Juice Analytics

(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 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 in the practice

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

Trilogy Meter by Dan Meth shows movie enjoyment by sequel.

Jorge Camoes’ small multiple graphic for unemployment data

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

• BI Commons: The book on trellis charts, AKA small multiples
• Junk Charts’ posts about small multiples

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.

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:

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.

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 Trend.ly and Axiis that attempt to break this mold, but they more often confuse than enlighten.

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?

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.

• 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.

2009 has been a year of sharing here at Juice. First there was our long-used DTP methodology for interactive Excel reporting. Then we released our JuiceKit™ SDK. Today, I want to share another bit of trickery we’ve used to solve a common PowerPoint presentation problem.

The challenge

Let’s say you need to produce the same presentation month after month, updating the data each time. Or maybe you have a set of slides that need to go to a bunch of different audiences each with their own specific market, product, business line, or industry.

Updating all the slides by hand can be tedious, slow and error-prone. The presentation is basically the same, you simply want to swap out the underlying data. You need something that acts like a “mail merge” for PowerPoint.

Our approach

When we’ve helped clients with this situation, our approach has been to create re-usable PowerPoint slides (i.e. templates) that link directly to a database. This gives us the ability to stamp out new presentation by changing the raw data underneath. Simple enough to say; not quite so simple in practice. Here are a few of the hairy bits:

1. Data structuring. We populate the data into a Windows-accessible SQL database such as MS Access or SQL Server so we can use SQL queries to define the data needed for our charts and tables.

2. Slide templates. We create slides with charts, tables, and text boxes that are formatted to account for the variance in the data that may need to be displayed. Ensuring that the charts always look good is surprisingly hard.

3. Connect templates to data. Originally we rolled our own solution by creating a “templating” language that we embedded in the notes section of the slides. More recently, we discovered PTReportGen, a tool that extracts data from a data source and populates it into PowerPoint. PTReportGen allows you to connect objects in the slides (i.e. charts, tables, text boxes) to results from SQL queries from our data source. For each slide, there is a .PTR file that connects the contents of the slide to the database.

4. Scripted production. PTReportGen gives command line control, allowing us to write Python scripts to cycle through our data and populate the charts and tables in our template slides. Because we are interested in generating dozens (sometimes hundreds) of versions of a single slide, our script iterates over the database to pull different results across multiple dimensions. Below is a bit of pseudo-code to give a sense of how the scripting works to produce slides by market and by demographic:

markets = ('Market1','Market2','Market3')
demographics = ('Demo1','Demo2','Demo3')
PTRFileName = 'C:\Documents\UserName\Desktop\MyReportGenerator.ptr'

for demo in demographics:
    for market in markets:
        ReportFileName = 'PathName\FolderName\demo\market.ppt'
        cmd = 'PPTReport.exe PTRFileName -demo -market'

1. Post-processing. While most chart and data table designs can be achieved by clever template layouts, some advanced designs involve additional intervention to achieve the desired level of polish. A python script combs through the result template and adds coloration and layout improvements.

It isn’t simple, but once constructed this “slide factory” is a valuable capability that can free up an enormous amount of time from presentation grunt work. Here’s a short video that gives you a sense of what the process looks like. Personally, I find the production of slides vaguely hypnotic.

Other approaches and resources

We are not the first people to encounter or solve this problem. Below are a few other resources on the topic. I’d be curious if there is a native MS Office solution that I could include in this list.

• PowerPoint Automation Toolkit: “With the PPTATK, PowerPoint becomes a best-case union of a presentation tool and a report writer. With the Tookit, you can build presentations which combine static slides from a slide library and data-driven slides which display charts, tables, and graphs from structured data sources.”

• PresentationPoint: “Generate new up-to-date multimedia reports with 1 click only – put real-time data in your presentations.”

• Microsoft Help: “Working with PowerPoint Presentations from Access Using Automation. Create a PowerPoint slide presentation from scratch using Access data.”

• Stack Overflow discussion on “PowerPoint Automation from MS Access…queries to chart?”

At the recent Turning Statistics into Knowledge conference (here’s a synopsis), I saw The New York Times’ Amanda Cox present on how their 25-person design team designs and builds infographics. In my opinion, The New York Times sets the bar for telling stories with data. Amanda, I later found out, is sometimes referred to as the Michael Phelps of Infographics — presumably for her tendency to win infographics awards, not for getting photographed with a bong.

