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ANALYTICS

Visualizations That Really Work by Scott Berinato

FROM THE JUNE 2016 ISSUE

Not long ago, the ability to create smart data visualizations, or dataviz, was a nice-to-haveskill. For the most part, it benefited design- and data-minded managers who made adeliberate decision to invest in acquiring it. That’s changed. Now visual communication is a must-have skill for all managers, because more and more often, it’s the only way to make sense

of the work they do.

Data is the primary force behind this shift. Decision making increasingly relies on data, which comes

at us with such overwhelming velocity, and in such volume, that we can’t comprehend it without

some layer of abstraction, such as a visual one. A typical example: At Boeing the managers of the

Osprey program need to improve the efficiency of the aircraft’s takeoffs and landings. But each time

the Osprey gets off the ground or touches back down, its sensors create a terabyte of data. Ten

takeoffs and landings produce as much data as is held in the Library of Congress. Without

visualization, detecting the inefficiencies hidden in the patterns and anomalies of that data would

be an impossible slog.

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But even information that’s not statistical demands visual expression. Complex systems—business

process workflows, for example, or the way customers move through a store—are hard to

understand, much less fix, if you can’t first see them.

Thanks to the internet and a growing number of affordable tools, translating information into visuals

is now easy (and cheap) for everyone, regardless of data skills or design skills. This is largely a

positive development. One drawback, though, is that it reinforces the impulse to “click and viz”

without first thinking about your purpose and goals. Convenient is a tempting replacement for good,

but it will lead to charts that are merely adequate or, worse, ineffective. Automatically converting

spreadsheet cells into a chart only visualizes pieces of a spreadsheet; it doesn’t capture an idea. As

the presentation expert Nancy Duarte puts it, “Don’t project the idea that you’re showing a chart.

Project the idea that you’re showing a reflection of human activity, of things people did to make a

line go up and down. It’s not ‘Here are our Q3 financial results,’ it’s ‘Here’s where we missed our

targets.’”

Managers who want to get better at making charts often start by learning rules. When should I use a

bar chart? How many colors are too many? Where should the key go? Do I have to start my y-axis at

zero? Visual grammar is important and useful—but knowing it doesn’t guarantee that you’ll make

good charts. To start with chart-making rules is to forgo strategy for execution; it’s to pack for a trip

without knowing where you’re going.

Your visual communication will prove far more successful if you begin by acknowledging that it is

not a lone action but, rather, several activities, each of which requires distinct types of planning,

resources, and skills. The typology I offer here was created as a reaction to my making the very

mistake I just described: The book from which this article is adapted started out as something like a

rule book. But after exploring the history of visualization, the exciting state of visualization

research, and smart ideas from experts and pioneers, I reconsidered the project. We didn’t need

another rule book; we needed a way to think about the increasingly crucial discipline of visual

communication as a whole.

The typology described in this article is simple. By answering just two questions, you can set

yourself up to succeed.

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CONCEPTUAL FOCUS: Ideas GOALS: Simplify, teach (“Here’s how our organization is structured.”)

DATA-DRIVEN FOCUS: Statistics GOALS: Inform, enlighten (“Here are our revenues for the past two years.”)

The Two Questions

To start thinking visually, consider the nature and purpose of your visualization:

Is the information conceptual or data-driven?

Am I declaring something or exploring

something?

If you know the answers to these questions,

you can plan what resources and tools you’ll

need and begin to discern what type of

visualization will help you achieve your goals

most effectively.

The first question is the simpler of the two, and the answer is usually obvious. Either you’re

visualizing qualitative information or you’re plotting quantitative information: ideas or statistics.

But notice that the question is about the information itself, not the forms you might ultimately use

to show it. For example, the classic Gartner Hype Cycle uses a traditionally data-driven form—a line

chart—but no actual data. It’s a concept.

DECLARATIVE FOCUS: Documenting, designing GOALS: Afrm (“Here is our budget by department.”)

EXPLORATORY FOCUS: Prototyping, iterating, interacting, automating GOALS: Conrm (“Let’s see if marketing investments contributed to rising prots.”) and discover (“What would we see if we visualized customer purchases by gender, location, and purchase amount in real time?”)

If the first question identifies what you have, the second elicits what you’re doing: either

communicating information (declarative) or trying to figure something out (exploratory).

