Damasio’s research into why this happened changed how we think about the brain: emotion isn’t the obstacle to good decisions. It’s a prerequisite.

Our brain processes information through two systems: affect (emotion) and cognition (reasoning). The affective system makes judgements—it tells you whether something is good or bad. The cognitive system interprets and makes sense of things. The two work together, and neither is sufficient on its own.

What does this have to do with data storytelling though? A lot, actually. If you want your audience to make decisions based on data, you cannot take emotion out of the equation—whether you’re sharing a chart internally or running an external communications campaign.

Can dashboards be emotional?

Let’s start with the most improbable place for the affect system to hide in. When you think about emotion in data design, dashboards probably aren’t what comes to mind. They’re grids of charts, often updated automatically, glanced at once per day or week. But they’re not doomed to feel lifeless.

Take this generic carbon emissions dashboard. What do you feel looking at it? Probably nothing. It’s accurate, sure, but it doesn’t make you feel anything.

Now, how about this one?

What changed? The titles now ask questions: “Are we on track?”, “Which entity should we focus on?” Colour is used with meaning, like red flags where targets are missed. And each chart carries a short annotation underneath the headline.

That’s all it takes. You are now reacting to the numbers. What you feel may vary: maybe it’s worry, maybe it’s relief. But you do feel something, and that is what will push you to act.

The underrated emotion for presentations

You have certainly made a slide deck (or 473 of them) in your career. And I'd be willing to bet that for many of those, the audience's reaction was… polite attention. But when you present data, you have the most powerful emotion at your disposal: surprise. Think about it. A chart that appears all at once is information. A chart that builds, element by element, is a story. Your audience doesn’t know what’s coming next. They’re leaning to find out more.

You can add surprise to your presentations in a couple of ways. Highlight elements one by one, like in this Gapminder example.

Or go further and actually reveal the data piece by piece.

The difference is subtle in theory but powerful in practice—you’re guiding your audience through data discovery.

When (too much) emotion is already there

So far, we’ve been adding emotion where there isn’t enough of it. But what about the opposite situation? What do you do when the topic is already so emotionally charged that the audience risks shutting down?

Last year, we started a pro bono initiative at Parabole: data storytelling pieces on topics that matter but don’t get enough attention. The first one we published was about spina bifida, one of the most common fetal anomalies that can turn your life upside down in 2 minutes. And it was incredibly personal to me.

The instinct might be to lean into the emotion. But since the story already carried so much sadness, we needed data to steady it. So we alternated between the personal narrative, illustrations and charts that make the story real, trustworthy and hopeful.

The goal is the same: find the balance between what your audience feels and what they understand. One without the other won’t move anyone to act.

If we believe that we need our stories to make an audience feel something so that they do something, then we need to become students of emotional intelligence. —Jeremy Connell-Waite

Next time you design or present with data, start with the emotion angle and see how it changes the outcome.

Thanks for reading!

See you next month,

—Evelina

Let’s break it down through the lens of a real project: the AI Disruption Index we recently worked on with Moloco and BCG.

Start with the conflict

For a story to be interesting, there needs to be conflict. An event that shatters the status quo and grips the reader: what will happen next? In screenwriting, this is called the inciting incident. You know how, every time you watch a film, there are a few minutes of stage-setting and then bam... something happens that shifts the life of the main characters.

For the AI Disruption Index, this moment was the behavioural shifts that are now observed because of people’s use of AI. A third of adults discover brands through personal AI agents. 47% of consumers use AI to research purchases. That kind of thing.

Now, the conflict won’t be the first section that appears in your data piece. But it should be the starting point of your narrative thinking. No conflict, no story.

Skip the boring “Hello”

So if the conflict isn’t the first thing your audience sees, what is? The hook.

In real life, we tend to begin presentations with “Hi, my name is X, and I do Y”. While that is nice and comfortable for us, the audience simply doesn’t care. Or at least not right away. They first need to know: will this be worth my time? Should I keep reading past the opening screen?

That’s why, for the AI Disruption Index, we hooked the audience with a provocative statement before even giving the title of the piece: “The biggest shifts in AI today aren’t technological, they’re behavioural. Consumer behaviour is shifting and we have compiled the playbook for you.”

Once the hook is in, you set a bit of context: the research you did, the status quo of the market, the general overview. Think of it as earning the audience’s trust before you take them deeper.

Dive deeper

And deeper is where it gets interesting. What does the data say? How can your audience explore the different insights?

This is the climax of your data narrative: the place where the audience goes from “huh” (questions, curiosity gaps you created earlier) to “aha!” (realisations, understanding).

For the AI Disruption Index, we layered the climax in three ways. First, we introduced the different quadrants of the index: undefended, secured, breached and contested. Then, we placed different industries inside those quadrants: fintech and social under secured, news under breached, gaming under undefended and so on. Finally, we gave users the opportunity to dig even deeper: clicking on their industry reveals example companies, quotes from marketing leaders and a deep dive into the methodology.

The climax is the longest part of your data story. It’s up to you how you slice and dice it, how deep you want to go. After that, you move into the closure: the call to action and recommendations.

Read full piece

Make it yours

The story arc is a starting point, not a strict formula. Play with it, adapt it to your topic and your audience. The only rule that really matters: give your readers a reason to care before you give them data to process.

Thanks for reading!

See you next month,

—Evelina

This kind of sentence is usually shouted at you by a performance coach asking for pushups. And it’s not associated with happy moments. Recently, I stumbled upon this concept in an unexpected way—in a business book.

In The Business of Expertise, David C. Baker encourages the reader to provide 20 insights that emerge from their field of expertise and their experience. The rules are simple: assume that the reader is smart and that they know a bit about your area.

I loved the exercise, so I gave it a go. Below are mine, in no specific order!

20 salient learnings from the field of data communication.

#1 You need two levels of information hierarchy. Don’t expect people to always read 100% of your content: highlight the most salient information so it’s scannable quickly, and leave the rest for those who will read the entire piece.

#2 Colour perception is wildly personal. Different people will view different variations (or even hues!), so make sure you’re not relying on colour alone for your data insights.

#3 Storytelling requires real thinking and structure. Doing data storytelling doesn’t equate just making a data visualisation; you employ actual narrative and rhetorical techniques.

#4 Copywriting is not an after-thought. You need to practice and master effective title writing to engage the audience and create message redundancy; the data does not speak for itself.

#5 Keeping up with the field is part of your job. While looking at nice projects published by others can seem like leisure, it’s actually key to developing your design and storytelling eye.

