The idea

Anyone who loves their job has probably wanted to share it with their kids—get them excited about it, show how cool and meaningful it is. Even if they don’t follow in our footsteps, maybe they’ll at least respect and appreciate what their parents are passionate about.

Sometimes it’s just a dream, but we want to find a bright and engaging way to talk to our children about what we do for work.

That’s how it was for us—Alex and Natalia—working in the field of data visualization. We really wanted to share our world! Data visualization is amazing: it’s full of beauty and logic, sleek designs, a variety of charts, fascinating topics, and the chance to work with important data.

Moreover, working with data and visualization is not just interesting—it’s useful! Especially in our fast-changing world. We wanted to give children valuable skills early on so they’re ready to face the grown-up world.

We want to create shared, precious memories: to capture that magical moment when a child is still curious enough to wonder, “What does Mom or Dad do at work?”

So we thought: let’s tell and show them!

With these thoughts in mind, we started exploring the idea.

Natalia already had experience creating data viz characters and telling stories about them, but now she wanted to make stories not for adults; but for kids. Still all about data visualization. Alex already had experience writing books!

And we wanted to bring this story to life as a book!

We agreed to start the project and went off to brainstorm, sketch, and imagine!

Characters and first sketches

What’s a book without characters? Natalia decided it’d be better not to make them diagram-like people, but cute monsters or creatures. This way, they’d be easier for kids to tell apart—and we’d avoid having a big crowd of kids running around the book (great for comics, but not ideal for a storybook).

Naturally, the prototype for the girl character was Natalia’s own daughter, Maya—a curly-haired girl with red pigtails who loves bunnies. Over time, the character changed—her hair, color, and age evolved, which is completely normal.

We decided to name the girl Christine!

Then came the pie chart character. In the data viz community, the pie chart is often viewed with skepticism due to its limitations and how easily it can be misused. It’s unfortunate, because people do love the bright, round pie chart—it’s just part of reality. The trick is learning to use it well.

Our first diagram character was a pie chart, and we called him Piechi. Since pie charts need careful handling, Natalia imagined Piechi as a kind of dog that needs to be trained—not to overeat!

Everyone who learned about the book instantly loved Piechi. He became the mascot of the story and our favorite character—just like pie charts: lovable, though not always easy to manage.

Later, we started developing the Dad character, bits of the plot, and other chart-characters.
We tried several versions of the Dad—he’s a tired, somewhat sad data professional. But (spoiler!) this is so he can become joyful again by the end of the story.

At this early stage, the other chart characters were still not fully formed. But we did keep some early sketches of them too.

The plot

So, you have an idea who this book is about—but what actually happens in it?

We decided to go with a plot as old as time: a girl travels into a mysterious world of data to rescue her father, who’s gone missing within it!

Alex worked on the twists and turns of the plot, inventing obstacles and adventures, vividly describing the challenges on Christine’s path to save her dad. He also dreamed up the mysterious chart characters who not only help Christine on her journey but teach her how to use each chart properly!

Each chart has its own personality and unique “diet.” They’ll share those secrets in the book, too!

Christine bravely journeys toward her goal—a mysterious Data Tower always shimmering on the distant horizon—accompanied by her loyal chart friends, overcoming tricky challenges to discover what happened to her father and to rescue him!

Illustrations

Of course, making a book isn’t easy. We started with the plot and text. We outlined the key story points and structure. Afterwhich, Natalia did a storyboard while Alex finished writing all the text. That’s how we finally understood the storyline, the placement and meaning of illustrations, and completed the manuscript.

Then came the time to draw!

Natalia can draw, but mainly in small formats. She didn’t have experience with book illustration, and creating book artwork takes a lot of time—especially while working and raising a small child. It became clear we wouldn’t finish the illustrations in a year… or even two. So we decided to look for help and find ourselves a wonderful illustrator!

This too was a challenge—we needed a style both authors liked, someone with experience in children’s books, available time, and ideally some familiarity with data visualization.

All the illustrators were incredibly talented, though we couldn’t work with everyone. But it was amazing to see different takes on our characters—Piechi in particular got a lot of interpretations!

We tried out a few spreads with different illustrators before finally choosing Anastasiya Lykova as our lead illustrator. She has a young child herself, so the story resonated with her—and we loved her soft and expressive illustration style.

We didn’t want the book to be just a story—we wanted it to be useful too. So we included a chart chooser, and pages with profiles on each chart-character at the end of the book.

What’s next?

To start telling the world about the book, we put together a website introducing the story and its characters—the charts! Now this website has grown into a full-fledged data project for kids: Data2Kids! It includes a children’s competition, educational materials, merch, and of course, this book.

We even want to bring together a local community of data-parents and try out this format all together!

And we wanted to create more opportunities for shared activities between children and parents.

We decided to make a little workbook for kids: with fun, simple data visualization tasks, drawing prompts, unusual challenges, and ways to spend time together collecting data and making charts. The workbook is currently in development, and we’re testing the first version with our local community!

With the book finally published and a growing local community of parents and children learning data visualization alongside the book’s characters, we’re excited to launch an international children’s data-visualization competition—Data Kids!

Dates will be announced soon—meanwhile, you can already explore examples of children’s data-viz projects and educational practices from our local contest and subscribe to the project’s newsletter! 

We’d be happy to see you there! And we really hope to run more data-visualization activities for kids this spring! We also decided to create a themed workbook where the book’s characters will help children practice creating and using charts.

If you’re interested in the Data2Kids project, and want to help introduce kids to the world of data and dataviz, check our book Christine and the Magic Charts!

Thanks for reading!

We hope that, like us, you want to pass on the magic of this unusual but fascinating profession to the next generation!

The post Christine and the Magic Charts: A Data Visualization Book for Kids appeared first on Nightingale.

Connecting the Dots is organized into three sections: “Our Data Selves,” “Networks Coming to Life,” and “Hitting the Big Time, Network Style.” After a short introduction, “Our Data Selves” eases the reader into the concept of individual datafication via online profiles. Janosov discusses both social media profiles as well as online shopping profiles, breaking down what kinds of data might be collected, how that data can be stored both statically and dynamically, and where technology may be collecting additional data about us and attaching it to our profiles, even if we don’t explicitly answer a question or survey. He concludes with the example of targeted coupon distribution and use, showing how data aggregated from many users over time can help companies predict consumer behavior.

