1. Skip to navigation
  2. Skip to content
  3. Skip to sidebar

Our Blog

We’re teaming up with O’Reilly Media to challenge you to participate in a data visualization contest leading up to O’Reilly’s Strata New York Summit September 19 – 23.

Data has become nearly as essential as food in both our personal and professional lives. So, why not use food as the basis for a data visualization contest?

Play With Your Food.

Join in the competition and visualize information about all the delicious fare our society enjoys. (First, you’ll want to put down that chicken wing, lest you get sauce on your keypad.) Being the foodie you are, you’ll appreciate that we’ve found some pretty cool data sets from FoodFacts.com for you to play with, making this subject matter you can really, uh, sink your teeth into.

A Trip to NYC, Strata Conference Passes and More.

The grand prize winner will win a trip to the Big Apple to present their winning visualization at the O’Reilly Strata NY Summit in New York, NY September 20 – 21, 2011.  Other prizes include Strata NY Conference passes, ebooks from oreilly.com and more. Sweet.

On-the-Map Judges.

Who would pass up an opportunity to get the attention of these judges, let alone have their work reviewed by Flowing Data’s Nathan Yau, The New York Time’s Amanda Cox and Juice’s own Chris Gemignani?  Serious bragging rights.

All That.

More information about the challenge categories, the rules, the prizes, the judges, judging criteria and all you could possibly want or need know about the contest is here.

So, get started on your data visualization now, while your appetite is whet for competition. Entries are due by August 28, 2011.

Juice Fans Get 30% Off Strata NY.

Register now to attend the O’Reilly Strata NY Conference, and get 30% off your registration fee with the special Juice fan discount.  Just enter “JUICE” on the conference registration page.  Learn more.

Topics:
, , ,



I grew up in a bilingual household where we spoke French and English. Many of us who’ve been exposed to other languages realize that there are some words that just don’t translate well into English.

One of the words that got used often in our family was the French word gourmand.  Its closest translation in English is gluttony, but how often does anybody ever say that word?  Probably the simplest way to think of it is the antithesis of gourmet, or even better, someone who prefers quantity over quality.

While there can sometimes be a negative connotation with the phrase, “Il est gourmand,” (“He is gourmand”), it can also be just a recognition of someone’s preferences.

To this day, even though my French has gotten pretty bad, I still occasionally refer to people as gourmet or gourmand.  It could be when I’m sitting in a restaurant, standing behind them in line at Costco or even hearing about their current data initiative.

What is a data gourmet?

Like a Data Gourmet
Data is to an Information Connoisseur as Food is to a Gourmet Chef

Just like a food gourmet, a data gourmet is someone interested in something distinctive, visually appealing and inspired by results or action taken. It isn’t about hordes of numbers or metrics. It’s about getting the right metrics in place, putting them in the right context and letting them stand out.

Think of the chef who prepares the meal like the one in the picture. He or she not only wants to stimulate your taste buds, but also hopes that their use of color, plating and white space will appeal to you and your visual senses, as well.

What is your data gourmand?

Quality or Quantity?
Prioritize Data Quality Over Data Quantity

So, as I alluded to earlier, not everyone is a gourmet. Many people value quantity over quality. As it relates to data, someone who is a gourmand is probably unsure of what they really want to do with all the data they are requesting. They figure it best to get as much as they can while they can, especially if they aren’t sure what they will do with it.

Unfortunately, they probably have never been exposed to a really useful dashboard or visualization. Ultimately, what they think will satiate them and potentially their users is as much data as possible. However, the volume of data would net a number of metrics, charts and gauges, etc. that would be more than they could ever consume.

Working with a Data Gourmand

When you find yourself in a situation where you are working with a data gourmand (and you will – it’s just a matter of time), don’t look down your well-trained visualization palate at them.  Instead, gently guide them along a path of visual-epicurean transformation.

Most likely, they’re going to want to load up their dashboard plate with every bit of data junk they can find.  Start by getting them to see their dashboard as a blank palette to meet specific goals vs. an empty pallet to load up everything they don’t need.

As they select different metrics, invest the extra time to train them to carefully select just the right information that provides the balance their data diet needs for a healthy body.  As they make their selections, help them to see that it’s okay to have favorite metrics.  As Amanda Cox of the New York Times says, “Data isn’t like your kids.  You don’t have to pretend to love them equally.”

