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Data is the coin of the digital information and internet worlds. He or she who controls data controls the world. Having that power is the goal of every mogul, politician and entrepreneur—and it is not difficult to obtain. Every time a transaction takes place, data is involved. Giorgia Lupi, a highly original data visualizer and partner at Pentagram, is on the crest of data presentation. Together with co-author Phillip Cox, her new book addresses data as more than mere figures on a screen and dots on a graph. SPEAK DATA: Artists, Scientists, Thinkers and Dreamers on How We Live Our Lives in Numbers is the first pop nonfiction book to explore the subjcet in its multiple dialects, through conversations with 17 business, tech, medicine, psychology, health and art leaders. These interviews go beyond the visual to the conceptual, to address data’s “powerful ability to reveal patterns, tell stories, stir emotion and illuminate complexity.”

Below, Lupi answers my questions about the present and future of data delivery, storage and, most importantly, fluency in a world where not having a grasp of the right data at the right time can be utterly dangerous.

I must admit I was fooled by the literal nature of your title and cover image. Before cracking open the book I imagined you and your co-author would be addressing data visualization. Instead you do as the title suggests—talk about data with those who are fluent in its meaning. How did this concept come into being? This book is certainly informed by more than a decade of work in data visualization, but we wanted the scope to be broader. We wanted to move beyond the narrow view of data visualization as merely a design discipline, and instead explore data as a language in and of itself—how it’s used, felt, questioned and even resisted across disciplines. We see data as something that touches every aspect of life. So we reached out to people who inspire us—artists, scientists, writers, technologists, all at the top of their field—to ask them how data shows up in their work and thinking. *Speak Data *is really a collective reflection on what data has become in our culture more than merely a manual on how to visualize it.

Each one of your interviewees answered the prompt, “Data is (or are)”. Which three or four responses do you believe are the most in sync with the way you use data? Hard to choose! James Clear’s idea that data is “recorded feedback” certainly resonated with us. The idea that data is always recorded, that it’s a natural phenomenon understood and tracked through human-made measurement, is key. On the flip side, a major theme of the book is how we can use data to tell stories and unlock emotional responses. We like what the writer and activist Naresh Ramchandani said: Data is “irrefutable points of drama in a real story.” We don’t often think about data this way, but it’s so true. Finally, the artist Ekene Ijeoma’s idea that data is “another way of seeing” also feels important to the media-rich times we live in today. Like photography, data can offer an exceedingly rich depiction of events or histories.

You write in your intro that “democracy is as good as its data.” This is in relation to a brief passage on the U.S. census. Why is this true? It’s a trite phrase, but it’s true: You don’t count if you’re not counted. What we choose to measure and collect data on informs what (and who) we as a society see, value, understand. The U.S. census example shows this. Conceptually, it’s meant to measure everyone equally, and it has major political and economic implications. It’s a foundational tool of our democracy. But how do we know if the census is accurate? And how do we even define “accuracy”? If the data is incomplete or biased, so is our democracy. In the end counting is an act of care—and power—and with power comes a massive responsibility to get it right. Our interview with Andy Marra, CEO of Advocates for Trans Equality, really drove this home. She explained to us the importance of data collection for trans people but really all marginalized groups, groups that have historically been ignored or “erased” by policymakers. We see this erasure still happening today with the latest news around gender markers on U.S. passports. That doesn’t mean that visibility translates to acceptance, but it’s still an important first step.

What does it mean that data is a mass of verifiable numbers that have no information in them … yet? We believe that numbers on their own are empty. They’re actually meaningless. They don’t tell us anything until we interpret them. Data becomes information only when it’s connected to context, story and human judgment. That’s really the thread running through the whole book—without that layer, data is what Seth Godin describes as just “a mass of verifiable numbers that have no information in them … yet.”

You note in an interview that there is nothing good or bad about abstract visualization, yet it is divorced from the everyday reality of people. Do we as people need data to be crystal clear? How important is clarity? Clarity matters, but not at the expense of complexity. We feel that too often we flatten complexity and reduce it, all in service to a sort of rote information efficiency. What’s problematic is when data is simplified or aggregated so much that it stops reflecting reality. The goal isn’t to make data “clear” in the sense of easy, but to make complexity more understandable and accessible to more people. Sometimes uncertainty or ambiguity are parts of the truth, and they need to be rendered as such. We say the same thing about what we call “missing data”—the data that’s not collected. If we don’t acknowledge the gaps and omissions in our data visualization somehow, we’re not really reflecting reality’s fullness.

Do you believe that data is not necessarily truth? Absolutely. Data is not truth. Our definition is that data is an abstraction of reality. Said another way, it’s a lens through which we can view the world. It can help us approach truth, but it’s always filtered by who collected it, why, and how it’s framed. Data is a human artifact, and because of that it carries human intentions and biases.

Can data be used as a weapon? And, if so, how do we defend ourselves? Yes … and it already is. Data can be twisted to manipulate perception, justify policies or hide injustice. The defense is critical thinking: knowing how data is produced, questioning its sources, being aware of what’s not being measured and being able to read and correctly interpret its communication. Some people call this data literacy but we call it data “fluency.”

How are the world’s “most pressing topics” best served by data collection and visualization? Conversely, how is this misused? When used with care, data can illuminate patterns we could not see otherwise, for example in current crises of health, climate, inequality. But when misused or manipulated, it can distort human experience into misleading categories and empty predictions. Often the difference between “good” and “bad” lies in intention: Are we using data to understand and communicate as a start of a conversation, or are we using it as an ultimate, unimpeachable truth that can’t be argued with and ultimately leads to control? The book’s conversations, from science to AI to art, are all about navigating that line.

You are invested, as are some of your interviewees, in data addressing healthcare. How does this manifest? And since you wrote this book before Robert Kennedy Jr. took over as health secretary, how has intuition and anecdote impacted existing data? There are no easy answers here, but we were inspired by our interview with Bon Ku, a physician now working as a design researcher, and Eric Topol, a cardiologist who has written prolifically about the role of AI in healthcare’s future. Both explain to us that data on its own is not enough. Quantification must be synthesized with qualitative feedback: real human-to-human contact and experience. We can’t afford to have one without the other. If we only have quantification, we lose the essential human element. But if we only have qualitative information—things like anecdotes or hunches—we can be untethered from empirical knowledge. This also opens the door even wider to human bias and personal agendas, as we’ve seen in current events.

Will AI have a positive, negative or neutral impact on how data is collected and used? AI will amplify whatever values we feed it. It can democratize access to knowledge and help us see hidden connections, but it can also reproduce biases at scale. We don’t believe that the danger is not AI itself, but instead our uncritical trust in it. What we need are ethical frameworks, and more diverse voices guiding how AI systems interpret and act on data.

Can data and technology combine to change attitudes, knowledge and policy for the better? Of course! Data has been at the heart of almost every scientific or technological discovery in the modern era. However, the case we make in the book is that, as the velocity of these discoveries increase in the age of AI (and they will), it will be even more important to hold fast to first principles. We can’t forget that we’re in a moment of profound transition. We liked what Paola Antonelli told us: “I think that every time there’s a new technology, there’s a period of testing it. It’s a sort of drunkenness. You see a lot of crap and you see a few diamonds. Then, slowly but surely, you sober up and start seeing the long aftereffects.”

**What do you hope the reader of Speaking Data will take away from this book? ** That data is not just a technical field but a language we all already speak, even if unconsciously. Our hope is that readers start to see data not as cold or distant and scary, but as a lens they can use. If the book inspires people to question what data means in their own lives and to use it more thoughtfully, then that’s enough.