30 Great Visualization Resources in 30 Days

A lot of the applications that Juice creates are designed to make information more accessible to people who wouldn’t consider themselves to be data experts. They realize the value in the data that they have, and in many cases they have some sort of analytics solution in place, but they know they’re not getting as much value from their data as they should. One of the hurdles we frequently come up against is that people who aren’t actively participating in the visualization discussion don’t know what’s possible. All they’ve ever seen, in many cases, are the confusing dashboards, charts, and graphs that are all too prevalent from the vendors in our space. You know the ones: a thick layer of technology slathered with some gloss and wiggle, between two slices of "do it yourself".

In many cases, we find ourselves closing this gap by referring to some of the best examples of work out there. As we were thinking about this, the idea to provide a simple walk through of these examples came into being. The result: a 30 day calendar chocked full of some of the best samples of skills enhancing examples we could find.

30 Days to Better Visualization

Each day is a bite sized chunk and takes only a few minutes to watch, read, do, or play. Some of the days are comprised of Juice content, but most days are from other sources that we’ve found useful.

You can download it to use yourself, or to share with your friends who need to expand their info-viz horizons. Either way, we think it’ll get your creative juices flowing.

Chart Makeovers

Earlier, Zach wrote a blog post on the ins and outs of chart selection. It reminded us how important it is to balance the right chart with the right visual presentation as dimensions and complexity change.

But your data presentation decisions don’t end there! Once you have a good handle on the right structure for organizing the presentation, you have to make it look good - making it function good and accomplish original goals. As promised in the previous post, here are the chosen chart structures at each stage of complexity redesigned for presentation. We’ll keep this simple with before and after shots, key design principles highlighted, and a freeform reflection on some practical design decisions. The explanations aren’t meant to be exhaustive but rather are a glimpse into design thinking.

Phase 1 | Sales + Calls, Aggregate Performance

Phase 1,  Phase 1 | Before and After

Before & After

Design Principles

  • Visualisation is not always the best solution
  • Emphasise the interesting

Design Reflection

  • For fonts, often the best choice is sans-serif, tabular fonts (like this). For this demonstration I simply used Helvetica because it gets the job done and everyone has access to it. The font size is 18pt for primary values and 12pt for secondary.
  • Qualitative values (calls, sales) will often be the text that should be treated with grey (50% black will do for most situations).
  • Quantitive values (559, 71,739) should be clear and easily distinguished from less immediately critcal information. Here they are bold, 80% black.
  • Superscript the dollar sign since its an unchanging qualitiative value.

Phase 2 | Sales + Calls / Product, Aggregate Performance

Phase 2, Phase 2 | Before and After

Before & After

Design Principles

  • Use color carefully
  • Use 50% grey carefully
  • Visual rhythm
  • Consider text style needs for dynamic content
  • Organize data visuals in a way that mimics thought process comparisons where appropriate

Design Reflection

  • Stacking the calls and sales bars should only be done with the right audience in mind. Though a dollar to calls value is not comparable in and of itself, in the midst of the context of other products, this makes it easier to visually compare the proportions of these values against each other from product to product. For example, immediately one can notice ’Ceramic Smoking Baby’ is a lucrative product.
  • Add consistent, distinct visual rhythm with light separation lines
  • Again, color should only be used to distinguish commonly changing quantitative values: numbers and bars in this case. But sometimes carefully using color on qualitative values can be helpful. The title (’Calls’), value (’202’), and visual representation (longest bar in this case) is an example good color management. No legend is needed, because the content itself explains visual relationships. The content is the legend.
  • Choose your 50% grey visuals wisely. Product names are secondary in visual weight to colored data values, because they are secondary mentally in the thought process of reading this chart.
  • Placing metric values to the left of the bars overcomes problematic rendering issues when values are very small.
  • Dollar signs are not superscripted because they would become unreadable.