#6 It's your job to advocate for best practices. Whether you have external or internal clients, you’ll sometimes need to explain the research and push back on what the stakeholders are asking—even if you end up going with their request.

#7 Expect the process to be iterative. Don’t have high hopes for finalising the chart or storytelling piece in one go; quality results require discussion and time.

#8 You’re a teacher half of the time. Whatever your focus within the field, pedagogy and onboarding will take a big chunk of your time (and patience).

#9 You need to be a jack of all trades. Even if you have a team of specialists, you’ll still need a basic understanding of many fields—communication, design, web, data— and it's both scary and fun.

#10 Communication is your superpower. You need to work on clear and effective messaging to explain your approach, outputs and roadblocks.

#11 Think of the human behind the data. Whether you’re reporting app usage stats or the number of people with access to water, always consider that there are humans behind that data—would they be happy with how you’re communicating it?

#12 You need to know many tools. Unless you specialise in one superpower like D3.js, you’ll need to switch easily from one tool to another depending on the client’s needs—not mastering it 100%, but enough to be operational quickly.

#13 Compromise is your second name. The most beautiful or creative solution might not be the best one for the stakeholders; you’ll often need to swallow your pride and go with the group’s consensus.

#14 You cannot do it alone. Even though you have an understanding of many fields to communicate data effectively, you’ll need help from experts to deliver parts of the projects and that’s ok; it will only make them better.

#15 You repeat “it depends” like a broken record. Most of the time, when you’re asked which chart or narrative structure is best, you’ll have to consider a dozen variables and the solution won’t be straightforward.

#16 No two projects will look the same. If you’re lucky, you’ll get to work on different projects and industries; it will be both thrilling and terrifying each time.

#17 Feedback might be uncomfortable. A big part of your job is to accept and integrate constructive criticism, for which you’ll need to develop thick skin.

#18 Simplicity is harder than complexity. Removing information from a design takes more skill and confidence than adding to it; the best data narratives are often the most concise ones.

#19 Your audience is not you. What seems obvious to you after weeks of staring at a dataset will not be obvious to someone seeing the chart for 5 seconds—test your work on fresh eyes.

#20 The field is still being defined. Data storytelling sits at the crossroads of many disciplines, and that means you’ll regularly have to explain what you do and why it matters.

Baker writes, “if you can’t articulate your expertise quickly, concisely and in a compelling manner, it’s just simply not true.” I'd add: even if you cannot get to 20 insights right away, the exercise of trying will sharpen how you think and talk about what you do.

What would you add for data storytelling?

Thanks for reading! See you next month.

—Evelina

“The size of the area was as much as 250 football fields.”

Can you visualise it in your head? Maybe, but likely not.

This kind of analogy is common in data storytelling. Its intention is good: to give the audience a reference point so they can better grasp the scale of an unknown object. But is it actually helpful? I’m not so sure. 250 football fields are still quite hard to comprehend.

That said, analogies are an incredible tool in our data storytelling belt. So, let’s look at how they work and how to use them to our advantage.

The analogy way

In philosophy and logic, analogies are primarily used as arguments or heuristic tools to transfer knowledge from a known subject (the source) to an unknown one (the target). They are widely recognised as aids to discovery.

According to Joseph Priestley, a pioneer in chemistry and electricity:

“Analogy is our best guide in all philosophical investigations; and all discoveries, which were not made by mere accident, have been made by the help of it.” (1769/1966: 14)

In today’s world, when we say “analogy,” we typically mean literary analogies: rhetorical figures used to explain an abstract concept by comparing it to a familiar one. For example:

“The structure of an atom is like a solar system. The nucleus is the sun and electrons are the planets orbiting it.”

In the context of data storytelling, analogies are crucial for bridging the gap between numbers and human understanding. While standard philosophical analogies focus on logic, data storytelling analogies focus on scale, process and impact.

Analogies that stick

Philosophy and history aside, which types of analogies are most useful for us today? Here are three key types and some real-world examples.

Magnitude analogies (aka social math)

This is arguably the most useful type of analogy for data communication. It translates vague figures into human-scale concepts. The goal is to make numbers “feel” real and encourage action.

My favourite example of this is the popcorn communications campaign in the book Made to Stick. Here is the story:

In 1992, Art Silverman had a tricky message to convey to the American public. The nutrition research group that he worked for had just found out that seemingly harmless popcorn in movie theatres was actually full of fat.

Nutritionists recommend that we eat no more than 20 grams of saturated fat each day. A medium-sized bag of popcorn had 37 grams—two days’ worth. The culprit was coconut oil, which theatres used to pop their popcorn. It was brimming with saturated fat.

So how to communicate the message of 37 grams of saturated fat? Silverman could have created a visual comparison—perhaps a bar graph, with one of the bars stretching twice as high as the other. But that was too dry and academic.

Instead, he called a press conference and announced the following: “A medium-sized popcorn at a typical movie theatre contains more artery-clogging fat than a bacon-and-eggs breakfast, a Big Mac and fries for lunch, and a steak dinner—combined!”

What happened here? The raw data said, “37 grams.” To most people, that number is meaningless. So Silverman turned it into a relatable analogy: three greasy meals for the size of one popcorn bag. It should come as no surprise that the story went viral. Sales plunged, and soon after, most of the nation’s biggest theatre chains announced they would stop using coconut oil.

Isn’t that the kind of impact we all dream of with data storytelling?

Process analogies (aka functional metaphors)

Sometimes you need to explain the mechanism before you get to the data. And that isn’t always as easy as it sounds. A complex scientific or biological process often needs an analogy to land just right.

A brilliant example of this is the article called Why outbreaks like coronavirus spread exponentially by Harry Stevens (remember the Covid lockdowns?). It illustrates how the virus travels between us and what social distancing can do.

To make concepts like “exponential growth” and “social distancing” accessible, the author simulated bouncing balls in a box:

• A healthy person is a moving dot.

• A sick person is a distinct coloured dot.

• The process of infection is a simple collision: if a sick dot hits a healthy dot, it changes colour.

"Social distancing" is explained simply by freezing some of the dots so they stop moving.

Why does this work? Well, I’m still talking about it five years later, so clearly, the approach stuck. It’s memorable because it strips away the biology and focuses on the mechanism of contact. You don’t need to understand virology to see that “dots that don’t move don’t touch other dots.”