Here’s a infographic from the presentation that I particularly liked:

This chart is a re-examination of the OECD Business Cycle Clock which:

has been designed to better visualize business cycles – fluctuations of economic activity around their long term potential level – and how some key economic indicators interact with the business cycle.

(Flowing Data also took a look at this chart and the other approaches to presenting the same data.)

Amanda’s version of this chart is great because it demonstrates what can be done with the under-used scatterplot chart. Scatterplots are effective at presenting the relative performance of a set of things (e.g. product portfolio). Typically they show a snapshot in time; Amanda has added a time dimension without visually overwhelming the user.

As we’ve done in the past, we wanted to try to recreate a New York Times-style graphic in Excel. Here’s how it came out:

We have a few tricks in here to make this Excel chart possible:

• The chart is a scatterplot with smoothed connector lines. A second highlight series displays just values based on the time selection at the top.
• The line chart at the bottom contains a bar chart that keys off of the time selector to help visually display the time range selected.
• All the labels on the chart are extra data series with data labels rather than adding text labels onto the chart. This approach makes it easier to place the points in the appropriate spot and not worry about problems on resizing.
• A simple macro on the “animate” button walks through the data.

You can download the Excel spreadsheet here

We were excited to see that Federal CIO Vivek Kundra and his team used our open-source JuiceKit™ treemap on the recently released Federal IT Spending Dashboard.

While Tim O’Reilly mistakenly gave credit for all the visualizations to Fusion Charts, we know better. A mother always recognizes her baby. I bet Google also recognized their Motion Chart.

Enough complaining about the broken bits of Business Intelligence; it’s time to highlight the things that are good and right in the industry. Like most industries, the renewal and innovation occurs at the fringe, beyond the comfort zone of established vendors.

I’ve created five categories and a catch-all to capture the solutions and companies (not so much technologies) that are leading the next generation of Business Intelligence. The categories are:

• Analyst tools
• Dashboards
• Targeted solutions
• Open-source and free
• Advanced visualizations
• Other stuff

Naturally I’ve focused on areas of Juice expertise and focus — not coincidentally, the places where we feel BI has neglected end-users. According to a study by the Business Application Research Center, BI end-user adoption sits at a lowly 8%.

I’m happy to take your suggestions (and update the post) for things I’ve missed in these categories or for entirely new categories.

Analyst tools

Tools that make it easy for analysts to pull data from multiple sources, analyze, visualize and share it.

Winner: Tableau, the reigning king of visual analytics tools, has added more web-based functionality to allow for online sharing and collaboration.

Runner-up: Good Data has arrived on the market with a web-first platform designed to democratize analytics. I had a chance to get a demo from the management team and was impressed with the ease of use and high-quality data presentation.

Dashboards

“A frequently updated analytical display that is clear and concise” (via a recent post)…and not likely to draw the rage of Stephen Few.

Winner: BonaVista Systems wants to make Excel a “first choice dashboard tool.” From the humble position of sparkline plug-in vendor, BonaVista has taken a leadership role in encouraging more effective dashboard design.

Runner-up (tie): Two BI companies, Qlikview and Microstrategy, seem to be following BonaVista’s lead. Unfortunately, they may only be dipping in a toe as I found just a couple examples that break from the traditional over-glossy, gauge-riddled dashboard interface.

• Qlikview
  

• Microstrategy
  

Targeted solutions

Companies that serve a narrow slice of the BI world extremely well. The desire to be all things to all people has been an Achilles Heel of the BI industry. The general purpose BI platforms often prove too broad and too generic to serve the unique problems of specific industries or functional areas.

Winner: Wall Street on Demand is a brilliant, below-the-radar provider of information solutions to the financial sector. Their sparse, articulate marketing text and few screenshots hint at a company that knows exactly what they do and deliver high-quality BI solutions. I wish I knew more.

Runner-up (multiple): The following are just a few companies that have focused on an industry or functional segment to deliver targeted BI solutions:

• Quantivo for customer behavior analytics
• Visual I|O for pharmaceuticals
• LucidEra for sale pipeline reporting and analytics

Open-source and free

(I know there is a difference.)

Winner: Pentaho offers an open-source end-to-end BI suite that is a competitive alternative to the big-guys. Of course, the implementation it isn’t necessarily cheap or easy.