Finally, if you need some help, refresh your skills with the Juice white paper, “A Guide to Creating Dashboards People Love to Use“.

Once you’ve finished, ask yourself these questions.  Does everything in front of your gourmand now have a reason to be there? Did they pause in appreciation or comment that they can’t wait to use it?  If so, you may be well on your way to executive data-chef status.

Have a data gourmand/gourmet story of your own?  We’d love to hear about it in the comments below.

Topics:
, , , , , , , , ,



Juice’s Jon Buffington took center stage at the National Capital Area Google Technology user’s group in the D.C. area recently and busted some fancy moves.  And, you should have seen him once his presentation started.

Taking the group through the process of designing and implementing an interactive data visualization using Google Web Toolkit (GWT), Jon also incorporated DOM, Canvas, SVG, ReST and Scala browser and server technologies to complete the information experience.

GWT is an open source development toolkit for building and optimizing complex browser-based applications, and is used by many products at Google, including Google Adwords and Orkut.

Armed with a tutorial, Jon compared browser graphical techniques and their respective technologies compatible with GWT. As exhibited in this little gem, Jon simplified the visualization down to a basic bar chart, making the similarities and differences between the technologies amazingly clear. (Yo, Jon.)

Download the presentation, and adopt some formidable moves of your own.

Jon Buffington Shares Insight on Building High Performance Data Visualizations

Jon leads our product development team here at Juice, and crafts ingenious software technology that transforms data into information experiences. You can check out more of his work, specifically, here.

We’ll let you guys know where we’ll be next. Or, if you really want to keep up, sign up for our RSS feed and/or follow us on Twitter. (Hint: We share lots of little tidbits on Twitter that we don’t share a-n-y-w-h-e-r-e else.)

Topics:
, , , , , ,



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.

Topics:
, , , ,



One thing we really like doing here at Juice is meeting and talking with folks who are interested in the practical application of visualization techniques to make their jobs and businesses better. We know a lot of you out there feel the same. So, we’re planning meet-ups in three cities over the next few months — Atlanta, Washington, D.C. and Boston. In addition to giving those of you in these areas a chance to get together in one place at the same time, it will give us a great excuse to share some data visualization knowledge that we think will benefit you and enhance your skills.

Each Juice Tour event will start with a meet-and-greet followed by a presentation focused on some basic rules for effectively communicating data – where we will provide you with some easy-to-use principles that you will walk away with, leaving you to become far more proficient at presenting your data forward no matter who your audience.

Afterwards, you will have an opportunity to meet one-on-one with Juice in free mini-problem-solving sessions where we can talk specifically about your visualization problems and offer suggestions to help you work through them.

If you’re interested, register here and let us know your name, email and your location. We’d like to gauge your level of interest in the Juice Tour — starting with Atlanta, Washington, D.C. and Boston. If you’re not in these areas, but are interested in the Tour, please let us know that, as well. (If these go really well, who knows, maybe we’ll expand to include other cities, too.)

We look forward to hearing from you! (Oh, and did I mention, it’s free?)

Topics:
, ,



With enough visualization methods to warrant a periodic table, it can be confusing to know what to use and when—and which visualizations are even worth considering at all. This series of posts is intended to introduce you to the visualization approaches that we find most useful, practical, and audience-friendly.

What is a motion chart?

Motion charts

Motion charts are essentially animated bubble charts. A bubble chart shows data using the x-axis, y-axis, and the size and color of the bubble. A motion chart displays changes over time by showing movement within the two-dimensional space and changes in the size and color of the bubbles.

Modern-day motion charts were developed by an organization called GapMinder as part of a product called Trendalyzer. Hans Rosling, one of the founders of GapMinder, popularized the motion chart visualization in a much-admired TED Talk.

Motion charts can include a number of features, including:

  1. “Trails” to trace the path of individual bubbles
  2. Animation bar to control the time range and animation
  3. Selectors to define the metrics shown on the axes, bubble size, and color
  4. Show/hide labels

Motion charts

What problem does this solve?