Visual analogies (aka design-as-data)

Last but not least, your analogy doesn’t always have to be separate from the data story; it can be the data story.

A visual analogy involves designing the visualisation itself to resemble the subject matter, aiding memory and recognition. We create an emotional connection through design—the chart's shape mimics the physical object being measured.

My all-time favourite example is Drowning in Plastic by Marco Hernandez and Simon Scarr. To show the number of plastic bottles that accumulate throughout a day, month, or year, the authors don't just use words. They visualise those bottles piling up against famous landmarks.

Throughout the article, we see:

• A pile of bottles the size of the Christ the Redeemer statue in Rio.

• A pile half the height of the Eiffel Tower.

• A mountain towering over the Burj Khalifa.

Not to mention those bottles falling from the sky as soon as you open the page!

This works because it solves the problem of abstraction. We cannot visualise "billions of bottles," but we can visualise the Eiffel Tower. By literally "drowning" a city in data points, the graphic triggers a feeling of suffocation and scale that a spreadsheet never could.

Which one should you use?

If you’re wondering which type of analogy to try first, here is a quick cheat sheet:

• Use visual analogies (like Reuters) when you need emotional impact (e.g. environmental issues, crisis data).

• Use magnitude analogies when you need scale (e.g. budget deficits, user growth).

• Use process analogies when you need understanding or buy-in (e.g. explaining why a project takes time).

If the examples above inspired you but you’re not sure how to implement them yourself, don’t panic! Parabole is here to help—drop us a note and we’ll define the best way to help you.

Thanks for reading this first edition of 2026. See you next month. ✨

—Evelina

Need I go on? These are the topics dominating the news. All of them are slightly (or very) overwhelming. The future might feel the most uncertain it has ever felt in your lifetime, especially if you’re a junior person just entering the job market.

But I do think there is reason to be excited; we just need to shift our focus. So what still matters and how does that impact the future of this publication?

Three trends to embrace in 2026.

In this uncertain world where robots are presumably taking over, there are three shifts to bet on—some we cannot run from, and others that matter more than ever.

🤖 Automation.

Like it or not, AI is here to stay. We can debate how it devalues the creative industry or increases environmental costs, but we cannot wish it away. Ignoring it risks obsolescence; clients and employers may view manual-only workflows as outdated, which could harm your future career prospects. Our best bet is to use it smartly.

In our line of work—creating narratives with data—AI is a capable assistant that saves time. It can be great for tasks that drain time without adding high value, such as:

• Drafting background context and sourcing data

• Cleaning or formatting datasets

• Copywriting and proofreading

• Designing quick prototypes

In all these scenarios, you remain the lead. Check its work, review the output, and never use it as the primary writer or designer.

💙 Impact.

In an uncertain job market, there is a yearning for more meaning. Many professionals are doubling down on projects that truly matter. On a smaller scale, design studios are focusing on non-profit work despite funding cuts. On a macro scale, the European Union just approved the revised Sustainable Finance Disclosure Regulation (SFDR)—a framework that formally acknowledges impact investing in EU legislation.

This might sound like dry policy, but for European citizens, it translates into stronger support for the green, digital, and social economies. It is a structural example of impact moving to the centre of policy exactly when it feels like the first thing we might abandon.

At Parabole Studio, we are following suit: doubling down on projects that create tangible impact for the economy and society.

📣 Storytelling.

Ever been told that “data storyteller” isn’t a real job? I certainly have. Turns out, it’s now all the hype.

According to a recent WSJ article, the percentage of LinkedIn job postings in the U.S. that include the term storyteller doubled in the past year—that is about 50,000 listings in marketing and 20,000 in media and communications.

This sends a signal that data storytelling also matters more than ever. Projects with a human touch that help digest data in approachable ways will help crucial topics standout in the AI content craze. An artificial intelligence can generate a dashboard to track KPIs in minutes, but ask it to write a human-focused data narrative? You’ll likely come out disappointed.

So where do we go from here?

A new chapter for The Plot—more focused, deeper content.

Based on these reflections, The Plot is changing its format and frequency.

Starting January 2026, it will become a monthly publication. We’ll focus on slightly longer (but not too long, I promise) content, alternating between:

1. Analytical deep dives: Exploring specific data storytelling techniques and skills needed in this new landscape.

2. Case studies: Breakdowns of projects from our studio and examples of impactful work from the wider community.

If you’re looking for more frequent inspiration to fill the gap, here are my top recommendations for newsletters that offer a quick dose of examples:

• Quantum of Sollazzo

• The Visualising Data Newsletter

• Datawrapper’s Weekly Dispatch

• Off the Charts

• Buena Vista Data Club (in French)

Thanks for reading! I hope you have a wonderful holiday season, if you celebrate. I’ll see you back here in about a month. ✨

—Evelina

It tells the story of spina bifida—a severe malformation of a spine that leads to life-long disabilities. My husband and I found out that our first baby had this condition last June, halfway through my pregnancy. What followed was heartbreak and a termination.

Read full story

That, however, is not the point of the story. The point is that thousands of expecting parents face this diagnosis every year. Yet before it knocked on my door, I had never heard of spina bifida. So, our team decided to change that in the best way we know how: through data storytelling.

We wrote and designed a data narrative about spina bifida: how it happens, what it means and what can be done to reduce the risk. In this edition of The Plot, I’ll give you a glimpse behind the scenes into some of the decisions we made to bring it to life.

Introducing gentle darkness

From the start, we knew the visual direction of the web page had to be dark. The subject is grim and there is no way around that. But we wanted a kind of darkness that still feels gentle, almost soothing, not sad or overwhelming. We looked at inspirational pieces from Reuters, National Geographic and Pentagram, to mention just a few.

In the end, we chose a background that isn’t pure black, but deep and muted. Dark purples and neutral beiges became the backbone of the palette, with light greens and reds used to bring contrast and highlight key data and illustration elements.

Typography followed the same principle: a soft serif for titles, paired with a clean, modern grotesque for body text. We used a monospace cut of the same typeface for annotations and data labels as a way to give them more weight while maintaining the textual harmony.

The result is a creative direction that feels calm, almost tender, even as it tells a difficult story.

Keeping it real, but not too real

If you Google “spina bifida” (which I don’t recommend), you’ll find quite graphic images of newborns with exposed spines. Technically, they’re accurate, but are they helpful? Not that much. We wanted to inform, not give people nightmares. So instead, our designer Valeria Beccari created gentle, watercolour-style illustrations that explain the anomaly without shocking the reader.

