The Five Types of People Who Use Visualization
The Five Types of People Who Use Visualization

The Five Types of People Who Use Visualization

Dan Gastineau
Sep 6 · 8 min read

Not all visualizations are created equal — some educate, some help to get a job done, and some simply amaze. The audience is clear on which one they want, but all too often they get a one-size-fits-all solution where some needs are met only some of the time. As my daughter’s preschool teacher liked to say, “You get what you get, and don’t pitch a fit.”

In the recent past, limitations in the tools and techniques of data visualization made this a reasonable approach. However, the proliferation of accessible tools and masters to learn from has dramatically decreased the time and cost of creating customized visualizations for a diverse set of users. Knowing this, users want and expect visuals that are tailored to who they are and what they’re trying to accomplish.

One approach could be to create a unique solution for each and every one of them so that all needs are met all of the time, but a world in which all those solutions have to be managed and maintained is not a world anyone wants to live in. The maximum usefulness of total customization is not worth the maximum effort required to pull it off.

A better approach is to consider how a visualization’s target users share important similarities with a larger group of user types, and then design for the most common needs of the group. This allows for a more reasonable balance between usefulness and effort where most needs are met most of the time.

Let’s take a look at each of these user groups to gain a better understanding of who they are and how visualization can help them.

The Doer

A doer is on the front lines of an organization where after the data is analyzed, the metrics are defined, and the strategy is set, all that’s left is for the doing to be done. Some doers know the difference between a mean and a median, but as with a quarterback who can recite all the lines from Othello, it’s not required.

An effective visualization for the doer is actionable and provides situational awareness. They don’t have time to ponder why’s or what if’s because the task awaits. Any metric put in front of her must answer the what, when, and where of a specific action, and do so succinctly. A visualization filled with the unnecessary context or multiple levels of info to dig through will not work for her.

A sales rep needs to know which leads to pursue; a store manager needs to know which products to stock; a political campaign worker needs to know which doors to knock on. Effective visualizations give them that and nothing else.

Visualizations for doers fall into two categories: those that help a doer while they’re doing, and those that help them decide what needs to be done next. The first needs to be understood with only a glance and alert the doer when immediate correction is required. Charts that work well emphasize a single number and give an obvious indication when attention is needed. Think single bars, gauges, or a big number all by itself.

In the second case, the doer has time to focus fully and wants to know what they should do next. More detail and context is okay, but the focus is still on a few metrics that are actionable and easy to understand. Charts like rank-ordered bars and symbol maps work well.

The Analyzer

The analyzer is never more comfortable than when in front of a spreadsheet full of numbers. They’re often skeptics who refuse any conclusion without seeing the underlying data himself. With analyzers, you must always show your work. But unlike the doer, the analyzer cares deeply about the why’s and what if’s.

An appropriate visualization for the analyzer allows them to explore detail and discover patterns. It is not preloaded with a story because it’s their job to figure out what the story is.

While providing the raw data will often suffice, visualization can enhance an analyzer’s work by helping him recognize patterns and iterate through scenarios more quickly. A good visualization for them reveals the lowest grains of data with high cardinality charts like scatterplots, or summarizes patterns with statistical charts like histograms and box plots. Flexibility to adjust dimensions and metrics is also a must so he can explore as he sees fit.

There are a number of effective tools and libraries that enable this combination of detail and exploration. Plotly and Jupyter Notebooks are great for those willing to code whereas Tableau, Qlik, and PowerBI are a good choice for the less technically inclined. Regardless of the tool, the goal is to enable quick creation of new visuals or deep exploration of preexisting ones so the analyzer can determine what story the data has to tell.

The Decider

A decider may not know how to navigate a database or why everyone keeps talking about the letter R, but with a wealth of experience and a firm grasp of the big picture, they know what to do with a well-crafted piece of analysis. While the analyzer reveals patterns and crafts stories from data, the decider has final say over which of those insights are most relevant to an organization. To make that determination they don’t need to get in the weeds but need the flexibility to ask clarifying questions. When it comes to visualization, the decider cares about forward-looking insight to determine the activities an organization should do as well as performance management, which helps them understand how well those activities are being done.