Phase 3 | Sales + Calls / Time, Aggregate Performance

Phase 3, Phase 3 | Before and After

Before & After

Design Principles

  • Minimize chart junk
  • Use white space for comfortable reading
  • Remove text values that can easily be interpreted with visual counterparts

Design Reflection

  • Center trend values on vertical hash marks
  • Measurement dimensions should be grey
  • Distinguish current date with value and endpoint
  • Remove extraneous date values that can be easily interpreted with well placed light hash marks
  • Distinguish every 5 hash marks with length difference

Phase 4 | Sales + Calls / Product + Time, Aggregate Performance

Phase 4, Phase 4 | Before and After

Before & After

Design Principles

  • Give values context
  • Red is easily noticeable when used sparingly
  • Allow for easy comparison

Design Reflection

  • I put the sparklines first in the visual reading for two reasons: 1) the width of this graphic is always the same/dependable and 2) the context of data is often helpful to present first so subsequent values can be better understood. This little snapshot of time provides that context.
  • On the sparklines, distinguish today’s value and the lowest value (red dot). Use red carefully. You don’t need much to draw attention (where color blind issues aren’t an issue)
  • Be sure to provide ample space between elements, and that all graphical elements are aligned on your grid.

Memorable or Actionable or Both.

Recently, I saw the largest concentration of iPad users in the world, controlled a computer screen with my eyes, and learned about our looming robotic future. No, Apple doesn’t have a technology lab on the moon, but I did attend CHI 2010 (short for Computer Human Interaction - the entire program along with papers and authors are referenced here). I left with a bit bigger toolkit and plenty of research to consider further. One such effort investigating chart junk has been reviewed by EagerEyes’ Robert Kosara. I share his enthusiasm for research in visualization, but let’s look more closely at some issues the paper raises and consider how these findings fit into the goals of visualization.

Nothing gets information visualization designers’ feathers more ruffled than the thought of junky charts being more desirable than "Tufte-compliant" charts. I was skeptical, to say the least, in attending a presentation by Scott Bateman for a paper entitled, Useful Junk? The Effects of Visual Embellishment on Comprehension and Memorability of Charts. (The title is a bit misleading in that the paper is really about embellishments and illustration - not so much traditionally poor structural graphics often considered common "chart junk.")

Embellished vs. Plain chart

(Example of embellished vs. plain chart with same data, from the paper)

The aesthetic treatment of data presentation is a long-time debate, and Scott came all the way from Canada to answer the question: Should we use chart junk? The answer is an emphatic "maybe." The goal of the study was to look at interpretation accuracy and long-term recall, and the papers says,

our results question some of the premises of the minimalist approach to chart design.

Make charts Memorable.

Skipping the gritty details of the study, here are the findings of a provoking illustration with data embedded compared to an boring, "plain" chart:

  • more memorable over the long-term;
  • perceived as having more value and sense of chart bias; and
  • most enjoyable and easiest to remember.

More memorable is better, right? The question we should be asking is, better than what. Of course, more memorable is better than less memorable, but at what cost? And what do we really want people to remember? It’s doubtful the best way to drum up interest in data is by making it light up and do a dance to feed the public’s already marketing heavy information diet. | The Richest and Poorest Neighborhoods

Your data as is mostly marketing if it looks like this: | The Richest and Poorest Neighborhoods

Fully embellished charts


  • Draws attention, memorable imagery
  • Little analytical thinking needed, wider audience
  • Endless diversity
  • Creative exploration
  • Graphics and illustration heavy


  • It looks and feels glossy so people will treat it with the bias of a magazine or commercial TV ad
  • Little data depth
  • Non conclusive, likely not actionable
  • Few standards, wild chart organization
  • Production costs
  • Little research, relatively cheap
  • Illustration / Graphic artist talent required

Perhaps one’s attention is more likely to be drawn to these embellished charts if they are engaged in an entertaining or passive ritual, like watching TV, browsing the web, or shuffling through a newspaper. Perhaps they get the same personal impact as the funny pages. We should consider a greater sense of bias or value message is introduced through this style of data presentation (as confirmed by the study), and that can be detrimental to a viewer’s trust. It isn’t that imagery doesn’t have a place in the same conversation with data, but there are better ways to go about drawing attention than applying illustrations to data points.

In the data presentation arena, we definitely want data to be memorable, but even more so we want data to be actionable; therefore, valuable data remains the attraction.