Making the science accessible

Writing a narrative based on scientific research is always a trade off between precision and accessibility. For this story, we needed to make that call in multiple instances. First off, we focused on the three main spina bifida types—split into closed and open. Technically, we could have mentioned a couple of other types in between that are neither completely closed nor open. That would have added a level of precision to this chapter, but also brought confusion. So as the medical community considers the defect open if it’s not completely closed, we stuck with this simplified list.

It took us quite some time to make the data accessible too. To give you an example, we had a long debate on how to show the prevalence rates of spina bifida. In the most recent, reliable reports, you may find 3-5 different figures for the same country. Not because some of them are more correct than others but because pregnancy outcomes are tracked in cohort studies that use different methodologies.

Initially, we wanted to show all these datapoints. But early reviewers (thanks, Jorge!) pointed out that the distribution was confusing rather than enlightening. So we simplified the graph by showing only the highest metric per country. Now, the reader can focus on the bigger picture: “how common is this in my country”?

This wasn’t an easy story to write but we’re quite proud of the result. The initial response to its launch was overwhelmingly positive and we hope it will act as a valuable resource for expecting parents and their families for a long time.

If you have ideas for the next Parabole Original—a data narrative on a topic that doesn’t get enough attention—let us know in the comments!

Thanks for getting personal with me,

—Evelina

Many publicly available databanks still behave like that. But today, thanks to AI, we can do better without breaking the bank. And I’m proud to say that we already have.

Over the last few months, our team helped One Campaign design the ONE Data Agent™—a tool that lets you access health financing data in seconds, without a headache. It takes the pain points of traditional data-search experiences and gives them a hug. See for yourself.

What’s different? Instead of browsing dozens of indicators that have a remote connection to your keywords, you can simply ask a question in natural language. Then, refine your results with follow-up requests or adjust filters in the control panel. Neat, isn’t it?

Try the ONE Data Agent™

Is the ONE Data Agent™ perfect? Of course not. Like any innovation, it will need many tryouts and tweaks to become robust in the long run. But the precedent is set: with AI agents, we can now significantly improve the data-search experience at a lower development cost. Not only for health financing, but for any domain. And who wouldn’t want to live in a world where data collection is headache-free?

Thanks for dreaming with me,

—Evelina

To make the task less overwhelming (and, dare I say, enjoyable!), use this publication as a three-step cheat-sheet when you plan your next impact report.

Build a solid narrative structure

Start by asking: what do you want to communicate? What belongs in the report so readers see who you are, where you’ve been and where you’re headed? Think in broad strokes first—chapters and sequencing. Many of the best reports follow this three-act shape:

• Act 1: Overview: origins, strategy, team and community. Often includes a CEO or stakeholder letter.

• Act 2: Deep-dive into your impact on people and planet, often broken down by type or audience.

• Act 3: Concluding notes, or your strategy for the coming years.

Act 2 is usually the longest and a good place for creative title writing. Recent examples from Dept, Vinted and Lululemon show how strong section names pull readers in:

If that sounds like a big undertaking, that’s because it is. An impact report is complex and time-intensive. But it should be a team effort. So even if you’re the point person, bring in colleagues from programmes, data, finance, HR and comms and, when useful, external consultants and advisors to help you flesh out the narrative.

It’s worth investing early in a content structure that fits your organisation’s goals, editorial style and audience. Once the foundation is set, you can reuse the same framework year after year—updating results rather than reinventing the report, and shortening timelines in the process.

Set measurable goals

With the structure ready, enrich it with data. Regardless of the type of work you do, you need figures to back up the claims on what you’ve achieved. Instead of simply including some time-series and bar charts here and there, treat data as your through-line. Start with a north-star metric: the one (or three) goals your organisation is working toward. Then make the report a transparent progress tracker.

OpenClassrooms, an online education company, has recently announced that:

They’ve been reporting this figure in their mission reports for a while now. Their “north-star” target is 500,000 positive career outcomes per year, and from 2025 onward, they aim to hit that level every year.

Oatly, a food company that makes products from oats, set multiple objectives in their sustainability reports. They then share the progress in each of the ambitious pillars, using them to divide the report into smaller sections.

What stands out about Oatly is that they don’t pretend every target was a win. It seems counterintuitive at first—impact reports are meant to showcase successes—but candour about failures builds trust. Their cover slogan says it best: “Wake up. Fail. Succeed. Try again.” Food for thought?

Let professionals do the design

You have the structure and content, and the data is set—so what’s left? Making the report inviting to read. You probably have an in-house design team that can set the creative direction and layout of the report, but that’s not all. The charts need the same level of attention and you may need to hire outside help specialised in data visualisation.

When I open an impact report, I can tell really quickly how much effort went into its design. Many times, the overall look is polished but the graphics are weak. The first sign of that? Overdose of pies. And sometimes it feels like the design team wasn’t involved at all.

Design decisions can make or break the report and you should take them seriously. A professional output should include:

• An effective creative direction, both unique and on-brand

• Varied chart choices that fit the data, not only pies and bars

• Consistency across typography, colours and graphics

Oatly’s reports are a great example of significant design effort. Even though each page communicates different information, the style remains the coherent.

If their playful tone isn’t your style, Lululemon shows how a gentler approach can still be coherent, clear and unique.

Whatever your style, what matters is thought, effort and consistency. You want readers to feel they’re holding an artefact the company considered important enough to spend time on.

Bottom line: Narrative, data and design will take you from zero to an effective impact report. Set your editorial and design style, bring in the right stakeholders and don’t be shy about repeating the same structure year on year.

Feeling lost? We’re here to help.

Making impact reports is our specialty at Parabole Studio! Reach out and we’ll help you create a report that has real impact. 💫

Get in touch

But communicating with data doesn’t need to be daunting. You can take baby steps and start with some simple numbers.

Tell them who you are

Help your audience grasp how big your organisation, where to find it and what you do exactly. Simple sentences will do the trick: “6,412 students trained”, “Stores in 25 countries”, “2.3 million meals distributed since launch.” Place these figures where people meet you—website home page, newsletter intro or social bio.

Tell them what you’ve achieved

You can also go a bit deeper and show your target clients what to expect from your product or service. If you track outcomes, let them shine: “Onboarding time reduced from 14 days to 10”, “$2m saved across 48 clients”, “Exam pass rate up 18 percentage points year on year”. One short sentence of context—who benefited and how—turns the number into a tangible benefit.