Runner-up: If anything should scare the BI industry, it is the possibility of a Google Analytics model extended into more general data analysis and visualization tools. Google Fusion Tables may just be the tip of the iceberg.

Advanced visualizations

Bringing leading-edge visualization techniques out of academia and into the business world.

Winner: Many Eyes continues to impress with high-quality visualizations. They are easy to create and clean in design and usability. Impress your boss with a slick visualization in your next presentation.

Runner-up (tie): Openviz / Advanced Visual Systems and Panopticon appear to be the two BI vendors battling it out for leadership in advanced visualization solutions. Unlike Many Eyes, these guys lack Tufte-esque sophistication in infoviz design. That said, there is a big difference between creating a one-off New York Times-quality visualization and delivering a toolset that is re-usable in many different situations.

Other stuff to be admired

Free charts with good default design. InetSoft’s Style Chart and Google Charts offer free, embeddable charts.

Jargon-free BI marketing. With few exceptions, BI web sites are densely populated with those awful stock-photography people sitting around conference tables (or worse, the ethnically-diverse V-formation marching at you) and meaningless business jargon and techno-babble. I really appreciate Blink Logic’s web site with its straight talk and clean, readable design.

Beyond the desktop. RoamBI has a great-looking iPhone application that is designed to “transform your data into insightful, interactive visualizations delivered to the iPhone.” It makes the Oracle and Qlikview iPhone apps look old-school.

I’ve developed a bit of a penchant (obsession?) for decomposing the pieces of analytical applications and framing the good and the bad characteristics. So far I’ve taken on treemaps, real-time dashboards, alerts, composite measures, success metrics.

Next up the poor, neglected, and taken-for-granted filter. For such a common and essential component, it seems rare that designers take a moment to consider how to make the best possible filtering mechanism. Here are the five elements I consider critical to a good filter selector along with examples from exemplary interface designs.

1. Selections
2. Impact
3. Context
4. Persistence
5. Short-cuts

Selections

Good filters make it obvious to users what has been selected. That might seem like an obvious necessity but consider what happens when you filter in an Excel list. The filter section, even if it is a single item, is immediately hidden from view.

Jonathan Harris’ frequently referenced We Feel Fine visualization offers one of my favorite filtering examples. Notice how the selected items are highlighted and the non-selected items are de-emphasized. The bar at the top clearly shows what has been selected, even after the filter selector is “put away.”

Impact

The best filtering mechanisms also give instant feedback about the impact of your filters. This can be as simple as a subtle indicator that the filters are being applied. Even better, as demonstrated in the The New York Times’ Rent or Buy site, the graph animates in real-time as filters are applied. This creates a very tangible connection that helps the user understand the impact of the filtering choices.

Context

Filters should provide information around the items being selected. What does it look like? How many are there? Take the simple font selector in Office applications: Isn’t it a no brainer that the names of the options are shown in the actual typeface? Here are a couple other fine examples of context:

Click shirt is Bret Victor’s brilliant t-shirt design interface. In it, he offers an elegant filter implementation where all the selections show images of what you are about to select.

Elastic lists is one of the most innovative approaches to filtering. The height of individual blocks in the selectable stack shows the frequency of the items, an embedded sparkline shows the trend, and brightness indicates “weight of the metadata value compared to the overall distribution” (a bit too ambitious/confusing, in my view).

Persistence

Given the importance of filters to most information applications, it is surprising how often the interface makes them hard to find. As I mentioned in an earlier post, the failure of many analytical and reporting applications is that “they assume users know precisely what they need before they’ve begun the analysis.” Filtering shouldn’t be a one shot deal; the functionality should always be accessible.

Kayak, a travel site, integrated the selection filters into the results so users can easily change their trip criteria without having to start a new search.

Short-cuts

Finally, filters should make it easy to apply common selections (All, None) or complex sets (My Saved Filters, Northwest Region).

Moodstream by Getty Images recognizes that users aren’t always going to want to configure a bunch of filters individually. The presets wheel solves this problem by offering a series of pre-defined “filter sets.”

Finally, I’d be remiss if I didn’t mention the sophisticated and powerful filtering functionality delivered in Tableau. In addition to providing filtering by selecting graphs (i.e. in context filtering), the application allows for multiple selector types, wild-carding, conditional filters, top/bottom filters, and on and on. If you want a comprehensive catalog of potential ways to offer filtering, watch the Filter Data video here.

