Data Thinking Defined

Thinking like a data scientist can transform the way organizations develop products, services, processes, people, and strategy. This approach, which is known as data thinking, brings together human creativity, knowledge, and imagination with what is technically and quantitatively feasible. It also allows people who aren’t trained as data scientists to use data science tools and techniques to address a vast range of challenges.

About this site

I’m often asked to share what I know about data thinking. I’ve developed this website in response to that request. Here, I introduce data thinking, how it came to be, how it is being used, and steps and tools for mastering it. Because I invented data thinking (with Megan ;)), there are no resources or perspectives from others (yet). But, I do share resources for particular components where relevant. Everything on this site is free for you to use and share with proper attribution.

Data thinking in context

We live and work in a world filled with data, where many of the problems we face are dynamic, multifaceted, and inherently complex. Think of some of the biggest questions being asked by businesses, government, educational, and social organizations: How will we navigate the disruptive forces of the day, including AI and algorithmic-driven life? How will we grow and improve in response to unprecedented change? How can we use data to support individuals while simultaneously changing big systems? For me, data thinking offers an approach for addressing these and other big questions.

There’s no single definition of data thinking. It’s an idea, a strategy, a method, and a way of seeing the world. It will eventually grow beyond the confines of any individual, person, or website. For me, data thinking is a way to solve problems through blending creativity, imagination, and subject matter expertise with the core tools of data science. It isn’t a fail-safe approach; nor is it the only approach. But based on the impact I’ve already seen in my work, the relevance (and urgency) of data thinking has never been greater.

Data thinking today

Data thinking is about to explode upon the world! It’s moving from a nascent idea that “people really ought to be more empowered to work with and learn from data directly” to an established one, where as many people as possible are inspired and equipped to make much-needed discoveries around the world. With this, inevitably, will come interest and critique. People will debate its definition, pedigree, and value. As a leading and committed practitioner and founder of data thinking, I have a stake in this conversation — and a responsibility to contextualize its value in the present moment and, importantly, the future.

I’ve learned a lot over the years, and I’d like to share my experiences. I’ve seen data thinking transform lives and organizations, and on occasions I’ve seen it fall short when approached superficially, or without a solid foundation of study. Data thinking takes practice; and as a scientist, artist, consultant, researcher, thought leader (LOL), and more, I’ve followed the journey to mastery, and developed maps that can guide others.

Data scientist’s mindset

I’m a data scientist who shares a mindset with other data scientists. Data science is a new, inherently interdisciplinary, and rapidly changing field that brings together people from fields as varied as engineering, computer science, psychology, linguistics, political science, sociology, mathematics, statistics, ecology, and more. I developed data thinking as a way of explaining data science’s applications and utility so that others can practice it, too. Data thinking uses creative activities to foster collaboration and ask and answer questions in data-centered ways. We adopt a curiosity-first mindset with the intent to make discoveries, to assume nothing, and to see uncertainty as a strength rather than a weakness.

To think like a data scientist requires asking big questions, dreaming up wild ideas, trusting our instincts and knowledge, and also joyfully welcoming evidence that we might be wrong. The data scientist’s mindset embraces bravery, optimism, iteration, creativity, and uncertainty. And most critically, data thinking keeps thoughtful reflection at the center of every process. A thoughtful data scientist knows that as long as you stay focused on the reason you’re conducting your research — and are willing to listen to the results — you can arrive at true discoveries that really move the world.

Anyone can approach the world like a data scientist. But to unlock greater potential and learn how to work as a dynamic discoverer, scientific confidence is key. For me, scientific confidence is the idea that everyone is a scientist, and that science is not the ability to prepare slides for a microscope, spot constellations in the sky, or smash particles together; it’s a way of understanding the world.