“Networks Coming to Life” expands the reader’s purview to understand how interconnected profiles create a network. This section of the book is where Janosov’s unique approach to choosing examples shines. Unpacking everything from Game of Thrones character deaths to NFT art markets and even DJ popularity, Janosov explains the anatomy of networks, with their nodes and different types of links, and how networks are born, grow, and sometimes collapse. He engages the reader in discussions of rather heady scientific concepts, like weighted and directional relationships and preferential attachment, but keeps his writing accessible by using familiar topics as the backdrop.

Finally, “Hitting the Big Time, Network Style” brings the first two parts together and applies network theory to the real world. Instead of just showing where networks exist, Janosov demonstrates the utility of these mathematical concepts in the real world. He discusses how network theory can help predict the spread of disease, increase workplace productivity, engineer successful social media campaigns, and more. The final chapter also touches on a particularly timely subject: artificial intelligence. This chapter unpacks some of the inner workings of AI and is followed by a conclusion where Janosov ties all three parts together, leaving the reader with the feeling that they’ve tackled the challenge of the book, which will help them better understand more complex discussions of network theory.

When I first started reading Connecting the Dots, Janosov’s light and often joking voice immediately set this book apart from other network theory books and articles. His voice, paired with examples that I recognized, like Game of Thrones, electronic music, and oddly specific online ads, made me want to keep reading, even when I didn’t immediately recognize some of the more network theory-specific concepts. In addition to well-placed examples, Janosov’s organization is experimentation-forward. He often explains how he devised the idea for a project or analysis before unpacking what he actually did, which made me more invested in knowing what he found.

While Janosov provides ample links to view his network projects throughout the book, I was slightly disappointed to discover that none of the diagrams were printed on the page. That said, Janosov does not stop at just linking his own work, but often mentions further reading throughout the chapters and provides a comprehensive list of references by chapter at the end of the book, making Connecting the Dots a network of information in and of itself.

By the time I finished Connecting the Dots, I felt that my grasp of network theory was greatly improved. I would absolutely recommend this book for anyone curious about behavior prediction, datafication, or network theory. Connecting the Dots has a low barrier to entry and easily sheds light on what is often a very confusing topic.

Learn more about Connecting the Dots and preorder it on its website.

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Desiree Abbott’s Everyday Data Visualization beckons even the thoroughly Tableau-tested and Power BI-ified among us back to the exhilarating feeling of beginner’s mind. While the book is pitched as a comprehensive introduction for newcomers to the field, experienced practitioners will find unexpected depth in Abbott’s treatment of foundational topics. Her master’s degree in physics brings scientific rigor to subjects like color theory that often receive only superficial treatment in visualization texts.

Color theory worth your time

Chapter 4, “Choosing Colors,” exemplifies what sets this book apart. Abbott doesn’t just tell you to use sequential palettes for ordered data—she explains why, grounding her advice in the mathematics of color spaces and the computational logic of RGB values. Her explanation of hexadecimal color notation transformed what I’d always treated as rote memorization into genuine understanding. She walks readers through why 255 becomes FF in hex notation, connecting bytes, bits, and the fundamental constraints of computer displays to the practical work of choosing colors for a dashboard.

This depth extends to palette selection. Abbott distinguishes between continuous color ramps, stepped versions of continuous palettes, and categorical schemes with precision rarely found in practitioner-oriented texts. Her discussion of when to use divergent palettes—”for continuous data that’s about variation around a meaningful single value”—gave me new language for decisions I’d been making intuitively for years. 

Abbott’s lighthearted writing style keeps even technical material engaging. Her aside on the etymology of “uppercase” and “lowercase”—capital letters stored in the physically upper case of a printing press—exemplifies the “little rabbit holes” that propelled me through chapters I might have otherwise skimmed. She manages to make WCAG accessibility guidelines genuinely interesting, a feat I would not have thought possible.

Accessibility challenges that stick

The accessibility chapter challenged my practice in concrete ways. I hadn’t considered how the hover-based interactions I deploy constantly in both JavaScript and Tableau translate to nothing at all for keyboard-only users. Abbott’s treatment of this issue was neither preachy nor superficial—she provided actionable guidance while acknowledging real-world constraints. This balance characterizes her approach throughout: practical without being prescriptive, thorough without being pedantic.

Project management wisdom

The later chapters on project management offer hard-won wisdom on scope creep and stakeholder management. Abbott’s advice to “be specific nearly to the point of being pedantic when scoping the project” resonates with anyone who’s watched a two-week project balloon into two months (or six!). Her discussion of “too many cooks in the kitchen”—stakeholders who feed off each other’s displeasure and provide contradictory feedback—will strike a chord with consultants and in-house practitioners alike.

Particularly valuable is her advice on building visualizations for data that doesn’t yet exist. Rather than dismissing this as impossible, she provides concrete strategies: generate test data using the actual systems, use random data generators, or even prompt your favorite large language model with specific structural requirements. Her emphasis on “future-proofing” sparse data by leaving adequate space for categories to fill in later addresses a common but rarely discussed challenge.

What’s missing

For all its strengths, the book occasionally sacrifices depth for breadth. Part 1’s survey of visualization history and visual perception covers well-trodden ground without adding substantially to existing literature. Tool-specific guidance is intentionally minimal—Abbott frequently notes that implementation details “depend greatly on the tool you use”—which keeps the book from dating quickly but may frustrate readers seeking copy-and-paste solutions.

The book also assumes readers work primarily with traditional business intelligence tools rather than code-based approaches. Those of us migrating toward D3.js, Observable, or Svelte Plot will need to do our own translation work, though the fundamental principles Abbott articulates transfer readily to any medium.

The verdict

Everyday Data Visualization succeeds precisely because Abbott takes beginners seriously enough to teach them well. In doing so, she’s created a book that rewards careful reading from practitioners at any level. The beginner’s mind, after all, isn’t about knowing less—it’s about remaining open to learning more. Abbott’s book is an invitation to that openness, grounded in scientific rigor and leavened with genuine charm.