Advanced visualization methods exist for three reasons:

  1. To show more dimensions of data simultaneously, therefore revealing more interesting stories in the data
  2. To show high level patterns as well as the individual elements that make up the pattern
  3. To dazzle viewers

Motion charts accomplishes all three. First, it brings the time-dimension into a chart that would otherwise represent a snapshot in time. Motion charts can help in an analysis if you find that you are asking yourself, how did I get here?

Secondly, Hans Rosling’s talk beautifully demonstrates the ability to see big picture patterns (flows of bubbles from one quadrant to another) while also focusing on the individual components. Finally, motion charts are sexy because stuff moves around the screen.

What to watch out for when using motion charts?

The masterful hands of a pro like Hans Rosling make motion charts look powerful and intuitive. Tiger Wood’s Phil Mickelson’s golf clubs are only a small part of what makes his game look so good. Effective use of motion charts can be tricky:

  • As an analysis tool, motion charts ask a lot of our visual pattern recognition skills. Bubble floating around in all directions, changing size and color can overwhelm many people. Hans Rosling had a clear story to tell. He also was able to narrow the data, metrics, and scope of his visualization to support his story.

  • Animation isn’t ideal for showing trends. Displaying trails can help, but is still inferior to the simple readability of a line chart. Don’t take my word for it: research shows that animation is not great for showing changes over time.

  • Animations also don’t transfer to static images–like that PowerPoint presentation you need to deliver to your boss.

  • Resist the temptation to cram in one more layer of data. Take this blog post comment for example:

“Great bubble chart solution. I’ve been looking for a 3D bubble chart so I can move bubbles in 3D space, allowing me to track an additional dimension. Any ideas?”

I’ve got an idea: Don’t do it!


Motion charts in practice

GapMinder shows a variety of public data sets using motion charts

Motion charts

Google Analytics has built motion charts into their interface to visualize visitor and traffic patterns.
Motion charts


Do it yourself in Excel

  • Anand has a very helpful blog post about Motion Charts in Excel, including a sample excel spreadsheet.
  • Jon Peltier has a first and second generation spreadsheet for motion charts.

Do it yourself with other tools

  • Google motion chart gadget is a flash-based widget that can be used in conjunction with Google Spreadsheets. More instructions here.
  • Google’s Public Data Explorer offers tons of data sources visualized using motion charts.
  • TrendCompass is a complex Flex-based motion chart tool. It offers all the functionality of the Google Gadget (and more), but little of the usability.
  • Tableau Public can create bubble charts with the ability to “scroll” through time.

More resources

Topics:
,



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.”)

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

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.

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

Fully embellished charts Pros Cons
Graphics and illustration heavy Draws attention, memorable imagery It looks and feels glossy so people will treat it with the bias of a magazine or commercial TV ad
Little data depth Little analytical thinking needed, wider audience Non conclusive, likely not actionable
Endless diversity Creative exploration 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.

0%
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 Pros Cons
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 goingtorain.com

goingtorain.com

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
Topics:
, , ,



(With enough visualization methods to warrant a periodic table, it can be confusing to know what to use and when—and which visualizations are even worth considering at all. This series of posts is intended to introduce you to the visualization approaches that we find most useful, practical, and audience-friendly.)

What is a parallel coordinates chart?

Parallel coordinates is a visualization technique used to plot individual data elements across many dimensions. Each of the dimensions corresponds to a vertical axis and each data element is displayed as a series of connected points along the dimensions/axes.

Jon Peltier’s chart of baseball players below offers a simple example.

Peltier parallel coordinates

Each line corresponds to a player with performance plotted across four characteristics. Two players have been highlighted to compared values.

Parallel coordinates was invented by Alfred Inselberg in the 1970s as a way to visualize high-dimensional data. These charts are more often found in academic and scientific communities than in business and consumer data visualizations. This isn’t too surprising as parallel coordinate charts can become very dense and difficult to comprehend. Stephen Few has a typical reaction (PDF):

The first time that I saw a parallel coordinates visualization, I almost laughed out loud. My initial impression was “How absurd!” I couldn’t imagine how anyone could make sense of the dense clutter caused by hundreds of overlapping lines. This certainly isn’t a chart that you would present to the board of directors or place on your Web site for the general public. In fact, the strength of parallel coordinates isn’t in their ability to communicate some truth in the data to others, but rather in their ability to bring meaningful multivariate patterns and comparisons to light when used interactively for analysis.