Make charts Actionable.


Would you say this graphic is more or less plain than the example "plain" chart taken from the research paper earlier in the post? Would you say its more or less actionable? 

A chart is actionable if it answers enough questions of its viewer to instigate a meaningful decision or reaction to information presented. Therefore, charts are only actionable when the right information is presented to the right people with the right visual communication. 

Edward Tufte describes the use of this graphic by the New York Times that accompanied a data dense table along with a news column on the subject. It’s a simple point: in order to present meaningful, compelling, or personally motivating information, there either needs to be exactly the right data presented, given the context of the data and person, or enough dimensions and slices of data to be meaningful to a broader range of questions and needs. Supporting textual content always helps to tell the story, which builds the viewers mental model - thereby, making the data more understandable.

Non-embellished charts



No non-data graphics

Minimized distractions from data focus, no graphics or imagery suggesting bias,

Teachable, fundamental guidelines

little visual appeal unless the data density is high (which can feel overwhelming)

Sufficient data-depth emphasis

Actionable information

Requires more patience or experience from viewer.

Production costs

No illustration talent required

Research time and resources required, relatively expensive

The problem with embellishments as a primary style for getting the public engaged with data is that it continues to suggest that truly understanding how data impacts their world is beyond common thought or interest. The dimensions are minimal and value statements dominate.

But value statements aren’t always bad. Sometimes when you’re saying so little with an information-starved chart, its better to come out and say the point you’re trying to make with a single data point. Like this beautiful example from

Its Communications 101: say what you’re going to say, say it, and say what you said. When the information is somewhat clearly target and not exploratory in nature, this frank approach is often more effective. Embellished charts commonly stand alone with no supporting, meaningful story or conclusion. If the information is valid and valuable enough to be published, there should at least be enough effort to find and integrate a reliable source with more info to answer questions where the chart data left the viewer wondering.

Make charts Both.

When it comes to complicated information, stop treating it as if it can be polished nicely into a single chart and that will be sufficient to create understanding, motivation, and action. Charts make data visible and play off our innate human need to create a mental image of the information story we’re presented with. We need both visual attraction / definition and concrete factual data.

Illustration, graphics, and photography trigger emotion and interest in our right brain. They give us a chance to associate ideas and create mental connections to make sense of the world. Our right brain needs "embellishment" thinking to make connections.

Meanwhile, our left brain needs values, raw facts, and the ability to measure worth. Our left brain needs "plain chart" thinking to determine the cause and effect of connections; its interested in thinking about what really matters and impacts things at this moment.

There are few visualizations that even begin to approach the balance between imagery and data.

Example 1. The Tweet Tracker visualization is at least on the right track. One may say here that illustration is used as data points, but I would suggest the technique is appropriate here because the imagery is uniquely matched, within context, as another dimension to its data category.

Winter Olympics Tweet Tracker by Stamen.

Winter Olympics Tweet Tracker by Stamen.

Example 2. Embellishments come in diverse forms. You may have seen this presentation Al Gore gave on global warming. Notice what happens at 9:08 in the video as Al continues his commentary while riding a lift on stage up the side of the chart. Do you hear the background laughter? This kind of laughter is good. You know you’re audience is engaged. Duarte Design designed an embellished visual here to grab people’s attention and make the point memorable - alongside the data chart. This engaging visual device makes the data more memorable because the data is still the center of attention.

Visualization is simply the best language to create meaningful connections between data, thereby making it valuable. All charts are related to visualization, whether its good design or not. The conversation of whether embellishments are good or bad depends on many things, but the real question we should be asking is whether they are making your data more or less valuable. It is a fine thing to attract interest to data, but not when that is a device to overlook the real care needed in preparing sufficient information. Plain charts are fine also, but likely only for quick personal projects in excel where a mental model of the data connections are already well understood.

I’m thankful for Scott’s work with his colleagues on this research, and for people like Robert who also promote appreciation for the much needed research in visualization. The theme of graphical embellishment is thrown around so much in the visualization community that it rarely receives careful deliberation, and this paper starts a purposeful conversation. However, there is a long way in working towards conclusive goals.