Whether you’re onboarding a junior analyst or simply seeking to shore up gaps in your own knowledge, this book deserves a place on your shelf.

Desiree Abbott’s Everyday Data Visualization is available from the publisher and other booksellers, including Amazon.

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Part I: Process contains the first eleven chapters of the book. The first three chapters are primers for the following eight, describing why dashboards are important (Chapter 1), the authors’ Dashboards that Deliver framework (Chapter 2), and the roles often found on teams that create dashboards (Chapter 3). Each of the eight other chapters in Part I take a deep dive into the seven steps of the Dashboards That Deliver framework: spark (Chapter 4), discovery (Chapters 5 and 6), prototyping (Chapters 7 and 8), development and user testing (Chapter 9), release (Chapter 10), adoption (Chapter 10), and maintenance and enhancements (Chapter 11). The list of steps may seem overwhelming at first, but the chapters are conversational and broken down into short, labeled sections, making them easy to skim and find relevant sections at a glance.

After the reader has taken in the information in Part I, the authors move into Part II: Scenarios. In this section, fifteen dashboard projects are broken down, each in its own chapter. Breakdowns not only identify the dashboard designer, organization, audience, tools used, and creation timeline for each dashboard, but also go into the fine details of each project. Annotated still images of the final dashboards and photos and scans of the sketches and prototypes that preceded the final product make these scenarios come to life. Even more useful, the authors provide commentary on the dashboard at the end of each chapter, noting key takeaways and broadly applicable insights from each specific scenario. They even used an example from Outlier 2024—Michael Gethers’ Professional Racing Team Race Strategy Dashboard (Chapter 24)!

The book concludes with Part III: Succeeding in the Real World. This section has the fewest chapters—nine in total—that each address a debated issue or broadly applicable insight in dashboard design. The authors address the promises and pitfalls of software defaults, the actual definition of a dashboard, generative AI, and much more. This section feels the most broadly applicable, even to those who may not create dashboards specifically, but who still work on data analysis and design issues.

While reading Dashboards That Deliver, I found myself skipping around to different sections as I became interested or found them relevant to my work. I was pleased to find that when I later settled in to read the book cover to cover, it read just as well continuously as it did when used as a reference book. The accessible language, paired with many sidebars, well-labeled sections, and relevant graphics, makes the book feel more like an active discussion than a textbook and adds to its utility as a frequently referenced book on any data visualizer’s shelf.

Though the four authors write with a unified voice throughout the book, Dashboards That Deliver still showcases each of their individual personalities and areas of expertise. Some sidebars, end notes, and even entire chapters are written by individual authors or small groups and some parts of the book specifically showcase when two or more authors had contrasting ideas. Cotgreave, Makulec, Shaffer, and Wexler engage with each other on the pages of Dashboards That Deliver instead of flattening their opinions, proving there is always more than one solution to a problem. Their tactic encourages readers to formulate their own opinions and gives them the agency to apply their critical thinking to their own work, equipped with the Dashboards That Deliver framework.

Dashboards That Deliver is undoubtedly worth the 500-page read. The authors bring their expertise to the table and lay their process bare for all to benefit. The book is approachable, useful as a reference, and applicable beyond just dashboard design, even though it uses dashboards as a jumping off point for understanding the data analysis and visualization process. Cotgreave, Makulec, Shaffer, and Wexler have delivered a book that will be used by many data visualizers for years to come.

Dashboards That Deliver is currently available for preorder and will publish on September 23, 2025.

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Stakeholder whispering by Bill Shander is an approachable book about why it’s important to solve the right problem, and how you can make sure that you’re doing it. Having worked with many designers over the years, I can say that stakeholder whispering is the hardest part of the job to get right, and often the most important one. The book offers simple, clear advice on how to make sure you’re getting to the bottom of a situation before diving in with solutions. 

It can be very hard to whisper well. Consequences for failure can be severe, but there aren’t a lot of books that focus on just this one aspect of working with a team. This book offers guidance from an expert whisperer on the small things that might trip a new designer up. Reading it is like shadowing a senior designer at work.

Bill brings the reader along at a level that’s gentle enough for a beginner but also valuable for an expert. Written with empathy and a sense of humor, the book feels like a comfortable conversation over tea with a friend, commiserating and sharing tips with someone who has had all of the same struggles and knows what it’s like. At different times, I found myself laughing out loud, grimacing in recognition, and nodding along. I appreciated how Bill used simple, practical examples to demonstrate his points (usually accompanied by a verbal wink, just to make sure we saw what he did there).

What does this have to do with data vis? Everything, really. Helping people push past “I want this chart” and get to a good outcome is a struggle we all face. This book is for anyone who needs to work with multiple stakeholders to help their projects succeed. (It might also be useful for stakeholders who need to work with designers, so that they can understand why we’re asking all these questions.)

Here are some of the topics addressed in the book.

Common painpoints:

• Pushing back without saying no
• Stakeholders who dictate solutions or don’t care about their stakeholders (especially the hidden ones)
• Knowing how & when to lose the battle
• Breaking a problem down into manageable chunks
• Switching roles as you moving from problem identification into the design process, and remaining flexible in your approach

What you will learn:

• Using neuroscience and cognitive behavioral therapy to understand stakeholder dynamics
• Keeping the focus on the problem, and not making it about you
• Empathy as a tool to enter the client’s frame of mind, without losing your own
• Creating a space for not-knowing: encouraging curiosity, even when people think they know what they need
• How to prepare for a conversation, and how to use what you hear
• The four components of productive listening: focus, attention, interruption-free, and picking up on nonverbal cues 
• Switching between the surface ask and deeper structure when solving a problem
• Listening for holistic understanding, and simplifying without oversimplifying
• Why finding the right problem might not be enough (and what to try next)
• What success looks like
• How to tell whether your stakeholders are open to whispering, and what to do when they’re not

These topics apply everywhere. I think these techniques might matter more for data vis for a few reasons:

• Stakeholders are less likely to understand the details (of the user task, or the solution)
• Other designers may not have the technical experience to follow along 
• Experts may be so frustrated by trying to explain the problem that they won’t even try. When you can use these techniques to demonstrate understanding, you get to the real conversation faster.

