, so perhaps you should write a book about it. :)
In short, I think it's OK to use the term storytelling for something that is not a masterpiece written by Shonda Rhimes. Perhaps there's a hierarchy to be created here: a split between *long-form data stories* that have most traditional story elements in them, and *lighter data stories* (these need a better name).
Evelina,
Thanks for pointing out that your posts are just little gems you share with the world along your journey, and that the full treasure is in your workshops & working with you. I believe it! :) Haha, sorry if I got too fixated on one post! But, you're right that the posts are definitely working: I've appreciated reading them, & they've definitely got me thinking! Thank you!
I really like the points you make in your last comment, they make me think about things in a few different ways:
1. Rather than being seen as binary "is this truly a Shonda Rhimes-level narrative?", data storytelling might be more helpfully seen as existing on a 2D spectrum:
- from short <--> long form. You suggested the language of "trailer <--> story", which I think is great!
- from pure quantitative description <--> full-on story (ie with characters, emotional arcs, etc)
It seems like practitioners like you focus on nudging people from "data speaks for itself" side of the spectrum toward "how do I guide my audience through the core ideas in the data, and create a human connection?" side. And do so by drawing comparisons to story, which people already value & recognize. Which is really effective, I suspect!
2. I originally protested the use of "storytelling" to mean "good communication/explanation", perhaps because, after spending a long time deep in the weeds of both data & story & explanation... I've become a bit of a snob, haha. I think: why call it a "data story" when it's more aptly called "a well-labeled, interactive chart with some examples that help you see how it relates to you"?
But I'm suspecting now that "storytelling", in the wild, means something much more basic than I initially assumed from my tower in Snob-land: maybe it simply describes guiding others through data, rather than assuming the data "speaks for itself."
Perhaps what is meant is not so much that one should specifically utilize actual mechanics of storytelling, but more that we should *act more like storytellers* when presenting data -- ie we should skillfully capture our audience's attention, & guide them carefully through the data.
When someone says, "look at this data story!", what they're often really saying is, "look at the skill with which the author guides us through the data & its implications!"
Defining data storytelling as "guiding someone through data with a level of care comparable to a great storyteller" makes a lot of sense to me! (Even if it doesn't quite roll off the tongue, haha)
I think the term "data storytelling" catches on because we're so familiar with great stories, & so unfamiliar with even the *concept* of clearly explained data. We notice an incoherent story instantly -- an incoherent data visualization is glossed over without note, or perhaps with the belief we lack the expertise to parse it. Often, we're so impressed when a complex, data-based concept is explained clearly, the best way we can describe the feeling is "storytelling", because that's the last place we felt such clarity.
This is why things like the WaPo COVID simulation get labeled as storytelling: not because they correspond in any way to actual stories, but because they bring a level of clarity & connection we typically only experience in stories.
3. Perhaps the other aspect I'm realizing rubs me the wrong way about "data storytelling" is that there's already an entire emergent practice dedicated to explaining complex ideas digitally, one that's perhaps more relevant than the catch-all art of "storytelling." (Also one that I've been laboring to try to grasp for years, haha, which is maybe why I'm so intent on this distinction! Please forgive me :)
Here are quick examples of how the forms are clearly distinct:
from the indomitable Nicky Case:
in story, we say "show don't tell"
but in explanation, we say "show, *then* tell"
https://youtu.be/b-M2U3Jl1Cg?t=306
Here's another:
in story, we ask "How is the character's inner world transformed?"
in explanation, we ask, "How is the audience's understanding transformed?"
Another:
in story, a single character typically guides our attention & grounds us in their vivid experience (there's an entire art around how to do this effectively, the book Save the Cat itself is a reference to one such technique)
in explanation, we move "up and down the ladder of abstraction", an incredibly challenging & distinct task, and one with major theoretical contributions from folks like
Bret Victor http://worrydream.com/LadderOfAbstraction/
and later
Susie Lu https://youtu.be/BaLPkuWRnHE?t=183
The fact that it's so helpful to contrast good explanation with story, in my view, isn't an argument for calling effective dataviz "storytelling." It's an argument that there's genuine value in distinguishing the techniques of visual explanation from that of storytelling.
4. Despite all this, I think the phrase "storytelling with X" will, by default, win out over other phraseology. This is because it promises a feeling that we all long for: the experience of human connection & intuitive understanding in a world that feels scattered & irresolute. In short, clarity & humanity. And who wouldn't want that feeling when dealing with something as challenging and often overwhelming as data?
There's a deeper idea here, I think:
Marshall McLuhan describes all media as merely extensions of existing human faculties. Clothing is an extension of skin, the wheel is an extension of the foot, digital media an extension of our ability to participate in a village. (it's not a perfect metaphor, haha, but bear with our man Marshall for a moment here...)
Most striking, he sees electronic circuitry as an extension of our nervous system. In this view, our modern-day nerves extend out around the world, pulsing with wars in other countries & the heated thoughts of someone we've never physically met.
Data, in this view, is one way for our digital "nervous system" to feel the world around us. Dataviz, then, is gathering that frenetic stimulus & finding coherence in it. It is not to say our sense of coherence will necessarily reflect the real world, just that it will give us respite from the chaos of our petabytes of nerve endings.
I long for that coherence, sitting in California, eating chicken tzatziki in my pajamas at 4pm as my cats sleep nearby. I think we all do. I am not sure it is always a story. I'm not sure what it all means, or that I will believe it if I do find it. But I know we will keep looking.