Managers most often work with declarative

visualizations, which make a statement,

usually to an audience in a formal setting. If

you have a spreadsheet workbook full of sales

data and you’re using it to show quarterly

sales in a presentation, your purpose is

declarative.

But let’s say your boss wants to understand

why the sales team’s performance has lagged

lately. You suspect that seasonal cycles have

caused the dip, but you’re not sure. Now your

purpose is exploratory, and you’ll use the

same data to create visuals that will confirm

or refute your hypothesis. The audience is

usually yourself or a small team. If your hypothesis is confirmed, you may well show your boss a

declarative visualization, saying, “Here’s what’s happening to sales.”

Exploratory visualizations are actually of two kinds. In the example above, you were testing a

hypothesis. But suppose you don’t have an idea about why performance is lagging—you don’t know

what you’re looking for. You want to mine your workbook to see what patterns, trends, and

anomalies emerge. What will you see, for example, when you measure sales performance in relation

to the size of the region a salesperson manages? What happens if you compare seasonal trends in

various geographies? How does weather affect sales? Such data brainstorming can deliver fresh

insights. Big strategic questions—Why are revenues falling? Where can we find efficiencies? How do

customers interact with us?—can benefit from a discovery-focused exploratory visualization.

The Four Types

The nature and purpose questions combine in a classic 2×2 to define four types of visual

communication: idea illustration, idea generation, visual discovery, and everyday dataviz.

IDEA ILLUSTRATION

INFO TYPE: Process, framework

TYPICAL SETTING: Presentations, teaching

PRIMARY SKILLS: Design, editing

GOALS: Learning, simplifying, explaining

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Idea Illustration. We might call this quadrant the “consultants’ corner.”

Consultants can’t resist process

diagrams, cycle diagrams, and the

like. At their best, idea illustrations clarify complex

ideas by drawing on our ability to understand

metaphors (trees, bridges) and simple design

conventions (circles, hierarchies). Org charts and

decision trees are classic examples of idea

illustration. So is the 2×2 that frames this article.

Idea illustration demands clear and simple design, but its reliance on metaphor invites unnecessary

adornment. Because the discipline and boundaries of data sets aren’t built in to idea illustration,

they must be imposed. The focus should be on clear communication, structure, and the logic of the

ideas. The most useful skills here are similar to what a text editor brings to a manuscript—the ability

to pare things down to their essence. Some design skills will be useful too, whether they’re your own

or hired.

Suppose a company engages consultants to

help its R&D group find inspiration in other

industries. The consultants use a technique

called the pyramid search—a way to get

information from experts in other fields close

to your own, who point you to the top experts

in their fields, who point you to experts in still

other fields, who then help you find the

experts in those fields, and so on.

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It’s actually tricky to explain, so the consultants may use visualization to help. How does a pyramid

search work? It looks something like this:

The axes use conventions that we can grasp immediately: industries plotted near to far and

expertise mapped low to high. The pyramid shape itself shows the relative rarity of top experts

compared with lower-level ones. Words in the title—“climbing” and “pyramids”—help us grasp the

idea quickly. Finally, the designer didn’t succumb to a temptation to decorate: The pyramids aren’t

literal, three-dimensional, sandstone-colored objects.

Too often, idea illustration doesn’t go that well, and you end up with something like this:

IDEA GENERATION

INFO TYPE: Complex, undened

TYPICAL SETTING: Working session, brainstorming

PRIMARY SKILLS: Team-building, facilitation

GOALS: Problem solving, discovery, innovation

Here the color gradient, the drop shadows, and the 3-D pyramids distract us from the idea. The

arrows don’t actually demonstrate how a pyramid search works. And experts and top experts are

placed on the same plane instead of at different heights to convey relative status.

Idea Generation. Managers may not think of visualization as a tool to support idea generation, but they use it to brainstorm all the time—on whiteboards, on butcher

paper, or, classically, on the back of a napkin. Like idea illustration, idea generation

relies on conceptual metaphors, but it takes place in more-informal settings, such as

off-sites, strategy sessions, and early-phase innovation projects. It’s used to find new ways of seeing

how the business works and to answer complex managerial challenges: restructuring an

organization, coming up with a new business process, codifying a system for making decisions.