As with all experience, the magic happens in knowing how to dance, not in just following the steps. You need to develop a sense of rhythm and an instinct for where these principles apply. That said, experiment. Apply these techniques. They will help.

Question time with Bill!

I had a few questions after reading the book, so I reached out to Bill. He kindly answered them here, to share as part of the review:

This book was focused mainly on what I would call framing the problem: the needs identification step before you get into the design work. Can you talk about why you chose to focus on that part of the process?

The short answer is that I haven’t seen enough people write about or talk about this. It’s the part of the process that is mentioned but rarely explored in detail. In other words, designers (and others) are told they need to do “needs assessment” or “requirements gathering” and “ask questions”, etc. But to me, that’s like saying “make some beef stew” without providing a recipe. Because it’s not so simple. The recipe is the “how”. You need to ask the right questions, in the right way, of the right people, with the right tone, to really figure out what is called for. And that takes either years of hit and miss experience to figure out on your own or you can learn a process and a way of thinking about this that will get you up and running much more quickly. I wanted to provide that to people based on my experience. Oh, and by the way, a key part of all of this is to first just acknowledge the idea that our stakeholders often don’t know what they need. They need our help figuring it out. Once we acknowledge this, we can move on to the “how”.

What do you do when you’re stuck with a stakeholder who can’t be whispered?

As I say in the book, the short answer is that you should find new stakeholders. If your boss, or client, or whoever, won’t engage, then you should find a new boss/client/whoever. Honestly. Life is much more fulfilling when you’re working with people who respect you and engage with you as a thought partner. That being said, there are some techniques to help soften an intransigent stakeholder. For instance, start small. Just ask ONE key question, like “how will we measure success”, which is a very informative question to help you understand true needs pretty quickly. For instance, if your boss says “make a dashboard of our HR data”, but the measure of success is “employee retention goes up”, then you know retention is a key part of that HR data that needs to be the focus, and maybe it will lead to follow-up questions about how that data might help with retention, what other data might affect it, etc. Part of starting small is realizing you have to gain trust to engage with reticent stakeholders, so a short focused meeting with incisive questions will earn you longer and more complete conversations over time.

Designers are often very good at listening, but struggle when it’s time to transition from a position of understanding to become the expert presenting solutions. It can be hard to be seen as an expert when you’re in the role of listener and learner (especially working with an experienced team). Can you talk about ways to avoid this trap?

Expertise is an incredibly valuable thing. If you are new in your career, you may not be perceived as the expert, which makes things harder. But the great news is that you can lean on others’ expertise. Rather than saying to your stakeholders something like “pie charts suck!”, you can say, “we know from research on human visual perception that humans aren’t very good at distinct value comparisons when looking at circular shapes, so a pie chart won’t be as effective for this visual because you really want your audience to compare those two numbers – research also shows that a bar chart will be much more effective here, so I’d recommend that.” When you cite research, that glow of expertise will shine on you and you will gain trust. As you gain more and more trust, you will eventually be perceived as the expert and you will walk in the room with the gravitas and respect you need to engage effectively with any stakeholder!

Interruption free can sometimes be a problem for time management when talking to an expert. Can you share some techniques for using active listening to guide the conversation, as opposed to giving up control?

There is a fine line between active listening (really listening and hearing everything, without jumping constantly to your own thoughts and reactions and perceptions) and simply being someone’s audience, and they’re driving the entire conversation. The difference between the two is a true dialog where you are asking good follow-up questions based on what they’re saying. BUT, the key to doing this well is to NOT be perceived as just listening so you can jump in and respond, which is what most people do, right? (Listen, react…listen, react…) No, you need to truly listen, really hear what they’re saying. What they’re saying will trigger thoughts and reactions in you. Capture that if you need to. And respond with questions. But probably not every thought and question you have needs airing. What are the ones that you really need to address in the context of helping your stakeholder figure out what they really need? This is a gray area and something you can only learn over time and in your context, so this is something I can’t exactly teach, except to suggest you try to find that balance. Simply being reminded that there is a balance to be found will hopefully help you get there in time.

You discuss the importance of building a holistic understanding of the problem, and switching between superficial and deeper concerns. Can you talk about how to interpret what you hear, and how to process that interpretation with stakeholders?

One of the most important initial ideas in Stakeholder Whispering is to acknowledge that we live our lives driven largely by our subconscious. So in the context of work, that plays out in the automated response to all of our work. For instance, in today’s world, what do we do when we want to make “data-driven” decisions? We measure stuff, and then we make a dashboard out of it! This automated response isn’t bad, but it’s just so rote that we don’t always think it through. We need to measure stuff, but which stuff, and how much, for how long? And we need to understand that data, but is a dashboard the answer or might it just be a 5-minute call to review one key metric? It depends. So we have to probe deeper than the automated response. This applies to everything. So to the question, the “superficial” is the initial obvious concern/request/plan. And “deeper” review is literally the entire point of Stakeholder Whispering. Sometimes the superficial initial idea may be all that’s needed. But sometimes it isn’t. Whispering to figure that out is what it’s all about! The way to do it is to ask incisive questions, open your ears with your domain and data expertise, trust your gut about things that you know might be concerns or worth further exploration, and probe those. The book is full of specific techniques to do it, and it’s hard to explain without diving deep. But the short answer is simply to engage what I call “useful paranoia”. Something is always missing or not quite right, so probe it! But that doesn’t mean everything requires a deep rabbit hole. Explore thoughtfully, and know when you’ve done enough to move on to the next concern. This is also something you will develop over time, but hopefully the ideas I share in the book will speed up that process.

For a new researcher, it’s often hard to balance best practices from the quantitative social science research they might have learned in school and design research in a business setting. Concerns about deviating from script, “biasing” responses, etc. are common. To me, it’s always been a matter of incorporating those best practices into a more fluid dance of the conversation. Can you talk more about how you think about that balance?