Visualizations focused on forward-looking insight can often be ad hoc in nature, narrow in scope, and story-based in structure. It begins with a specific question or hypothesis which leads to a well-defined set of analytics and ends with proof or disproof of the hypothesis. Unlike the more interactive visualization types that allow users to draw their own conclusions, this visualization unambiguously tells a story. A typical output is a presentation that walks through each step of the argument with several slides of highly narrated charts.

On the other hand, a visualization focused on performance management is routinely refreshed, broad in scope, and less story focused. It’s meant to give an ongoing status of how well the organization is executing against the predetermined strategy. An ideal implementation is an interactive dashboard that shows a few high-level performance metrics compared against a goal, draws attention to any significant variances, and guides the decider to drill down several levels of detail until a root cause is found. In other words, the decider has to draw their own conclusions, but the visualization gives them a strong nudge in that direction.

The Casual Learner

The casual learner does not have a problem to solve. They are curious about the world in general and occasionally willing to dive deeper into a specific topic. For the casual learner, a page full of words is a decent way to learn, but a page full of words and visuals is even better. It’s not that they need the visuals; it’s just that with limited expertise and time to devote, showing and telling can make the topic more approachable and engaging.

An intriguing visualization for the casual learner satisfies curiosity and makes surprising connections. Of all the types of visualization users, the casual learner is most engaged by the story format. Since journalists understand as much as anybody the power of combining show and tell, many of the big news publications have become a great source of visual content that is both enlightening and entertaining.

From left to right: “The Case For Stephen Curry, MVP” from , “The Rhythm of Food” from , “How does ‘Hamilton,’ the non stop, hip-hop Broadway sensation tap rap’s master rhymes to blur musical lines? from

The maturation of web-based libraries like D3.js has given rise to a fun combination of story-based journalism and data visualization pleasantly referred to as “scrollytelling.” Check out some of the great examples above from FiveThirtyEight, Google News Lab, and The Wall Street Journal¹, but first, make sure that you have several hours to spare.

The Museum Goer

The museum goer is open to learning new things, but more than anything, they want to be inspired. They understand the utilitarian nature of data does not mean it has to be boring. When put in the right hands, it can produce stunning beauty.

Visualizations for the museum-goer play a critical role in providing both inspiration and innovation. In these projects, an underpinning of data exists and they sometimes inform, but the visualizations themselves are the main attraction and they always amaze. In a similar vein, Alberto Cairo recently , “As we encouraged designers not to fear experimentation, and even make mistakes, they could push the boundaries of what is acceptable and orthodox in visualization.”

Amazing works of art / data visualization from , , and

While the artistry often takes center stage, that does not necessarily mean these projects lack in complexity and analytical expertise. , creator of some of the most stunning visualizations you’ll find, got her start in astronomy and data science. Her work, and others like it, can be much more complex than what would be advisable in most cases. Their unique ability to arrange complexity in a way that is beautiful and accessible entices viewers to explore and play with data. Their work elevates the importance of design and opens up new possibilities for what visualization can do. In a way, they’re the ultimate ambassadors — a few minutes scrolling through the examples will disabuse anyone of the notion that data visualization is boring!

Visualization has the unique ability to bring to life facts that would otherwise be ignored or misunderstood. Because of visualization’s growing importance in the analytics community, many invaluable resources have been created to define its best practices. However, it’s not uncommon for a visual to fully adhere to these practices yet fail to resonate with its audience. It might be the right visual, but it’s the wrong audience. Visualizations that work know who their audience is and what they’re trying to accomplish. Starting with a clear understanding of the intended user group is the foundation for any successful visualization.

Dan is the practice leader for Visual Analytics at Aspirent Consulting. He has over 15 years of experience in finance, business analytics, and visualization working with Fortune 500 companies such as The Home Depot, Coca-Cola, and Mattel.

I stand behind no one in my love for Hamilton, but I may love this piece about the lyrical style of Hamilton more than the play itself


The Journal of the Data Visualization Society

Thanks to Jason Forrest

Dan Gastineau

Written by

Data visualization specialist at Aspirent Consulting,


The Journal of the Data Visualization Society

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