Analytics can be all about having the right tool for the job. When your data is text, traditional analysis tools (e.g. Excel, OLAP tools) are like peeling a mango with a chainsaw.

There are a number of visual exploration tools specifically designed for text data, including:

• Word clouds like Wordle (fun but superficial);
• Network diagrams like Visual Thesaurus (good for individual words, not text);
• Trend graphs like Baby Name Voyager or Google Trends;
• Granular presentations for interacting and exploring individual phrases, e.g. We Feel Fine and Twistori
• “Word trees” that let you navigate through lines of text to understand the most frequent words, relationships between words, and common phase and sentence structures.

It is quite difficult to find a Word Tree in the wild. The brilliant team at IBM’s Many Eyes were the first to make Word Tree’s generally available. The same ManyEyes team have also created an alternative approach for visual text exploration with a tool called Phrase Net.

Recently, we built a slightly different take on the Word Tree in Concentrate, our tool which allows users to explore huge search query lists to see how people use search keywords. For geeky entertainment, we created a special Concentrate demo account with the lyrics of songs from Rolling Stone’s 500 Greatest Songs of All Time. Click here to sign-in to the demo (Press submit and then choose WordTree at the top).

Here’s how our Word Tree works:

• The box at the center is your starting point. When you open a Word Tree, it will contain the most common word in the text data. You can edit this box to “re-center” the wordtree (name that tune):

• Stretched out on either side are words and phrases that are tied to that center word. The size of the words represents their relative frequency.

• Rolling over the words/phrases will highlight the connections to your center word and on the other side. You’ll also see a pop-up box with examples of the phrases containing selected words.

• You can open or close branches by clicking on a word. Words with hidden branches are highlighted in orange. We also have an ability colorize the words based on a metric in your text data.

While these more advanced visualizations are a start, I suspect there is a lot of room for other tools and techniques to visually explore text data. I’d be curious to hear about other tools you’ve seen along these lines.

Conventional wisdom says that an executive dashboard must fit on a single page or screen. The argument hinges on a pair of assertions about this constraint: it provides necessary discipline to focus on only the most critical information; and it enables the audience to see results “at a glance.”

The “discipline” argument is made forcefully by Avinash Kaushik (among others).

“if your dashboard does not fit on one page, you have a report, not a dashboard…This rule is important because it encourages rigorous thought to be applied in selecting the golden dashboard metric.”

I buy wholeheartedly into the value of constraints. However, defining a useful constraint as a “rule” assumes there is only one viable means to achieve the desired ends. Confining visual real estate is but one way to focus your thinking. There are others: How about limiting yourself to five key measures? How about demanding that a dashboard can be understood in 3 minutes by a new user? How about only presenting exceptions?

The argument that a one-page dashboard necessarily provides an view of your business “at a glance” is more self-deceiving. Well-known information-ista Stephen Few uses this rationale in his definition of a dashboard:

A visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance. PDF

I check my speedometer “at a glance”. I “glance” at a Heads-up Display (HUD) on a video game showing how much energy my character has remaining. These displays communicate but a single number that is already hovering on the corner of my consciousness. If we follow this advice literally, we’d show:

Assuming one page gives you quick, easy comprehension is like assuming all red cars are fast. That’s simply not true. It must be duly noted, however, that all red cars are cool.

More often, people follow the one-page dashboard rule off a cliff like these folks.

There are real problems with this definition:

• In reality, the one-page rule leads to jamming information into the available space.
• When everything must fit on a page, there isn’t room to describe the connections between information or fashion a story from the data.
• A good dashboard raises more questions than it can answer. Sticking to a static piece of paper limits any ability to find or present explanations.

Don’t get me wrong: A one-page dashboard is often an effective way to create “a visual display of the most important information needed to achieve one or more objectives.” But with streaming video, interactive visualizations, podcasts, Kindles, smart phones, video projectors…is it really necessary to limit ourselves to 8.5″ x 11″ piece of paper. Or might we open ourselves up to some more creative solutions to sharing the numbers; a short movie, a few slides, a short text narrative, or 140 characters.

I’d like to use this definition instead and will be back soon with some ideas on how to make your dashboards clear and concise.