I think that balance is actually inherent to the Whispering process. Because the way I recommend doing it (and I talk about this in the book) is like therapy. When you go into therapy, and you share your childhood trauma or relationship troubles (or whatever), your therapist doesn’t give you solutions or ask leading questions. They ask intentionally open-ended questions like “how does that make you feel?” The point of therapy is to help you understand what you’re feeling. That’s what Whispering (and research) is about. You ask unbiased questions to be sure your data is pure. Now, in Whispering (as in therapy), sometimes the questions will eventually start to lead the witness a bit. The therapist may eventually say “it seems like you’re getting angry…is that what you’re feeling?” because they are there to guide their patients to some degree, based on their expertise. And in a Whispering session, you may start to ask less open-ended questions as you get a sense of where things are going. You might start with something like “why do you think a dashboard is best for this project?” But later in the conversation, you might ask something like “do you think a report might be more effective since you mentioned that people will be reviewing this on a plane and only 2X per year…maybe a dashboard isn’t the best tool for the job?” It’s OK to get to this point because, as the therapist, using your expertise and experience and active listening, you can help guide your stakeholders to the best decision based on the conversation. You’re not conducting primary research, so the standard does shift a bit from those types of conversations, and that’s the “dance of the conversation”, as you describe it, that you need to get comfortable with.

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NR
So tell me, what’s your background? How did you get into data storytelling and become an expert?

ALD
I started in computer engineering; actually, I am a researcher and did my PhD on underwater acoustic networks and network security. This was enough for me to understand that this was not my field of research; what I wanted to do was to bring the research to others. I moved into network security, then to web applications, and to data science. But I understood that data science and data are not enough—you need to communicate the data.

I moved to data storytelling, specifically to communication. I started to work firstly at a theoretical level but then I looked deeper into data storytelling and realised that there is a real misunderstanding: storytelling is sometimes confused with enhancing data visualisation when storytelling should be bringing a story!

I began to study what a story is and I found many materials—a wide bibliography on this. I realised that to have a story you must have a plot and characters.

NR
That’s really interesting. When I asked about your background—and I love the fact that we have so many different backgrounds coming into our field of data and visualisation—I would never have guessed that. I thought maybe you’d come from literature, film, or cinematography, maybe even psychology, because there are so many of these different non-technical elements and subjects in the book. 

I felt it was quite an exciting read because, in a way, it made you feel almost like you need to be more of a film director than a storyteller. I love the way that you included all these elements.

ALD
I studied a lot—many books on films and cinematography, editing—and I tried to bring all this precious work done in other fields to the field of data. This was what I tried to do in the book.

NR
When it comes to data storytelling, I always find that some people consider it a real buzzword and, consequently, a lot of people like it while some people don’t. You’ve already mentioned the importance of separating the ideas a little bit from data visualisation, but do you ever get resistance to your work, or to the term data storytelling?

ALD
Yes, I think sometimes data storytelling is unfortunately not well received. I did an exam with some students just half an hour ago. They did a fantastic job on data science and machine learning, but they didn’t bring data storytelling into their project. The problem is that people, especially specifically technical people, think that doing the job is enough, but the greatest part is communicating what you do—it’s the same for a book. When you write a book, the minor part is writing a book. The most important thing is to make that your book is known; that’s the most difficult part.

NR
Yes, you’re right, I can certainly relate to that. It does feel like a transition, doesn’t it? People strive to be technical first. You learn the technical skills, and it can be almost later in your career that you learn the importance of storytelling. I know at my company that the leadership training is actually branded as ‘Leadership and Storytelling’ because it’s felt that it’s important to bring that element of storytelling in in order to be a good communicator, to be a leader. I think that’s why I find it really interesting that the data storytelling is so important, but it can be a hard sell depending your narrative. 

That’s why I really enjoyed the book because I felt there were so many different layers to it—everything that I read, you could relate to one of your own experiences or relate to a data story that you had to tell, and how it related to all the different kind of scenarios and examples in the book.

A couple of elements I loved: firstly that you say how important it is to have a hero and that if there’s no hero, or if you can’t find what the hero wants, then there’s no story, and that’s the situation we have sometimes! But, I’ve never heard the idea of having a sidekick in a data story, I love that idea! Tell me, where did that thought come from?

ALD
Thank you! Yes, I studied a lot—the structure of stories in cinema and novels—because I also like writing fiction. I took the different characters in common stories and I tried to map them. To search for the different characters in the story and map them to who they could be in a data-driven story. My book is also a research book because it doesn’t explain something that is already existing, but it takes something new. Maybe it’s not well received yet because the market is not ready. 

The book is theoretical and gives many examples that we could implement. The idea, from my background as a professor of data journalism at the University of Pisa, is to combine movies and data to make something realistic, but not fiction. You don’t create fiction; you create the stories directly extracted from data with the different characters. You could have the sidekick, you could also add the minions for the antagonist which I talk about in passing at some point in the book. You can add any characters, as many as you want, because you have the ability to create a story with the antagonist, the hero, the sidekick, the minions, and so on. It’s a new idea!

NR
I found there were new ideas in there for me, along with some of the some of the regular ideas, all backed up with more theory and more research than I’d seen before. And that’s what I really enjoyed throughout the book.

One question I wanted to ask you is how you have found the reaction across different cultures? I think of the Italians as great storytellers—we think of all the great Italian great film directors. Do you find that that your lectures and workshops go really well among your culture more so than you might do with, for example, some of us less expressive Brits, let’s say, who might be less open to storytelling?

ALD
At the moment I am a researcher, so I attend conferences where I talk about this topic and I see that few people are interested in this general topic of data storytelling, maybe because I attend more technical conferences. But the people who are interested are very responsive to this topic. They participate in workshops, they ask a lot of questions, and it’s very promising, but at the moment I think that the topic is not as well received as it should be.

NR
I suppose it goes back to what you said before about more technical people. Perhaps the Venn diagram depicting the overlap of people who are interested in storytelling is quite small, but at least they are seeking you out and being enthusiastic. I suppose they’re a small group of people representing a much larger group of technical people. I think that’s what we’re both fighting against!

I want to talk about the audience for a data story. There were a few things which surprised me in a good way, which I hadn’t really thought of before. 

With your data, you can tell your data story; previously I’d thought that you might need different stories for different audiences. But you explain that you have to tell your data story in a different way depending on your audience. So, your story is still the same, but the manner, or the way you tell that story, has to be very different to different audiences. I wonder if you can just expand on that because that’s not a way of framing it that I’d come across before.