Tell them where you’re headed

For the more ambitious among us, north-star metrics are a great way to enrich your communications with data. Share a goal your organisation is aiming towards and the progress you’ve made: “Goal: house 1,000 people by Dec 2026. Current: 742. This month: +42 thanks to the X partnership.” Regular updates will show you’re committed and transparent (and create easy content opportunities as a bonus!).

These examples show that data storytelling doesn’t need to be a huge endeavour right off the bat. Yes, you need data to be build trust with your audience, but you can start with small narrative elements. Pick the best figures that show your reach and impact and reuse them across different formats—website, newsletter, socials or slide decks.

Start today!

More in two weeks,

—Evelina

When I first started working with clients, this was an important question. I kept hearing that other designers were integrating the number of reviews in their client agreements, and many of them went for two. That sounded like enough to get to a decent result, so I did the same.

But I quickly ran into an unpleasant hurdle: reinforcement. Telling a client, “You’ve used your two rounds; we stop here unless you pay more,” wasn’t fun at all. I didn’t want to stop if the client wasn’t happy yet.

Great work takes time. Each adjustment round sharpens thinking and outcome until the piece reflects both the client’s vision and the designer’s craft. Sometimes that happens in one review. Sometimes it’s two. Or six.

It’s not about the count. It’s the outcome that matters. That’s why our studio offers unlimited iterations. Yes, I said unlimited. Outrageous? Possibly. But it’s how we consistently reach high-quality data communication.

However, for this concept to work, a few things must be true:

• Clear budget. Unlimited is priced in—the project fee anticipates reviews and refinement, with milestones and scope defined up front.

• Mutual respect. Designer and client treat each other’s time and expertise seriously.

• Good faith. No edits just because.

• Reasoned debate. Both parties can explain why a certain choice is better and be listened to until agreement is reached.

If those fundamentals aren’t there, the collaboration probably shouldn’t happen—whether you’re counting iterations or not.

Your turn: Unlimited iterations—love it or hate it?

Thanks for reading!

See you in two weeks,

—Evelina

This newsletter has been on a pause for a few months as I took time off writing for personal reasons. Starting today, we’re going back to a bi-weekly publication schedule for the remainder of the year. You can expect:

• Content on how to improve data communication in your organisation;

• Original data stories published by our studio;

• User-requested content (don’t be shy—reply to this email and let me know what topics you’d like to read about the most!).

If you read recent impact reports or scroll interactive sites, you’ll spot charts that use gradients, alternate hues or stay defiantly monochrome. Are we entering a new colour era in dataviz?

Let’s tour the evidence.

1. Gradients

Remember when gradients were banned for obscuring data edges? Dept® challenged that dogma in their recent impact report with soft colour transitions in each chart.

Figures & Figures turned it up for a Vox climate project—employing colour fades that guide the eye along evolving temperatures.

Finally, AlgoTV proved gradients can even survive motion in animated dashboards.

2. Alternating hues

Rainbow categories are considered one of the biggest chart crimes. Yet ADL’s recent report alternated bar colours to brilliant effect: each value feels distinct and the pattern itself mirrors fragmentation.

Google’s Material Design 3 goes even further, exploring expressive palettes that shift colour, shape and hierarchy. Their research on the topic is fascinating, too.

3. Monochrome

Are single-hue charts always bland? A recent design trend proves the opposite. In this example, The Guardian used nothing but deep reds to tell the Trump approval rating story.

The web design below follows a similar technique, alternating between light and dark yellows and purples.

And this plant-focused graphic employs greens only.

What conclusions can we draw from the above examples? The arguments against such experimentation typically focus on legibility issues.

So the question is: Could you read each graphic above quickly? If yes, the design passes. The perceived danger of too much colour may be smaller than the engagement you gain—provided you layer in labels, tooltips or annotations for added clarity.

Over to you

We’re already testing bolder colour palettes at our studio. If you feel adventurous to try to explore them with us, here are our best tips:

• Gradients: Try it if you have continuous data and want to signal movement. Watch out for low contrast at the extremes; annotate start/end values for clarity.

• Alternating hues: Try it if categorical data needs equal weight and labels alone aren’t enough. Watch out for accidental hierarchies—our brains will search for a dominant tone.

• Monochrome charts: Try it if the narrative leans on brand colour or mood. Watch out for adequate light/dark contrast (WCAG 2.1 AA is your friend).

Thanks for reading!

See you soon,

—Evelina

Ad Herennium is the oldest surviving Latin book on rhetoric. Historians aren't sure who wrote it—some say Cicero, others Cornificius—but its content is still incredibly relevant (more than 2,000 years later!). The book is built around the five principles of rhetoric.

Here’s how to think about them:

• Invention: discovery of arguments and material for your speech

• Arrangement: organisation and structure of your speech

• Style: choosing the appropriate and effective language and rhetorical figures

• Memory: techniques for memorising the content

• Delivery: effective presentation of the speech, including voice control, gestures and facial expressions

Sounds familiar? With the exception of memory, these principles align almost surprisingly with the design process we use in our work. We first explore data and available content, then decide how to structure it, style it and finally communicate it. I've written about process on The Plot multiple times (in this and this article, for example), and even though we've grouped the steps for the studio, the core idea remains unchanged.

If you're reading this and already follow a similar process, you might not be impressed. I get it. What struck me when re-exploring Ad Herennium recently wasn't the structure itself but its suggested implementation. The author encourages practitioners to follow three techniques to develop their skills.

For us, that would mean:

• Theory: understanding key principles and rules of data storytelling, providing the necessary foundation

• Imitation: studying and emulating exemplary designers and storytellers

• Practice: deliberate, consistent practice where theory and imitation become internalised

Do you recognise yourself in this? I surely see my own learning journey here. When I first started in data visualisation, I consumed dozens of books, articles and webinars. I also spent countless hours imitating others' work—not to publish as my own, of course, but to learn specific design techniques. And then I practised, practised, practised... and still do.

This framework works brilliantly not just for beginners but for all of us learning new skills (which never end in our line of work!). I’m currently exploring motion design, which requires studying a bit of theory and best practices, looking at examples and finally trying things on my own. And even with topics we've mastered, it's sometimes valuable to return from practice to theory to imitation, transforming this framework from Ad Herennium into a continuous learning loop.

So, what are you currently learning and how are you switching between these three modes?