Other visualization related papers presented at CHI 2010:

  • Useful Junk? The Effects of Visual Embellsihment on Comprehension and Memorability of Charts.
  • ManyNets: An Interface for Multiple Network Analysis and Visualization
  • Individual Models of Color Differentiation to Improve Interpretability of Information Visualization
  • High-Precision Magnification Lenses
  • Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design
  • Integrating Text with Video and 3D Graphics: The Effects of Text Drawing Styles on Text Readability
  • Animated UI Transitions and Perception of Time — a User Study on Animated Effects on a Mobile Screen

Survey Results: Are the Viz-Pundits Really Helping?

A few weeks ago Juice asked our readers to give us a few insights into whether or not we and other info-viz sites are actually helping them and their organizations be more effective at communicating information.

Well, the time has come to take a look at the results (oooh - pins and needles). The survey was way more popular than we expected, receiving well over 500 responses.

We had a few questions that were of the form "select the answer that best describes you" but, for the most part, we focussed on text based answers so that we could try to avoid directing the answers and could demonstrate some non-traditional visualization styles to explore results. As a side note, the open ended answers to the text based questions were truly intriguing to read - hopefully the presentation of the results below will give you a small insight to what we learned.

So, here are the results.

Survey Results

The first section of questions dealt with getting some context about our readers. Since the questions were multiple choice, we’re showing the results in traditional bar chart format.

Question 1

In terms of size, which of the following is your company most like?

  • A one man band
  • The Dirty Dozen
  • The University of Rhode Island
  • Microsoft
Q1: Company Size
Question 2

In terms of information presentation expertise, who do you see yourself as?

  • The Excel Chart Wizard incarnate (I’m happy with the quickest route)
  • Harold and the Purple Crayon (I’m pretty good, but not too finicky)
  • A Tufte clone (every chart is carefully and lovingly crafted with intention)
Q2: Expertise
Question 3

If your company were stuck on Gilligan’s Island, would you be able to use information presentation to get rescued?

  • No, Gilligan keeps using our Tufte books to prop up the break room table.
  • Maybe. The Skipper rigged up this island beacon system using coconuts, vines, and tiki torches.
  • You betcha! The Professor could build a huge island sized information display that could be seen, understood, and acted upon by the astronauts on the International Space Station.
Q3: Escape from Gilligan’s Island
Question 4

What two information sources do you most frequently use for information presentation tips, trends, and best practices?

  • BI Vendor’s website (e.g., Business Objects, Tableau, Cognos, etc.)
  • The Dashboard Spy
  • Dashboards by Example
  • FlowingData
  • Infographic News
  • Information Aesthetics
  • Jorge Camoes’ Charts
  • Juice Analytics
  • Junk Charts
  • Tufte’s web Site
  • Visual Business Intelligence (Stephen Few’s site)
  • VizThink
  • Other
Q4: Popular Sites

However, What we really want to know is what sites are most closely related. So we tried looking at them with a phrase net from ManyEyes:

Q4: Phrasenet

( You can experiment with it yourself here. )

This is a great way to demonstrate how sites are "connected". We see a very strong relationship between Juice and the other non-Juice sites, but not a strong relationship between the non-Juice sites, themselves. In retrospect, the question would have been more effective had we asked respondents for their "top three or four" sites (approximately: total number of options ÷ 3).

The next group of questions were crafted to help us understand the problems our users and their organizations are encountering when it comes to presenting information to stakeholders and users. For most of these questions we broke the number one rule in surveys: stay away from text based answers.

Question 5

Using one word for each, list three things that you most frequently find useful from these sources?

Q5: Tag Cloud

( You can experiment with it yourself here. )

This was one of the most useful result sets and clearly shows that people like examples and new ideas for visualizations, followed by tips on how to get it done. (I’m hoping this post meets all of those criteria to some level.)

Question 6

Within your organization, would you say the understanding of information visualization best practices is:

  • Staying the same
  • Improving
Q6: Improving?
Question 7

What one word describes the biggest barrier to improved information presentation at your company?