ALD
Yes, because now what I’m also seeing on the web is that you have to think about your audience from the beginning. So, you think about your audience, and you plan your story. But if at a certain point you want to change your audience, you need to completely redesign your story. Instead, I think that the message that the data brings is always the same, independent of your audience. The facts, the reality you extract from the data, are always the same. And for this you create a story.

Next, you adapt this message to your channel, or your audience. But the message is always the same. If you have to tell your story to a kid, you use simple words. If you have to tell your story to executives or very technical people, you add more details; but the message, the core that your data brings, is always the same.

NR
Yes, it was a shift for me. I always considered that you had to write a story depending on your audience, but you don’t! Your data determines the story, you curate the narrative, and it then becomes about how you tell it and how you how you deliver it. 

That was one of my key takeaways. 

I think you gave an example where your feedback was you “woke up the audience”. I love it when you just get a three or four-word piece of feedback that really sums up a positive “oh wow” reaction. I guess that’s something that we don’t always succeed in doing!

ALD
Two months ago, I tailored my data story to a specific audience of people. We have a project for the Jewish community in Pisa where I live. I tailored the story to them because they were also all Jewish people, and during my presentation, they cried. I was able to adapt the message to them and I talked about their problems.

NR
That instance was all about empathy in your presentation, one of the things you emphasise is important in terms of reaching your audience.

Now, I have a question about AI, which you mentioned right at the end of your book. This feels like a really wide, general question, but do you think there is a whole other book to write, or a whole different way of thinking about data storytelling when it comes to AI?

ALD
*produces a book from her desk, as if by magic!*
This is my book about data storytelling and AI.

NR
That’s amazing, I had no idea. That almost looks like I set you up for that question, but I didn’t. I promise you! OK, well that answers that question. So how would you summarize that book if people are interested in data storytelling too from an AI perspective?

ALD
I think AI is something that has started and it’s something that we can’t stop, so we must find a possible, responsible way to combine AI and data storytelling before AI is used in a “wild” way. 

We must do some research, and we must combine data, storytelling, and AI in a responsible way. Otherwise, AI takes its way and the field becomes very wild. We as humans can’t lose the control—we have to determine how to manage this combination. We must do something, otherwise it’s a problem because AI manipulation can lead to a wild world. There are many problems: invented stories, invented data, bias, hallucinations, and so on. 

We must do something! My research is specifically in this field to give some directions on how to combine AI and data storytelling because AI can’t communicate things alone. We must define how AI can be used to communicate data properly—we can’t escape!

NR
No, I couldn’t agree more. And it’s fascinating because when I think of parts of my professional role right now, which combines data fluency and AI fluency, I’m trying to encourage data storytelling, but also encourage fluency in in all the technology that we have available to our clients, which includes AI. There are going to be some people now who are new to data storytelling and maybe a little bit reticent, who will think, ‘OK, if I’ve got to do this, I’m going to get AI to do it. I’ll put it in ChatGPT.’

In many ways, I think there are certain people for whom AI will accelerate moving into data storytelling and obviously that has various considerations that we have to think about carefully. AI should be something that could be really good assistance for these people. But yes, I do think we need to make sure that people have all the awareness and understanding that they need in order to use AI responsibly and ethically, and to get the most benefits. I am fascinated to see what is in your book because it does have a real relevance right now. It’s amazing that what can be just a footnote in 2023 or 2024 can be huge here and now, with everybody using it in 2025!

ALD
The book is a technical book, so maybe you can skip the technical parts, but you can appreciate the general parts which go further into what I mentioned just now.

NR
I guess I’ve reached the end of my questions. Thank you for talking about your book!

ALD
Thank you.

You can purchase Become a Great Data Storyteller from the publisher’s site, Amazon, or wherever you like to buy your books.

The post REVIEW: Become a Great Data Storyteller by Angelica Lo Duca appeared first on Nightingale.

We follow a really useful structure—once the concept of data literacy is defined as the ability to read, work with, analyse, and communicate with data, these strands are discussed in more detail with examples throughout a typical organisation. How does your R&D team need to read data? Or your Executive team? Do your IT team need to be able to work with data? Your Sales and/or Marketing teams? (spoiler alert—yes, of course!). Does your Product team, or your Exec team need to be able to analyse data? And how might your Data Science team, or your Finance team, need to communicate with data? Chances are, at least one of these examples will hit close to home in terms of your own corporate setup and needs to work together with greater levels of data literacy.

We learn the three C’s of data literacy: curiosity, creativity and critical thinking. These concepts resonate strongly with me—as a practitioner of data visualisation (often far removed from the business situation) my own ethos also revolves around these categories. Indeed, I publicise the themes of my own book with a talk entitled “How Curiosity leads to Creativity”. Whatever the circumstance, we always learn, appreciate and understand more with a curious and creative mindset. And these three Cs lead to a crucial fourth C—data culture. Every chapter and concept introduced implies an improvement in company data culture, and that is so often the holy grail we’re seeking! 

So, given the likely audience of this review, and the background of the review’s author, how does data visualisation come into play, does it have a role in data literacy? In citing two of the most iconic visualisations to have stood the test of time in our field, Jordan considers John Snow’s 1854 Broad Street cholera outbreak map and Charles Joseph Minard’s 1869 visualisation of Napoleon’s March on Russia. The key word used in respect of data visualisation is simplification with data visualisation defined as a simplified approach to studying data.

The comparison between a well presented dataviz and a table of 100,000 rows and 50 columns of data is marked indeed! And Minard’s visual allows us to simplify the details of Napoleon’s march. Snow’s visual allowed the local community to reach a simple conclusion—gathering water from the same spot by the same people was a key reason for the proliferation of the cholera outbreak. By halting this practice, a community was able to halt an outbreak of cholera and prevent further disease. From these iconic examples we conclude that simplification is the key to presenting data that encourages curiosity. Then we’re on the track of the three Cs!