Mr. Few’s final point is right on: with the application of interactive highlighting, filtering, and roll-over detail, parallel coordinate charts can reveal interesting stories in your data.

What problem does this solve?

For most standard charts, there are only so many dimensions you can effectively show. A typical progression of charts by dimensions goes like this:

Dimensions Chart type
2 Scatterplot
3 Bubble chart
4 Bubble chart with colors
5 Bubble chart with colors and animation

And now you’ve pretty much made an indecipherable graphic. That’s where parallel coordinates can help in showing many dimensions, limited only by horizontal space.

Like all good visualizations, parallel coordinates can also show both the forest and the tree. The big picture can be seen in the patterns of lines; individual lines can be highlighted to see detailed performance of specific data elements.

What to watch out for when using parallel coordinates?

With its power to visualize multi-dimensional data, why aren’t parallel coordinate chart more popular? Here are a few of the issues:

  • Large data sets create a lot of visual clutter. More from S. Few: “Most of us who have used parallel coordinates to explore and analyze multivariate data would agree that meaningful patterns can be obscured in a clutter of lines, especially with large data sets.”
  • The order of the axes impacts how the reader understands the data. Relationships between adjacent dimensions are easier to perceive than between non-adjacent dimensions.
  • As the axes get closer to each other it becomes more difficult to perceive structure or clusters.
  • Depending on the data, each axis can have a different scale, which is difficult to display and for the reader to absorb.
  • Lines may be mistaken for trends or change in values even thought they are only used to show the connected relationship of points.
  • Then there is stuff like the following that can give the visualization technique a bad name:
    Ugly parallel coordinates

Parallel coordinates in practice

Protovis: In this example, hundreds of cars can be quickly compared by filtering along any dimension. Click and drag along the red rule for a given dimension to update the filter.
Protovis parallel coordinates

Junk Charts revised a New York Times graphic to come up with this take on a parallel coordinates chart:
Junk Charts parallel coordinates

Advisor Solutions’s Parabox solution goes beyond the parallel coordinate lines to also show information about the distribution of values by dimension.
Parabox parallel coordinates


Do it yourself in Excel

Do it yourself with other tools

  • Macrofocus uses parallel coordinate visualizations extensively in their products (InfoScope, SurveyVisualizer)
  • GGobi is an open source visualization program for exploring high-dimensional data”
  • FluxViz is a simple cross-platform tool that uses parallel coordinates for the visualization of high-dimensional spaces”

More resources

Topics:
,



(With enough visualization methods to warrant a periodic table, it can be confusing to know what to use and when—and which visualizations are even worth considering at all. This series of posts is intended to introduce you to the visualization approaches that we find most useful, practical, and audience-friendly.)

Small multiples maps

What is a small multiple?

Small multiples are a visualization concept introduced by Edward Tufte. He described them as:

“Illustrations of postage-stamp size are indexed by category or a label, sequenced over time like the frames of a movie, or ordered by a quantitative variable not used in the single image itself.”

In other words, small multiples use the same basic graphic or chart to display difference slices of a data set. Small multiples can show rich, multi-dimensional data without trying to cram all that information into a single, overly-complex chart. Small multiples go by many names, including Trellis Chart, Lattice Chart, Grid Chart, and Panel Chart. I would even argue that sparklines are a simpler, smaller cousin of small multiples.

What problem does this solve?

Small multiples offer a few valuable features:

  1. They allow for the display of many variables with less risk of confusing your audience. Trying to display three or more variables in a single chart is a challenge Stephen Few calls overplotting (PDF).
  2. The reader can quickly learn to read an individual chart and apply this knowledge as they scan the rest of the charts. This shifts the reader’s effort from understanding how the chart works to what the data says. That’s a worthy goal in all data presentation.
  3. Small multiples enable comparison across variables and reveal the range of potential patterns in the charts.

What to watch out for when using small multiples?