I selected a Wordle (as opposed to a tag cloud) for questions 7 and 8 because I wanted to see the results in a way that would give me the general feeling of the barriers and benefits - I wanted the answers to spur some sort of emotive response. I think a Wordle does this better than a tag cloud.

Q7: Barriers

( You can experiment with it yourself here. )

Question 8

What one word describes the biggest boon to improved information presentation at your company?

Q8: Benefits

( You can experiment with it yourself here. )

While the "barriers" answers were interesting, there are some real nuggets hidden in these "benefits" results.

Question 9

Finish this sentence: "My company would be oh so much better at information presentation if we just had..."

What we really want to know is what are the patterns and relationships between words. Having said that, the most common words are still interesting to see:

Q9: What would be better?

( You can experiment with it yourself here. )

But, we are really interested in the word patterns. So, we used the Juice search patterns tools Concentrate to identify patterns. The top patterns were



more X


more time X


better X


X data


X time


more time to X


time X


a better X


X data.


X more time


people X


more people X


more resources X


the right X


more people who X


people who X


time to X


more time and X


Now, if we look at how the "non-common" words relate visually, here’s what we get:

Q9: Phrasenet

( You can experiment with it yourself here. )

Question 10

Finish this sentence: "If I were to advise someone on how to best improve your capability to create really useful information presentation solutions, I’d say don’t forget..."

Again, it’s interesting to see the most commonly used words:

Q10: How to improve

( You can experiment with it yourself here. )

But the most value again comes from looking at the phrase net:

Q10: Phrasenet

( You can experiment with it yourself here. )

Question 11

Finally, we’re going to post results on our blog for free download. However, if you want us to notify you when the report is ready, please provide your email address below.(And because we have a large international following, please add your country as well, if you don’t mind. Why? ’cuz we’re just curious. Thanks!)

So, we’re going to show only the countries here, no email addresses (whew!). Let’s start with looking at the standard distribution:

Q11: Respondent Countries

And here’s the geographic representation from Many Eyes:

Q11: Many Eyes Map

( You can experiment with it yourself here. )

But, having looked at that, I thought it might be a little more interesting to look at the country locations like this (text sized based on number of participants):

Q11: Country Cloud

Additional Insights

And that was all of the questions that were in the survey. However, I thought some of the multiple choice "context" question required just a bit more analysis; there were some questions I still had that weren’t yet answered. So, I loaded the data into Tableau’s Public version of their application to give a little more analysis flexibility. Here is the dashboard I created to better understand expertise:

Characteristics of expertise
Characteristics of expertise

Powered by Tableau

What this shows is that organizations that are more capable of responding to tough information presentation challenges have a substantially higher ration of "Tufte Clones".

And this made me wonder how skills basis might be impacting different sizes of companies:

What companies are improving?
What companies are improving?

Powered by Tableau

A pretty nice linear correlation between company size and improvement trends, don’t you think?

You made it to the end!

This post turned out to be much longer than I wanted it to be, but hopefully you found it interesting and learned a few things about your fellow readers and how to display different kinds of survey responses. If you have other insights you think you see, please comment below! Thanks for participating!

Chart Selection, Art and Science

Choosing the right chart for data presentation isn’t easy -- even if you do it for a living. For those with less practice, it may resembles the flash of confusion I experience when my wife asks "Which of these outfits looks best on me?"

"...uhhhhhhh, both?"

And like that answer, there isn’t any safety in sitting on the fence.

Wouldn’t it be nice if there was a formula for choosing the right chart? The fact that there isn’t suggests it is a mix of art and science. There are plenty of examples of people who have taken a crack at this problem:

  • Andrew Abela created a diagram that categorizes chart types.
  • In Stephen Few’s book Show Me the Numbers, Chapter 5 provides an overview of graph fundamentals. Bonus: I received the following Graph Selection Matrix (PDF) from Steve.
  • In Stephen Kosslyn’s book Graph Design for the Eye and Mind, Chapter 2 is entitled "Choosing a Graph Format"
  • Sanket Nadhani shared this short tutorial which tackles the basic choices.
  • From NC State, a flow diagram  for chart selection
  • An Oracle-financed white paper entitled: "Selecting the Best Graph Based on Data, Tasks, and User Roles" (PDF)
  • BonaVista Systems has an Excel add-in for choosing the right chart.