Although idea generation can be done alone,

it benefits from collaboration and borrows

from design thinking—gathering as many

diverse points of view and visual approaches

as possible before homing in on one and

refining it. Jon Kolko, the founder and

director of the Austin Center for Design and

the author of Well-Designed: How to Use

Empathy to Create Products People Love, fills

the whiteboard walls of his office with

conceptual, exploratory visualizations. “It’s

our go-to method for thinking through

complexity,” he says. “Sketching is this effort to work through ambiguity and muddiness and come

to crispness.” Managers who are good at leading teams, facilitating brainstorming sessions, and

encouraging and then capturing creative thinking will do well in this quadrant. Design skills and

editing are less important here, and sometimes counterproductive. When you’re seeking

breakthroughs, editing is the opposite of what you need, and you should think in rapid sketches;

refined designs will just slow you down.

Suppose a marketing team is holding an off-site. The team members need to come up with a way to

show executives their proposed strategy for going upmarket. An hourlong whiteboard session yields

several approaches and ideas (none of which are erased) for presenting the strategy. Ultimately, one

approach gains purchase with the team, which thinks it best captures the key point: Get fewer

customers to spend much more. The whiteboard looks something like this:

Of course, visuals that emerge from idea generation often lead to more formally designed and

presented idea illustrations.

Visual Discovery. This is the most complicated quadrant, because in truth it holds two categories. Recall that we originally separated exploratory purposes into two

kinds: testing a hypothesis and mining for patterns, trends, and anomalies. The

former is focused, whereas the latter is more flexible. The bigger and more complex

VISUAL DISCOVERY

INFO TYPE: Big data, complex, dynamic

TYPICAL SETTING: Working sessions, testing, analysis

PRIMARY SKILLS: Business intelligence, programming, paired analysis

GOALS: Trend spotting, sense making, deep analysis

the data, and the less you know going in, the more

open-ended the work.

Visual confirmation. You’re answering one of two

questions with this kind of project: Is what I suspect

actually true? or What are some other ways of

depicting this idea?

The scope of the data tends to be manageable, and

the chart types you’re likely to use are common—

although when trying to depict things in new ways,

you may venture into some less-common

types. Confirmation usually doesn’t happen in

a formal setting; it’s the work you do to find

the charts you want to create for

presentations. That means your time will shift

away from design and toward prototyping

that allows you to rapidly iterate on the

dataviz. Some skill at manipulating

spreadsheets and knowledge of programs or

sites that enable swift prototyping are useful

here.

Suppose a marketing manager believes that at certain times of the day more customers shop his site

on mobile devices than on desktops, but his marketing programs aren’t designed to take advantage

of that. He loads some data into an online tool (called Datawrapper) to see if he’s right (1 above).

He can’t yet confirm or refute his hypothesis. He can’t tell much of anything, but he’s prototyping

and using a tool that makes it easy to try different views into the data. He works fast; design is not a

concern. He tries a line chart instead of a bar chart (2).

Now he’s seeing something, but working with three variables still doesn’t quite get at the mobile-

versus-desktop view he wants, so he tries again with two variables (3). Each time he iterates, he

evaluates whether he can confirm his original hypothesis: At certain times of day more customers

are shopping on mobile devices than on desktops.

On the fourth try he zooms in and confirms his hypothesis (4).

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New software tools mean this type of visualization is easier than ever before: They’re making data

analysts of us all.

Visual exploration. Open-ended data-driven visualizations tend to be the province of data scientists

and business intelligence analysts, although new tools have begun to engage general managers in

visual exploration. It’s exciting to try, because it often produces insights that can’t be gleaned any

other way.

Because we don’t know what we’re looking for, these visuals tend to plot data more inclusively. In

extreme cases, this kind of project may combine multiple data sets or load dynamic, real-time data

into a system that updates automatically. Statistical modeling benefits from visual exploration.

Exploration also lends itself to interactivity:

Managers can adjust parameters, inject new data

sources, and continually revisualize. Complex

data sometimes also suits specialized and unusual

visualization, such as force-directed diagrams that

show how networks cluster, or topographical

plots.

Function trumps form here: Analytical, programming, data management, and business intelligence

skills are more crucial than the ability to create presentable charts. Not surprisingly, this half of the

quadrant is where managers are most likely to call in experts to help set up systems to wrangle data

and create visualizations that fit their analytic goals.