—Evelina

Probably only a handful of you can say you have a formal education in data visualisation—maybe even a bachelor's or master's degree. Most of us have taken a more winding path of study and work experiences that brought us to where we are today. I worked with data analytics, public speaking, nonprofits, and event management, before all these experiences led me to data storytelling.

At the very beginning of my visualisation journey, I was curious about everyone else’s paths. So I created a chart (very meta, I know!) about the diverse fields that people in our community come from. While it’s somewhat biased—based on published book authors—it gives you an idea of how many different disciplines can lead to dataviz.

As a community, we know that having multiple experiences is an asset, yet somehow we’re not always super proud of these non-linear journeys. When I interview designers for the studio, they often apologetically say, "I studied something else first before finding a passion for data visualisation."

What could make us feel better about it? Some evidence, of course.

I recently finished reading Range by David Epstein where the author provided just the proof we need. What Epstein argues, with substantial evidence, is that trying different things before specialising in one area can actually make you a better specialist. In Range, we read stories of people we admire with unexpected career paths:

• Roger Federer played quite a lot of different sports (including basketball, handball, soccer, badminton, and even skateboarding and swimming) before focusing on tennis

• Vincent van Gogh worked as an art dealer, book store clerk and even religious missionary before finding his calling as a painter relatively late in life

Crazy, isn’t it? So perhaps we shouldn’t aim for 10,000 hours learning the same thing—a concept popularised by Malcolm Gladwell in Outliers, I’m sure you’ve heard of it—but splitting those hours up a little. What if you spent the first few thousand hours exploring before you double down on one specialty?

It could look something like this:

1. First, branch out: try different fields and sub-fields until you find “the one”

2. Then, spend a significant amount of time studying and practising that craft

3. Once mastery achieved, branch out again to keep learning

If you've been reading this newsletter for a while, you know I've gone deep down the rabbit hole of learning everything there is to know about data visualisation—evidenced by my large collection of books. But once I felt I'd covered most aspects of the topic, I started exploring other creative fields, and encourage my team to do the same. We regularly sharpen skills that are slightly adjacent to our core expertise—UX design, animation, branding and more. There’s always something to learn under the umbrella of data storytelling!

When you return to your primary field after exploring elsewhere, you'll bring fresh perspectives that make your work even better. So do take that winding path and wear it proudly as a badge!

Thanks for reading 💨

See you in two weeks,

—Evelina

Do you often default to option 1? I hate to break it to you, but that might be shooting yourself in the foot. Let me explain why.

What’s a title really for?

The title is that big, bold chunk of text sitting at the top of your chart. It's often the first thing people see because it’s at the top of your visual hierarchy. So ask yourself: Do you want your audience's first reaction to be curiosity and engagement... or a yawn?

Looking at the examples above:

1. "Number of subscribers per day" - just describes what we're looking at (yawn)

2. "Report downloads have gone down by a third" - states the key takeaway (nice!)

3. "The Wild West" - creative but potentially difficult to pull off (tricky)

Turns out, option 2 is your best shot. But don’t take my word for it—let’s look at the research.

What the research says

The authors of the paper Beyond Memorability: Visualization Recognition and Recall arrived at a fascinating conclusion: titles that deliver key takeaways make visualisations much more memorable. Why? They create stronger mental connections that help viewers understand and remember what they're seeing.

In fact, when people were asked to recall graphics a moment after seeing them, titles were mentioned in nearly half of all descriptions. The difference between 1️⃣ City population and 2️⃣ 66% of Americans feel Romney performed better than Obama in debates as a title is massive. The second one gives your brain a clear framework for understanding the data.

This is exactly what Janine Kurnoff and Lee Lazarus talk about in Everyday Business Storytelling: we need to write headlines, not headings.

Strong headlines in practice

In a recent report we designed for Moloco, we used big, bold headlines that were impossible to miss on the page.

Sometimes they even connected multiple graphics together, creating a cohesive narrative.

So how can you write good titles yourself? Here are three tips to get you started.

Keep them short.

Short headlines are punchier because they're easier to process at a glance. So instead of 1️⃣ An analysis of the declining trend in monthly website visitors over the last quarter, try 2️⃣ Website traffic down 23% since January. If you cannot get away with such a short title, aim for no more than two lines.

Make them easy to digest.

When people can process information easily, they're more likely to engage with it and remember it. Use concrete language and simplify numbers and fractions, when possible. Instead of 1️⃣ Temporal analysis of user acquisition metrics shows subscriber counts are up by 37% in the morning, try 2️⃣ When do people subscribe? Mornings win by a more than a third.

Keep the audience wanting more.

The best headlines create a curiosity gap—they give enough information to orient the reader but leave something unresolved that compels them to keep reading. So perhaps you replace 1️⃣ Analysis of customer retention factors by 2️⃣ The surprising reason customers stay (it's not customer service).

Headlines all the way? Not so fast

Before you rush off to rewrite all your chart titles, there's something important to consider. In Frames and Slants in Titles of Visualizations on Controversial Topics, researchers showed that titles can significantly influence how people interpret data—often without them even realising it.

For example, with the exact same data about Syrian refugees, look at these two different but factually accurate titles:

• U.S. has accepted a lower percentage of Syrian refugees than the U.K., Spain, Finland, and Australia

• U.S. has accepted more Syrian refugees than the U.K., Italy, Russia, and Finland combined

Both titles present technically correct information from the exact same dataset, but they tell different stories due to the data points selected. And here's the surprising part—the subtle nudges often work better than obvious ones. The researchers found that gentle framing, like simply highlighting certain metrics or comparisons, can actually be more persuasive than strong statements because they feel more objective to readers.

So what’s the bottom line? Choose headlines that deliver meaningful takeaways for your audience—but choose them carefully and ethically. Your title isn't just describing data; it's shaping how people understand it.

Thanks for reading!

See you in two weeks,

—Evelina

P.S. If there are topics you’d like me to cover in the next months, reply to this email with your ideas 🚀

When most of us hear the word data, our minds immediately jump to spreadsheets, charts and numerical analysis. That's quantitative data—the stuff that dominates conversations about data-driven decision making. But what if the numbers only tell part of the story?

The mission of the organisation in question is to monitor and evaluate healthcare providers across the UK. For teams like theirs, qualitative information—patient experiences, testimonials, open-ended survey responses—provides crucial context that numbers alone can't capture.

So how do we harness the qualitative insights? And why should we bother visualising them?