(If you know of any others, put them in the comments and I’ll add to this list.)

While these are all great resources, I thought it could be instructive to walk through a sample chart selection process, starting simple then gradually adding more complex requirements. The focus of this post is on ’wireframing’ the correct presentation techniques; in a follow-up we’ll replicate these same charts noting best practices with refined aesthetics and layout.

I typically ask four questions in choosing how to present data:

1. What data is important to show? Specifically, which dimensions and metrics need to be shown at the same time.

2. What do I want to emphasize in the data? For example, do I want to compare different values, show relationships, or present changes over time? What story am I trying to tell?

3. What options do I have for displaying this data? Your Excel chart menu is a start, but don’t forget options such as tables, sparklines, small multiples, and advanced visualizations like treemaps. Many Eyes’ list of visualizations can spark additional ideas.

4. Which option is most effective at communicating the data? Which chart or visualization emphasizes what’s important in the most direct and readable way?

Imagine a sales organization where two metrics matter most: activity (as measured by call volume) and sales (as measured by dollars sold). The simplest place to start with this data is to present aggregate performance for those two measures. Even with this most basic situation, you have a few options:

Step 1, All Options

Conclusion:Data doesn’t always need visualizing. The common and dreadful example of this mistake is when people use a speedometer-style gauge to show a single number (option 3). It is a lot of work, pixels, and distraction for no user value. In this example, we have just a single data point for each measure and no comparisons (e.g. to goals, to last year’s performance, the values against each other), so it’s best to keep things clean with option 1.

Next, let’s look at options for showing activity and sales data by product. In this case, the emphasis should be on the relative performance of each product.

Step 2, Option 1
Step 2, Option 2

Conclusion: Option 1 is the winner. We prefer a vertical layout of labels (bar chart) to a horizontal (i.e. column chart - not shown) because the labels are more readable and the horizontal layout can suggests a time element in the graph. As has been thoroughly documented, a pie chart doesn’t allow you to see differences in values as effectively as a bar chart.

What if we wanted to understand these two metrics by time?

Time needs to be displayed horizontally. We’ve seen ambitious examples from and Axiis that attempt to break this mold, but they more often confuse than enlighten.

Step 3, Option 1
Step 3, Option 2
Step 3, Option 3

Conclusion:I’ve backed away from using dual axis charts after experiencing too many situations where people are confused by which line goes with which axis, no matter how clearly labeled. Because the emphasis for the data needs to be the trend over time, I would recommend option 2 over option 3’s sparklines.

Now it gets interesting: What if we wanted to understand these two metrics by product and by time?

Step 4, Option 1
Step 4, Option 2
Step 4, Option 3

Conclusion: The best option for this case depends on the importance of clearly communicating the detailed trend for each product. In most cases, the "essence" of the trend is good enough, i.e. Is the trend up? Down? Erratic? Smooth? Under that assumption, option 3 provides a nice comparison of the relative product performance and trend.

A few final observations:

  • Labeling matters. How labels are laid out in a chart can be a big difference in readability. It is almost always better if the label text can be written horizontally and be closely tied to the value (rather than in a disconnected legend).
  • Multiple areas of emphasis. There will be compromises when you need to emphasize two things simultaneously (trend, relative values). Pick which one matters most.
  • Know your options. the more types of charts you know of and understand how to apply, the better set of options you’ll be able to come up with.

S. Few Renounces Dual-Axis Graphs; Juice Ups Ante

After deep introspection, Stephen Few has determined that graphs with dual-scaled axes are fundamentally flawed. Rather than risk the potential for confusion, he believes that there are superior graphing approaches for situations where related data series have different units or magnitudes. His measured and thorough analysis concludes:

“It is inappropriate to use more than one quantitative scale on a single axis, because, to some degree, this encourages people to compare magnitudes of values between them, but this is meaningless.”

I commend Stephen for the courage to start down this path, but he hasn’t gone far enough. Here at Juice, we must often take controversial positions. You may remember that we were among the first to criticize Microsoft’s “databars”, the first to take on the powerful Dashboard Gauge lobby, and the first to challenge the applicability of Tom Davenport’s “Competing on Analytics” sales machine.

