A Zen Buddhist teacher, Suzuki Roshi, famously said, “In the beginner’s mind there are many possibilities, but in the expert’s there are few.” The implication is that expertise can inadvertently short-circuit creativity and curiosity. The quote rings true for me, resonant with my own occasional surprise at the success of someone’s seemingly off-the-wall data visualization project. Fortunately for us battle-weary data practitioners, the notion of beginner’s mind can be applied to a day as well as a career. I periodically delight in my renewed ability to reframe a problem in an unexpected way in the light of a morning following a satisfying sleep.
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.
Nicole Mark
Nicole Mark is a data visualization engineer and consultant based in southern Delaware. She’s equally passionate about both the art and science of data visualization. She developed and maintains theU.S. Breed-Specific Legislation Database Project, and she’s working on her master’s degree in data science at the University of Colorado Boulder. In her downtime, Nicole enjoys reading, yoga, and hanging out with her pitbull, Potato.