Anmol Garg, a data scientist at Tesla Motors, has used visual exploration to tap into the vast amount

of sensor data the company’s cars produce. Garg created an interactive chart that shows the pressure

in a car’s tires over time. In true exploratory form, he and his team first created the visualizations

and then found a variety of uses for them: to see whether tires are properly inflated when a car

leaves the factory, how often customers reinflate them, and how long customers take to respond to a

low-pressure alert; to find leak rates; and to do some predictive modeling on when tires are likely to

go flat. The pressure of all four tires is visualized on a scatter plot, which, however inscrutable to a

general audience, is clear to its intended audience.

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EVERYDAY DATAVIZ

INFO TYPE: Simple, low volume

TYPICAL SETTING: Formal, presentations

PRIMARY SKILLS: Design, storytelling

GOALS: Afrming, setting context

Garg was exploring data to find insights that could be gleaned only through visuals. “We’re dealing

with terabytes of data all the time,” he says. “You can’t find anything looking at spreadsheets and

querying databases. It has to be visual.” For presentations to the executive team, Garg translates

these exploration sessions into the kinds of simpler charts discussed below. “Management loves

seeing visualizations,” he says.

Everyday Dataviz. Whereas data scientists do most of the work on visual exploration, managers do most of the work on everyday visualizations. This quadrant

comprises the basic charts and graphs you normally paste from a spreadsheet into a

presentation. They are usually simple—line charts, bar charts, pies, and scatter plots.

“Simple” is the key. Ideally, the visualization

will communicate a single message, charting

only a few variables. And the goal is

straightforward: affirming and setting

context. Simplicity is primarily a design

challenge, so design skills are important.

Clarity and consistency make these charts

most effective in the setting where they’re

typically used: a formal presentation. In a

presentation, time is constrained. A poorly designed chart will waste that time by provoking

questions that require the presenter to interpret information that’s meant to be obvious. If an

everyday dataviz can’t speak for itself, it has failed—just like a joke whose punch line has to be

explained.

That’s not to say that declarative charts shouldn’t generate discussion. But the discussion should be

about the idea in the chart, not the chart itself.

Suppose an HR VP will be presenting to the rest of the executive committee about the company’s

health care costs. She wants to convey that the growth of these costs has slowed significantly,

creating an opportunity to invest in additional health care services.

The VP has read an online report about this trend that includes a link to some government data. She

downloads the data and clicks on the line chart option in Excel. She has her viz in a few seconds. But

because this is for a presentation, she asks a designer colleague to add detail from the data set to

give a more comprehensive view.

This is a well-designed, accurate chart, but it’s probably not the right one. The executive committee

doesn’t need two decades’ worth of historical context to discuss the company’s strategy for

employee benefits investments. The point the VP wants to make is that cost increases have slowed

over the past few years. Is that clearly communicated here?

In general, when it takes more than a few seconds to digest the data in a chart, the chart will work

better on paper or on a personal-device screen, for someone who’s not expected to listen to a

presentation while trying to take in so much information. For example, health care policy makers

might benefit from seeing this chart in advance of a hearing at which they’ll discuss these long-term

trends.

Our VP needs something cleaner for her context. She could make her point as simply as this:

Simplicity like this takes some discipline—and courage—to achieve. The impulse is to include

everything you know. Busy charts communicate the idea that you’ve been just that—busy. “Look at

all the data I have and the work I’ve done,” they seem to say. But that’s not the VP’s goal. She wants

to persuade her colleagues to invest in new programs. With this chart, she won’t have to utter a word

for the executive team to understand the trend. She has clearly established a foundation for her

recommendations.

In some ways, “data visualization” is a terrible term. It seems to reduce the construction of good

charts to a mechanical procedure. It evokes the tools and methodology required to create rather

than the creation itself. It’s like calling Moby-Dick a “word sequentialization” or The Starry Night a

“pigment distribution.”

It also reflects an ongoing obsession in the dataviz world with process over outcomes. Visualization

is merely a process. What we actually do when we make a good chart is get at some truth and move

people to feel it—to see what couldn’t be seen before. To change minds. To cause action.

Some basic common grammar will improve our ability to communicate visually. But good outcomes

require a broader understanding and a strategic approach—which the typology described here is

meant to help you develop.

A version of this article appeared in the June 2016 issue (pp.92–100) of Harvard Business Review.

Scott Berinato is a senior editor at Harvard Business Review and the author of Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations.

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