While it is true that the second axis can be deceptive, let’s not let the first axis off without asking some tough questions. It is the confusion—nay, the collusion—of the two that causes trouble—who is to say which is the bad seed? We must ask ourselves, do not axes belong in the “Axis of Evil”?

The problem is broader than Stephen suggests: axes are just the tip of the iceberg when it comes to graphic bling that potentially distract or confuse readers:

Take data labels, for example. They encourage users to consider specific values rather than focusing on relative sizes or placement of graph lines or bars.

Legends draw the reader’s eye away from the central storyline of a graphic.

Gridlines… please don’t waste my time with these flat faux-series. One wouldn’t put pinstripping on a Ferrari.

Place your graph in proper context and titles become redundant.

Minimalism is in. Extraneous graph decoration is out. Look no further than Tufte’s sparkline: no excessive graph decoration there.


The world cries out for a new charting aesthetic. One that champions elegance and casts down gaudiness. Let us evoke the pure visual essence of the data. Let us find a pure form to evoke the emotion and hidden meaning of the data. Now is the time for Naked graphs—stripped to the essentials (TM).

Our argument is simple: the visualization of information is the message. The data is but an intermediary form of that visualization. Therefore, any residue from the raw data should be scrubbed from your final graph. Only when you achieve this unadulterated state will the meaning of the graphic burn its way into your consciousness.

Here’s an example of an analysis that casts light on both the relationship of the Fed to hedge funds while simultaneously answering your question about what happened with last month’s sales in the Newark division.

naked analysis

Truly here we see the words of Mark 9:43 made real:

If your hand causes you to stumble, cut it off; it is better for you to enter life crippled, than, having your two hands, to go into hell, into the unquenchable fire.

Gaze in awe, viewers, and find wisdom on this very foolish day.

The Colbert Bump is Real, Colbert’s Nation Not What He Thinks it is

Stephen Colbert has mentioned that he’s having trouble getting guests during the writer’s stike. We find this puzzling, given the supposed benefits of the Colbert Bump. Does being on the Colbert Show really provide a bump—a critical leap that vaults a writer, or a politician to superstardom?

We know that Colbert isn’t a big fan of “facts,” and only needs his gut to tell him the Colbert Bump is real. At Juice, we let the data decide what’s real or not, so our apologies to Stephen for not taking his word for it. Intrigued, Juice Analytics set out to find out the truth. We gathered data about Amazon sales rank for 20 authors that appeared on his show in recent months. How did those ranks change in the days immediately before and after the authors’ appearance on the show?

Amazon Sales Rank of Colbert Guests

Hmmm, there might be something there but those sales ranks don’t tell us much. Fortunately for Stephen, some “eggheads” have worked out roughly how Amazon sales rank corresponds to actual book sales. We calculated the sales, and normalized the data so that the week prior to appearing on the Colbert Report was equal to 1.0. Here’s a picture.

Projected Sales of Colbert Guests

That looks like a bump, Conan. In fact, being on the Colbert Report increases sales by 10 times on average. That bump doesn’t last forever, but, let’s face it, what does?

We also wanted to know, what kinds of books are Colbert’s audience going crazy for? After all, Colbert is well known as a rock-solid conservative. He’s tight with the Bush Administration. Even though he debates a few liberal (“pinko”) authors now and then, most of his guests are writers of pop-intellectual studies of the Gladwellian persuasion.