Synthetise complex information

Let's face it—reading through paragraphs of text is mentally taxing. Our brains crave structure and patterns. That's where visualisation comes in, helping us digest complex information at a glance. By extracting key themes from text and transforming them into conceptual graphics, we make information more accessible. A simple Venn diagram can elegantly show how concepts interconnect, creating an immediate aha! moment that paragraphs of text might not achieve.

For more complex scenarios, you might use thematic mapping or sentiment analysis visualisations that preserve nuance while making patterns visible.

Or perhaps your graphic needs to be even more layered, like the heatmap below.

These are just some examples of how systemising qualitative data could help your reader understand it and introduce two layers of reading.

Put faces to numbers

Our brains aren't wired to connect deeply with abstract numbers. We need to feel the data to truly understand it. (I know, it’s not news for those reading this newsletter, but it’s worth repeating every once in a while!)

A simple yet effective technique is incorporating direct quotes alongside quantitative charts. Not to replace the numbers, that is, but to illustrate what they mean for real people.

Similarly, pairing data with images and videos of the people affected helps your audience grasp what's at stake. It’s one thing to hear about faraway natural disasters but quite another to feel what it means for families on the ground.

When you let the people behind the data talk, the audience will remember the numbers better. It may seem counterintuitive but that’s how it works.

Create context

The above examples focus on making qualitative data visual, but you can create even more impact when combining quantitative and qualitative information elements. Numbers provide facts, while text offers valuable context for interpretation.

Annotations are your secret weapon here. Like in this fun Axios visualisation, adding concise explanatory notes directly to a chart guides viewers to the key insights and provides explanations around them.

***

As I wrapped up the workshops, I found myself wondering: is the future of data storytelling inherently mixed-method? Will the most effective data communicators be those who skillfully blend quantitative precision with qualitative depth?

What do you think? Have you found creative ways to incorporate qualitative information into your data stories?

Thanks for reading!

See you in two weeks,

—Evelina

📚 If you’re interested in exploring qualitative data visualisation further—including origins and methods—check out Richard Brath’s book Visualising with Text.

Have our reading habits really changed?

Think of the last time you were in front of a long, detailed report. Did you analyse all the content cover to cover? Or did you find yourself drawn to the graphics and large, bright elements, aka the images? If you’re anything like me, it’s probably the latter.

And you know what? That’s perfectly fine. Normal, in fact. It’s how our visual perception works. Unconsciously, we instantly notice certain elements more than others. This is called preattentive processing: it occurs when some items are so different from their surroundings that they “pop-out” and capture our attention. This process can speed up understanding while minimising effort for the audience.

So how can you use this next time you’re crafting a report?

Structure your information in two distinct layers:

1. The full content: detailed text, thorough explanations and supporting annexes;

2. The visual narrative: highlights for readers who need to read quickly.

Let's talk about making that second layer memorable.

Power up with pullout text

Ask yourself: If my reader remembers just one sentence from this page, what should it be? Make that text big and bold.

Don’t snob the standalone numbers

Big, bold statistics are like billboards for your data—they grab attention and stick. And they’re also quick to design.

Spend some time on the graphics

Obvious, I know. But there’s one key consideration to keep in mind for your visuals to work well in this second layer: they need to be self-sufficient. Think of them as tiny, independent stories that convey their message through data and headlines, without the reader having to refer to paragraphs of text. See some inspiration below.

As you craft your next report (or any written product, for that matter), put yourself in your reader's shoes. How will they experience your content if they’re only quickly skimming? What visual assets will guide them to your key messages, just like in a kids book?

Thanks for reading!

See you in two weeks,

—Evelina

Did you know? I lead Parabole Studio—a communications agency specialised in data storytelling and information design. We could help you:

• Transform your data into compelling narratives (check out our portfolio for inspiration!);

• Upskill your team through hands-on training in data storytelling techniques.

Let's work together—reach out anytime :)

Back to (story) business

The new year has me deep in thought about what we do. Do we really tell stories with data? Is there a better way to describe our craft? How do we best teach others to master this unique blend of skills? While I'm still processing these questions (more on that soon!), I wanted to share something special that many of you have been asking for: A curated collection of must-read books on communication—both data-focused and beyond.

Get HD version

For years, I've been sharing what I call my map of data visualisation books—a carefully crafted reading list for anyone looking to master the techniques of the field and find inspiration. It's been surprisingly popular, but here's the catch: becoming great at what we do requires more than just understanding how to make good charts. That's why I've always had a parallel journey exploring the broader world of communication.

Communication might sound pretty broad, even vague. But isn't that exactly what we do? We're constantly finding effective ways to share insights that stick and inspire action. While we often call it storytelling, that term might not be totally appropriate—we need to master everything from structure to copywriting, from building empathy to seamlessly integrating data into the mix. So I've put together this collection of books, both technical and recreational, that will help you become a better data communicator. Get nerdy with it!

You talkin’ to me?

Before you go exploring that amazing list of books, let’s talk about one of my favourites: Sam Leith's You Talkin' to Me?. I've returned to this gem multiple times, and each read reveals new layers of understanding. While this book might be shelved under public speaking for most people, it's so much more—it's a fascinating journey through the evolution of rhetoric and a practical guide to using these timeless principles effectively.

What makes it special? Leith manages to blend deep expertise with genuine humour, making complex rhetorical concepts not just digestible but genuinely fun. Here's a taste of his style in the excerpt below, where he explains one of the oldest rhetorical frameworks.

Which book from the collection catches your eye first? Cannot wait to hear your picks!

See you in two weeks,

—Evelina 📚

As I think about the future of data communication, I’m curious what you’d like to read about the most. Let me know in the poll bellow 👇

When "show everything" is actually the brief

How do you visualise multiple metrics and dimensions at once? How can you show at least three different figures, grouped by region and country, and throw in the possibility to filter? Oh, and make it intuitive. I can see you wincing – we've all been there!

Here's how we cracked it: we created the interactive map above that breaks down the data into digestible bites:

1. You start by picking a country group

2. Get three key metrics in a side panel

3. Spot differences with the sneaky little slider showing revenue ranges

4. Hover over countries for a deep dive

5. Switch game genres with a dropdown

See how the map works in the GIF below or explore it yourself!

The "good news, bad news" dilemma

Another head-scratcher: how do you show three metrics where high numbers mean different things? For one variable, higher is better. For another, it's worse. The initial data looked like the heatmap below—awfully bright, isn’t it? So what did we make instead?