Like any visualization, there are many ways to mess up and undermine the value of the data presentation:

  • Placement of the small multiples charts should reflect some logical order, e.g. dimensional matrix, geography, or time. This helps the user quickly find the charts that are interesting to them.
  • Small multiples should share the same measures, scales, size, and shape. Changing one of these factors undermines ability for people to re-use their understanding of the chart.
  • Simplicity of the chart is critical. Users should be able to process information across many of these charts. The following small chart from the New York Times works as an individual graphic; when shown in “postage stamp” size across 20 cities, this chart is too data-dense.

Small multiples


Small multiples in the practice

Andrew Gelman’s analysis of public support for vouchers, broken down by religion/ethnicity, income, and state.

Small multiples

Trilogy Meter by Dan Meth shows movie enjoyment by sequel.

Small multiples maps

Jorge Camoes’ small multiple graphic for unemployment data

Small multiples


Do it yourself in Excel

  • Jon Peltier creates small multiples in Excel
  • Kelly O’Day offers numerous tutorials and example workbooks on how to create “Panel charts” in Excel
  • Juice provides a few Excel tricks for replicating a chart across a data set

Do it yours with other free tools

  • Tableau Public. Nobody has small multiples more baked into their DNA than the folks at Tableau.
  • Many Eyes for map-based small multiples.

More resources

Topics:
,



David Simon (of The Wire fame) has sucked me into another brilliant television series with Generation Kill. It is the story of a Marine recon unit at the beginning of the Iraq war. At the heart of all the action, the seven-part miniseries offers an intimate and honest profiles of individual Marines.

The characters don’t so much displace stereotypes as reveal texture and insight about the unique qualities of individual Marines.

The series got me thinking once again about different ways to analyze data. Almost four years ago, I posted a couple blog posts (Part 1 and Part 2) making a case for analyzing and visualizing data at a granular level to uncover patterns and behaviors. Generation Kill is a case study in looking closely at the individual trees to understand the forest.

Analytics is a journey of exploration–a continuous series of iterations with the goal of deeper understanding based on better questions and more targeted analyses. Einstein said:

“To raise new questions, new possibilities, to regard old problems from a new angle, requires creative imagination and marks real advance in science.”

How to arrive at new questions?

In the previous blog post, I described examples from online learning, credit cards usage, and football film study to show how granular analysis can spur new questions. I’ve stumbled across a series of new examples recently:

Surveys. Survey analysis is hard work–just ask Ken who recently presented results from Juice’s survey on the practice of information visualization in organizations. If a survey is mostly about understanding your audience, rolling up responses by questions can’t be the only approach (though it is the most common). Cross tabs (“displays the joint distribution of two or more variables”) are one direction to go. Another approach is to look for people who share common characteristics or patterns in their responses.

Macrofocus’ SurveyVisualizer is the most innovative survey analysis tool I’ve seen and it emphasizes data at a granular level.

“All the analysis elements are always shown as grey lines in the background. This provides an overview of the ranges and spreads of the individual values for each node, and facilitates the detection of outliers.” (from Visualization of Large-Scale Customer Satisfaction Surveys Using a Parallel Coordinate Tree)

Medical research. Research studies are conducted against carefully defined target and control populations with aggregate statistics across these populations required for conclusions. However, the ability to review the patterns of diagnoses and procedures at the individual patient-level can help test assumptions about the target population and refine the parameters of a study. Better model inputs; better results.

Speech analytics. Michel Guillet at Nexidia recently told me about their approach to speech data:

Nexidia’s speech analytics can mine thousands of hours of audio to categorize, correlate or spot trends. However, it is quite often in identifying and listening to a lone outlier that the application provides its most valuable insights. Some examples of outliers can be the very long call of a particular call type, the extremely abrupt one, the one with the most languages spoken or the one where no one is speaking at all. An outlier can change your hypotheses and put you in a different direction…perhaps a better one. Nexidia’s reporting and analysis tools offer many different methodologies including histograms, analysis of means charts and flexible filtration by meta-data to identify outliers in large amounts of data. In addition, Nexidia’s ad-hoc search functionality allows users to search an entire body of audio content at any time, which is often helpful to find the “smoking gun” or a single recording which can make or break an argument.

Of course you can’t be assured of a full or accurate picture when looking at granular data, but somewhere between standard aggregation-based analysis and granular views lies the truth.

Topics:
,



Page 1 of 812345...Last »