Here are the authors and how we categorized them:

Pinkos: Jessica Valenti, Full Frontal Feminism: A Young Woman’s Guide to Why Feminism Matters, Wesley K. Clark, A Time to Lead: For Duty, Honor and Country, Robert Shrum, No Excuses: Concessions of a Serial Campaigner

‘Publicans: Tom DeLay, No Retreat, No Surrender: One American’s Fight

Pop Essayists: Daniel Gilbert, Stumbling on Happiness, Daniel B. Smith, Muses, Madmen, and Prophets: Rethinking the History, Science, and Meaning of Auditory Hallucination, Michael Gershon, The Second Brain: A Groundbreaking New Understanding of Nervous Disorders of the Stomach and Intestine, John J. Mearsheimer, The Israel Lobby and U.S. Foreign Policy, Thomas L. Friedman, The World Is Flat: A Brief History of the Twenty-first Century, Frank J. Sulloway, Born to Rebel: Birth Order, Family Dynamics, and Creative Lives, Jared Diamond, Guns, Germs, and Steel: The Fates of Human Societies, Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable, Richard Preston, The Wild Trees: A Story of Passion and Daring, Malcolm Gladwell, Blink: The Power of Thinking Without Thinking, Bjorn Lomberg, Cool It: The Skeptical Environmentalist’s Guide to Global Warming, Andrew Keen, The Cult of the Amateur: How Today’s Internet is Killing Our Culture, Michael Wallis, The Lincoln Highway: Coast to Coast from Times Square to the Golden Gate

Popular: Stephen Colbert, I Am America (And So Can You!), John Grisham, Playing For Pizza: A Novel, Tina Brown, The Diana Chronicles

How much of a bump did each of these groups receive?

Colbert Bump by Category of Guests

It’s a shock! Liberals and high-minded eggheads do better than popular or conservative books. I’m not sure if Colbert knows this, but his audience isn’t who he thinks they are.

Here are all the authors and their normalized sales around the time of their appearance on the Colbert Report.


This post was a collaborative effort of the entire Juice team. Pete Skomoroch concocted the idea, wrote copy, and found the study linking Amazon Sales Rank to actual sales. Zach data mined. David May whipped up elegant, instant visualizations. Sal Uryasev munged data.

Analytics Roundup: TIps for showing, sharing, communicating

Developer’s Guide - Google Chart API - Google Code
Beautiful stuff, particularly the Venn diagram.

Align Journal - BI Worst Practices
We often see articles on BI "Best Practices" here is an article telling us what NOT to do.

flot - Google Code
Attractive Javascript plotting for jQuery.

ongoing · On Communication
Interesting blog post about how different forms of communication rank for immediacy, lifespan, and audience reached.

The Excel Magician: 70+ Excel Tips and Shortcuts to help you make Excel Magic : Codswallop

Source for presentation ideas.

Introducing Chart Chooser

Find and Download Great-Looking Excel and PowerPoint Charts

Chart Chooser is an online tool that answers two questions we commonly get:

  1. What type of chart should I use to show my data?
  2. How can I make good looking Excel or PowerPoint charts?
Chart Chooser

Chart Chooser is easy:

  1. Check the boxes on the left that best describe your objective
  2. Select the chart that you want to use
  3. Choose from Excel or PowerPoint downloads to get a formatted chart template

A few notes about Chart Chooser:

  • Thanks to Andrew Abela of Extreme Presentations for inspiring Chart Chooser with his “Choosing a Good Chart” post and for working with us to put this tool together.
  • We’ve tried to make the charts both Tufte-compliant (i.e. minimal chart-junk) and visually attractive (thanks to Google for the color scheme).
  • Feel free to suggest other types of charts that you’d like to see in the Chart Chooser. Send an example to
  • If you’d like a customized version of Chart Chooser for your organization, write us at or call me at 202.251.7750.

Recreating the NY Times Cancer Graph

This New York Times cancer graph is a beautiful piece of work.

NY Times cancer graphic

I wanted to see if we could reproduce it with everyday tools.

Excel reproduction of the NY Times cancer graphic

Click here to watch a screencast showing how it was done. Warning the screencast is a little long—14 minutes—and a little unpolished. One cut, no retakes, banzai analytics!

Derek raised an interesting question about how to find the fonts used by the New York Times. While I don’t think you can find a high quality free version of these fonts (Helvetica Neue, Univers?), Microsoft has made some very good new fonts for Vista and these are also available to Microsoft Office users through a compatibility pack. Here’s a link or google for "microsoft office compatibility pack". I recommend using these fonts.

Here’s a version of the graph with these new fonts and more emphasis on getting the typography right.

Excel reproduction of the NY Times cancer graphic with better fonts