An interactive table that wears many hats:

• Tiny unit charts show the value at a glance

• Icons tell you if that's good or bad news

• Region view by default, but one click reveals all country details

• Bonus: sparkly gems (💎) to highlight high-potential markets

• And yes, you can filter by game genre:

There's more to explore in the report – from simple charts to the hero spinning globe animation. It's amazing what happens when you combine a creative team with a client who's up for trying new things. 🤩

2024 has been incredible. But if I'm honest, it's been a lot. As I look toward 2025, my goal is simple: do less, enjoy more.

Which brings me to a question: what should happen to The Plot? You might have noticed my bi-weekly schedule becoming inconsistent these past few months as life got busier and busier. So I'd love to hear your thoughts: should The Plot continue its journey in 2025, or has it told its story?

Let me know what you think below 😀

Until next time (whenever that may be 😉),

—Evelina

It’s a dataviz party.

Key moments in history are often pivotal for the advancement of the data visualisation field. Covid-19 pandemic was one of those moments. The U.S. presidential election—the most recent one. The sheer amount of graphics produced and viewed in one election day must break some sort of record. It’s also when new, unconventional visualisation techniques flourish. Graphics dress up a bit better for the occasion—just like you might when you go to a party.

What can we learn from them this year? Here are three dataviz techniques that caught my eye during this election period and that we should employ more often.

Key information only? Yes, please.

The audience needs all the information: how every state and county voted, how the count evolved over time, what’s the winning margin in each area, etc. Right? Not really. In the case of the election, we know in advance how some states are going to vote. This year, we were particularly interested in the swing states—the places that could “swing” to either Democratic or Republican candidates.

I love how some news outlets allowed the readers to focus on these battleground states only, like using a toggle in these NBC maps.

Or switching between two maps on the NZZ election coverage page, which also filters the bar chart for the total vote count at the top.

These are great examples of choosing the data points that are key to the story and allowing the audience to focus on them.

All maps are fair game.

If you ask a random person what kind of maps they know of, they’ll probably mention the choropleth maps and the point maps. While I don’t expect people to actually remember the term choropleth, most of them have seen that coloured map. Other, atypical map choices are often criticised for being strange and hard to read. One of those is a hex—or cartographic—map in which countries or states are not scaled based on their land size.

During the election period, these maps become fair game. That’s especially the case for the U.S. as their electoral system isn’t based on the popular vote which can be tricky to visualise. Many news outlets created electoral maps with shapes representing the number of electoral votes—see an example by Le Monde below.

Bloomberg even reproduced that pattern of units for their total vote bar chart at the top.

Other news outlets used size—not land size, but the number of electoral votes, to recreate the map. I quite like this example by NPR.

While we use maps all the time, they often represent a skewed view of the world. Perhaps these atypical election representations can pave the way for more creative mapping in the future.

Patterns over colour.

To separate multiple categories, colour is the best tool. Right? Well, maybe not. While we tend to default to colour to distinguish between elements, it’s not the only way. Perhaps it’s a good thing that for the U.S. election colour is known to represent specific parties, which means we cannot use it for anything else.

Enter patterns.

In this year’s chart parade, it was quite common to see the flip states highlighted using a hatched pattern. And I’m loving it.

The Economist also used a dotted line pattern to show where the results have changed since the last election.

Patterns can be a great way to encode additional data elements. If you’re too used to colour, you don’t need to get rid of it altogether—just try a pattern or two in addition.

What do you think of these examples? Which of these three techniques are you most likely to implement in your work? Let us know in the poll below.

See you next time 🗳️

—Evelina

Plan a data storytelling training for 2025

This year, I animated 20 trainings in 5 different countries. From public policy, to healthcare to second-hand goods—professionals in every industry need good data storytelling skills. If your team is interested in one of our trainings for next year, do reach out! I’m currently taking bookings for February 2025 and onwards.

But earlier this year, when our studio started taking on bigger communication projects, I finally caved. Turns out, AI tools are not that bad. Let me share my experiences with the big three AI assistants and how I actually use them in my work. No hype.

Which one?

What’s the difference among the panorama of AI assistants that you hear about? Good question, and I was wondering the same. After testing the main contenders, here are my two cents.

Claude (my favourite). Quick to understand, structured in its responses, and transparent about its process—it even shows you the code behind its generations. Bonus: they just added data analysis and visualisation features that I'm curious to try.

ChatGPT. Decent all-rounder. Great at multilingual conversations and can mirror your writing style for edits. However, it sometimes stumbles on seemingly simple tasks (like generating city coordinates—more on that in a minute).

Gemini. I had high hopes for it as it comes free with the Google enterprise account, but it's been... frustrating. It often provides vague answers and tons of unsolicited suggestions. Can’t say I recommend.

What for?

Now that we know how AI works, is it ready to replace data storytelling folks? I don’t think so. But it can be a good (and cheap) assistant for some of your daily tasks.

Copy editing (not writing). I don't let AI write anything for me from scratch—that usually results in vague, overly pompous content. Instead, I use it for:

• Tightening up existing text

• Finding alternative phrasings

• Brainstorming different tones

• Getting quick feedback on clarity

If you try that, always make sure to re-read the output. AI suggests, you decide.

Code debugging. When our D3.js developer was off last month, AI helped me make minor tweaks to an interactive dataviz project. But there's a catch: while it's great for spotting simple bugs and suggesting fixes, some problems still need a human touch. When you start going in circles with AI, it's probably time to call a real developer.

Basic data tasks. AI can speed up simple data generation and analysis. Example: for this recent project (launched just two days ago—will talk more about process soon!), we used it to generate random global city coordinates for a globe visualisation. Though we ended up fine-tuning the locations manually, it gave us a solid starting point. But it's not perfect: when we asked ChatGPT to remove coordinates that fell in oceans, it couldn't handle it. 🤷🏻‍♀️

So that’s my journey with AI so far—hope it answers some of your questions, if you’re still debating on how to use it. I’ll report back once I try Claude’s new dataviz functionalities!

Thank you for reading The Plot.

See you in two weeks,

—Evelina

P.S. Something I've noticed on the topic of AI: we all have different ways of talking to it. Some people go straight-to-business with commands (after all, it is a machine), while others—like me—throw in "good mornings" and "thank yous" as if chatting with a colleague. So I'm curious: are you team "execute task" or team "friendly chat"?

Learn data storytelling. In person.

I’m leading several training sessions before the year ends, but only one is open to the public. Join us in Utrecht this November for two days of data storytelling fun! With my online trainings on hold for now, this could be your last chance to attend a public workshop for a while.

Join workshop