REVIEW: Become a Great Data Storyteller by Angelica Lo Duca

I (NR) had a great discussion with Angelica Lo Duca (ALD) where we discussed her latest book: Become a Great Data Storyteller. Angelica’s book is a great addition to the field of data storytelling resources which takes influence from fiction writing and cinematography, introducing characters, heroes, and sidekicks into data storytelling. Here’s our conversation below:

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.