And what of AI? The book’s revised version addresses the emergence of ChatGPT since late 2022. The clear takeaway is that every concept of data and data literacy has parallels with AI and AI literacy. AI can help us with every level of data analytics (descriptive, diagnostic, predictive and prescriptive), so enhancing your, or your organisation’s AI literacy reaches to all facets of data and data literacy. The data literacy parallel is that, in the same way that we don’t all need to know how to code, it behoves us all to have data literacy skills, so we don’t all need to know how to code AI, we all benefit from having the skills to utilise AI efficiently. The book offers confirmation, if it were needed, that anyone implementing data fluency or data literacy initiatives should now do so in a way that’s unbreakably linked to AI literacy, and offers simple steps to help us on the way.

The book’s subtitle is “The data literacy skills everyone needs to succeed”—it’s a book that explains and enhances my belief that not only is it important for me as a data professional, but important for everyone who comes across or interacts with data (and now AI) in their lives. In other words, everyone!

You can purchase Be Data Literate (Second Edition) from the publisher’s site, Amazon, or wherever you like to buy your books.

The post REVIEW: Be Data Literate (Second Edition) by Jordan Morrow appeared first on Nightingale.

Lang kicks things off with the basics in Chapter 1. Terms like p-values, significance levels, and hypothesis testing are all laid out clearly, ending with an informative chi-square test case study. If that sounds daunting, don’t worry, Lang has your back. “Many analysts and Tableau developers struggle to implement statistics into their analysis or data visualizations,” he acknowledges, and this book is his way of helping bridge that gap.

One thing I love about this book is how visually intuitive it is. Chapter 2 introduces Tableau’s analytics pane with plenty of crisp, full-color screenshots to guide you along. It’s like having a patient teacher pointing at the screen saying, “Click here. Now try this.” These visuals continue as Lang dives into topics like distributions, histograms, and anomaly detection. I even learned a cool trick about using color-coded conditional formatting to highlight anomalies—game-changer!

Lang’s writing is approachable and engaging, but he doesn’t shy away from depth. His explanations of z-scores, standard deviations, and regressions (linear, polynomial, and multiple) are refreshingly clear. Even if you’ve been using Tableau for years, there’s a good chance you’ll discover some hidden gems here. And don’t worry about coding. Lang keeps the calculated fields simple and straightforward, showing just a few lines of code to demystify concepts.

The later chapters delve into advanced integrations with R and Python, offering detailed guidance on setting up RStudio, Rserve, and Anaconda to connect with Tableau. These chapters are packed with visuals to simplify the setup process. By the final chapters, readers are seamlessly tackling multiple linear regression using external tools integrated into Tableau. That said, these chapters might not appeal to everyone, especially if you’re not interested in coding or external integrations. If R and Python aren’t your focus, rest assured—the earlier chapters offer plenty of value on their own.

One of the biggest takeaways for me was how Lang frames Tableau as more than just a pretty chart maker. “Tableau is not simply a data visualization tool, but a company with a suite of tools to support data visualization and analytics at an enterprise level,” he writes. This perspective makes the book feel relevant not just for individual analysts but for entire teams looking to elevate their insights.

Would I recommend Statistical Tableau? Absolutely. Whether you’re brushing up on statistics, unlocking Tableau’s hidden features, or diving into advanced integrations, Lang’s engaging and thorough style ensures you’ll come away with new skills and insights. Just be prepared to pause and try out his tips as you read— you’re bound to learn something new!

You can purchase Statistical Tableau: How to Use Statistical Models and Decision Science in Tableau on Amazon.

The post Review: Statistical Tableau: How to Use Statistical Models and Decision Science in Tableau by Ethan Lang appeared first on Nightingale.

Another aspect that stands out in the book is its practical intention. There are journal assignments at the end of each chapter designed to help you grow as a data professional. The assignments contain prompts that encourage you to reflect and critically engage with the topics presented in the chapter. They also contain more actionable suggestions, such as networking with folks inside and outside your company. The assignments also help you find “meaning” in your work with regards to supporting others and the organization itself.

Personally, I learned about a lot of these concepts—sales cycle, churn, RFP, what various roles do—on the job, so this book was better purposed as a review. However, this book could be helpful for data professionals with no business background or are newer to the business side of things. Through the definitions and ample examples, it can help data professionals understand the common language used in business and also guide them in understanding their importance within your organization.

Data professionals at an intermediate level might still benefit from the practical exercises in the journal assignments and examples on how they can work with different functions. For example, the book encourages you to brainstorm “one new way you can help the organization with monetization through data,” in Chapter 5, or to “find what your next presentation will be, and find two ways you can get to know your audience for that presentation, better” in Chapter 9.

Individual chapter highlights include the four “rights” of data introduced in Chapter 1, which helps you focus on business objectives.

1. the RIGHT data
2. at the RIGHT time
3. for the RIGHT objective
4. with the RIGHT data literacy

Chapter 3 went into detail about various departments and roles within a company, and featured specific examples of how data professionals can support each function. I also liked that Chapter 7 focused on various issues that might occur, such as bias and lack of buy-in. Lastly, I really enjoyed reading the interviews with business and data professionals in Chapter 10. It was interesting seeing the commonalities and slight differences in responses to the same set of questions. 

This book can be purchased on Amazon, or through Kogan Page.

The post Review | Business 101 for the Data Professional by Jordan Morrow appeared first on Nightingale.

Alex Kolokolov and Maxim Zelensky say that there are three types of vizzer—business analyst, infographic creator, and data journalist. Are you clear about which you are? They are. And the ideal reader will be an analyst, for this is a complete guide to Power BI. 

Never made a dashboard? I hadn’t. But should I want to work anywhere other than journalism, I need to know how. The 2023 DVS State of the Data Viz Industry Survey revealed that, combined, Tableau, Power BI and Excel are the most used tools in the data visualization community.

As the ideal guinea pig for this manual on BI, I set out to recreate the graphics. I parked my hesitation around business phrases like “key performance indicators,” downloaded the data from GitHub, and went for it.

My challenge: could I create stuff without having to ask Google for backup? Straight up, I’ll say that it can be done just by following the first part of the book. There are plenty of pictures—very helpful for navigating menus. Despite the familiar Microsoft layout, it’s always helpful to see a big red circle around the thing you need.

Health warning! While I created charts that looked like those in the book, the numbers did not come out the same. The authors have reassured me that this is due to the raw data pre-dating the final copy. 

The book is a dipper—regardless of whether your chart choice is classic, trusted or ‘risky’. Maps and bubbles were covered early, and their inclusion was a nice surprise.

The writing and tone are friendly and not too technical. My heart sank slightly when I found the examples based around sales, but that’s OK; we’re doing minimalist business graphics, after all.

I was showered with advice on customising defaults, which you’ll want to consider. BI’s auto-generated titles are unashamedly dull. And in the time-poor field of business, it is so true that ‘everything should be clear at first glance.’

The biggest concept I took away is that ‘dashboards are not just tools for data discovery; they are also for facilitating communication among people.’ It’s a good reminder for anyone who is normally design-led.

For those who hate Excel charts, back yourself with this book and give BI a try.

The book is available in various formats on the publisher’s website and Amazon.

The post Review: Data Visualization with Microsoft Power BI: How to Design Savvy Dashboards appeared first on Nightingale.

•  X is the Marketing Manager of a company. He wants to use his company’s data to identify what is working and what is not. He wants the company to be data-driven. He hires a bunch of analysts and buys the latest tools in the market. After some months, he feels even after hiring bright data professionals and giving them the latest tools, the results are not as expected or the results are delayed. What could be the cause?

•  Y has four years of experience as an analyst. Y has had fellow analysts, seniors, managers in his team to guide and support him during his tenure. Y then moves to another company where he is the first data hire. Y is told that he would be guided initially until he picks up and later would have to build a data team from scratch. Y is confident about doing tasks that are given to him but he is unsure if he can build a data team on his own. Would he make the correct decisions? What if decisions do not work out as expected?

•  Z is the Chief Data Officer of his company which he calls data-driven. The data team uses data for their analyses, another team says they don’t think data could help them do their jobs better, and a third team says they use data but upon probing during a presentation, the team has no data to back their claims. Is Z’s company really data-driven? If not, what can Z do to build the data culture in his organization?

Data Culture by Dr. Shorful Islam provides solutions for the questions mentioned above and more. Overall, the book serves as an excellent guide and a detailed roadmap to those who need to build a culture of data with step-by-step guidance on what is to be done and how.

What is data culture? Why is it necessary to establish a good data culture within an organization? How and where do you start towards building the data culture in your organization? What would be the first step? Dr. Shorful describes all of this in his book and gives plenty of examples from his vast experience to demonstrate what works and what does not. 

In Dr. Shorful’s own words, “A company should integrate data into all aspects of the business”. All their conclusions or findings need to be evidence-based. Usage of data should not be limited to a few functions or teams in a company. In other words, how the company uses their data should define ‘who they are’ and not ‘what they are’. 

Every organization generates data. In order to manage that data and derive useful conclusions out of it, the organization needs to take necessary steps and use resources effectively. This book is an informative and interesting read for any organization that wants to understand how to see their data.

You can purchase Data Culture by Dr. Shorful Islam on Amazon or through Kogan Page

The post Book Review – Data Culture by Dr. Shorful Islam appeared first on Nightingale.

The story follows Daphne, who uses everyday examples to explain key ideas like algorithms and statistics. She explores how polar bears dive deeply or which spaceships can fly home by breaking data into colors. The book also encourages hands-on learning through activities such as creating charts or drawing data representations, helping kids build problem-solving skills while having fun. Daphne also learns that she can turn this data into brilliant stories and that throughout all of her adventures, she was gaining so much more than learning the data visualization process—she was learning who she was.

“Then she realized she was making friends by being herself.”
— Cole Nussbaumer Knaflic

Daphne Draws Data by Cole Nussbaumer Knaflic offers a delightful introduction to the world of data visualization, artistry, math, and data science for young readers. The book successfully breaks down complex concepts into playful ideas, encouraging children to view math and data through the lens of creativity.

A Fun, Visual Approach to Learning

Daphne Draws Data excels at blending art with data. Its colorful illustrations not only captivate children’s attention but also serve as powerful teaching tools. Through these visuals, kids learn how data can be transformed into pictures, charts, and stories. The book makes numbers and patterns accessible and fun, sparking curiosity in even the youngest readers. This dynamic approach helps children grasp essential math and science concepts through creativity.

Simple Explanations of Big Ideas

Rather than overwhelming young readers with jargon, Daphne Draws Data introduces key topics like data visualization, algorithms, and statistics through relatable and fun examples. The explanations are clear, concise, and designed to inspire curiosity and further exploration.

Hands-On Learning and Imagination

Interactive activities, such as creating charts to visualize how many books a child has read or drawing pictures to represent different types of data, give kids the opportunity to apply their new knowledge. These exercises are more than just fun—they reinforce critical ideas and help build problem-solving skills. By blending learning with play, the book provides an engaging and hands-on approach that deepens understanding. 

Encouraging Critical Thinking

Another strength of Daphne Draws Data is its ability to promote critical thinking. Throughout the book, the author poses questions that challenge children to think more deeply about data and its uses. This active engagement empowers young readers to take charge of their learning journey, rather than passively absorbing information.

Potential for Development

Overall, Daphne Draws Data is an excellent resource for introducing children to data visualization, math, and art, providing an enjoyable and educational experience by simplifying complex concepts through creativity and play. With a few additional resources for continued learning, this book has the potential to become an even more valuable tool for budding data scientists, artists, and problem-solvers alike. To support long-term engagement and deeper exploration, enhancements such as a companion deck of challenge cards or a workbook could enrich the experience, offering a range of activities at varying skill levels that encourage children to deepen their understanding of data and math. Also, an interactive journal or sketchbook where readers can document their experiments and progress would solidify the lessons learned and provide an opportunity for reflection and growth throughout their learning journey. 

To End

Daphne Draws Data by Cole Nussbaumer Knaflic offers a delightful introduction to the world of data visualization, artistry, math, and data science for young readers. The book successfully breaks down complex concepts into playful ideas, encouraging children to view math and data through the lens of creativity. There is so much more here than just numbers, graphs, and data—this is a true journey and adventure through not only Daphne’s life, but the life of a child discovering who they really are through the joys of discovering data visualization. 

You can purchase Daphne Draws Data by Cole Nussbaumer Knaflic on Amazon or on the official website.

The post Review: Daphne Draws Data appeared first on Nightingale.