Seth Godin, well-known marketing guru, took a strong and misguided stand against bar charts¹ in a recent blog post entitled “The three laws of great graphs”

Godin suggests that bar charts (and presumably other chart types like scatterplots, bubble charts, bullet charts, treemaps, etc.) give too much latitude for data confusion and ambiguity when used in presentations. In Godin’s view, a chart should make a single, clear point and leave no room for alternative conclusions.

“The only reason (did I mention only) to use a chart in a presentation is to make a point. If you want to prove some deep insight or give people textured data to draw their own conclusions, DON’T put it in a presentation.” “If the facts demand nuance, don’t use a graph, because you won’t get nuance, you’ll get confusion.”

Godin had hit on a similar point a while back when he critiqued Edward Tufte’s favorite infographic Napoleon’s March to Moscow.

“I think [Tufte] is completely out of his gourd and totally wrong. I think this is one of the worst graphs ever made…To make me take 15 minutes to study it doesn’t make sense.”

I agree that complex infographics aren’t an effective communication mechanism for many audiences, particularly if you are interested in telling a focused story.

Unfortunately, he uses this reasonable foundation as a jumping off point to claim that bar charts are overrated and unnecessary (he throws in a heinous 3D column chart for emphasis). “The problem with bar charts,” he says “is that they should either be line/area charts (when graphing a change over time, like unemployment rates) or they should be a simple pie chart”

That popping sound you hear is Stephen Few’s head bursting.

It is reasonable to argue that a value changing over time is often better suited to a line or area chart. But pie better than bar. Sorry, no can buy. He obviously got some flak after this first post:

“I stepped on the toes of many data presentation purists² yesterday, so let me reiterate my point to make it crystal clear: In a presentation to non-scientists (or to bored scientists), the purpose of a chart or graph is to make one point, vividly. Tell a story and move on. If you can’t be both vivid and truthful, it doesn’t belong in your presentation.”

His follow-up post Bar graphs vs. Pie charts attempts to solidify his argument but ends up stepping in more goo. To make his point, he shows a effective pie chart versus an ineffective bar chart.

In a not so subtle sleight of hand, he has added another data series to the bar chart to show how it doesn’t make a single, clear point. As my colleague Pete likes to say, if my aunt had…

His argument appears to boil down to a belief that pie charts are so simple that it is impossible to deviate from his one-point-per-chart rule. Or put another way: presenters can’t be trusted to follow this rule, so best to take away anything dangerous. This parallels the misplaced anger people have toward PowerPoint. I wrote a post called A Poor Craftsman Blames His Tools.

That question aside, I reject his rejection of bar charts for a number of reasons:

• There is a lot of evidence that bar charts are superior to pie charts even when showing simple data. Bar charts allow for better labeling and show relative size more effectively.
• Pie charts are the most frequently mis-used charts in my experience. There seems to be an irresistible need to craft animated, 3D, shiny pies — all of which adds zero communication value.
• Restricting data presentation to a few chart types limits your ability to communicate. Scatterplots, for example, can carry powerful and clear messages about relationships between variables.
• If the data and message is super simple (as Godin would want), using a chart is likely a waste of pixels. In his pie chart above, the only message he wants to convey is that trolls rule. Why not simply state: “Trolls are the largest segment with 45% share” and leave out the chart.
• Bar charts can show trends and magnitudes simultaneously. I don’t believe this has to be a liability in communication, nor should they always be separated. If I wanted to show that trolls are both the biggest segment and the fastest growing, breaking those facts into separate slides seems more distracting than useful. The points are tightly linked and supportive of each other.

1. The “bar” chart that Godin refers to is actually a column chart. I’ll use the term bar charts to refer to both bar and column charts in this post. However, the distinction between these two chart types is important as each chart is appropriate in different circumstances. For example, when there are a lot of categories, a (true) bar chart allows for much clearer labeling.

2. “Purists” seems to be used to label the objectors as a bunch of academics who don’t appreciate the realities of the business world. If you’re a loyal reader of our blog, you know that is a stick we prefer to use on others.

Many analytical applications fail for a simple reason: they assume users know precisely what they need before they’ve begun the analysis. There are cases where this assumption holds and the user has a specific end-point in mind. But more often, users depend on the tool to track down an answer with only a vague idea of where to start. The exploratory analysis that follows can feel like swimming upstream when the application isn’t designed to facilitate the journey.

The source of this mismatch is partly rooted in the technical perspective of database developers. The simplest path to providing data access is to let users fill out a form to define a SQL query. It is a linear mindset that isn’t well-suited to ambiguous problems.

I’d like to offer a couple examples that illustrates the difference between the common, form-based approach and a more dynamic, interactive approach. Then I’ll explain the implicit assumptions behind the different models and why it matters.

At its heart, Travelocity is a travel analysis tool intended to help you find the best flight (or hotel, car rental, package, etc.) given a complex set of parameters. The relative importance of each of these parameters (departure day/time, return day/time, airports, connections, preferred airlines, price, etc.) is a personal preference… but not one that is explicitly or fully known even to the user. For example, it would be hard for me to say exactly how much more I would pay for a non-stop flight or what is the relative value of a more convenient airport versus a more reliable airline. These preferences are hard to understand prior to seeing specific trade-offs.

Travelocity approaches this complex problem in the way that so many analytical problems do: it asks for all your preferences first then offers a static list results for the specified query.

A few things to note about this search results page:

1. On a busy web page, “Change Your Search” is not emphasized.
2. The “tracker” across the top shows a linear five-step process. The user is expected to flow through this sequence in order.
3. Getting results for a new search takes more than ten seconds.

I’ve been a loyal Travelocity user for years, and I don’t want to imply that this site is poorly designed or difficult to use. The problem is more subtle than that.

By way of comparison, let’s take a look at a more recent entrant to the online travel business, Kayak. This site is designed with a different usage model in mind. Kayak starts by asking for the same information as Travelocity, but the results pages is designed to support further analysis:

The biggest difference is the prominent filtering functionality on the left side of the page. The filters allow users to narrow down their original search without leaving the results page (it takes less than a second to view refreshed results after changing a filter—no “run report” button required). In addition, Kayak places more emphasis on the start-over option. The designers of this site did not assume your first search would be enough to get you to the perfect flight option. Finally, notice the different “views” of the data that are available for a given result set. The views help support different types of decisions based on the same search parameters.

Analytical applications for business have similar underlying structures and usage models. The analysis process in Omniture SiteCatalyst, the leading web analytics platform for large sites, offers a typical example:

This application offers lots of functionality, and it feels like featuring functionality is the primary purpose of the start page. If you want to get to useful data rather than view an advertisement for Omniture products and events, you can start by selecting the “Report Builder:”

Now, it is form-filling time. Like Travelocity, the user is expected to choose the precise parameters before they get to see anything. The resulting report requires a 10 second wait, and the result is static. Any additional filtering will require you to run a new report

Now let’s look at how Google Analytics chooses to structure the user experience:

In contrast to SiteCatalyst, Google Analytics shows you results immediately—no defining or configuring a report before you can get started. Similar to Kayak, the application offers a bunch of options on the report results page to refine parameters (e.g. data ranges, metrics, comparisons).

Travelocity and Omniture make a few assumptions common to analytical applications:

• Users can accurately define their need (i.e. they already know what they are looking for).
• Users can precisely define their need (i.e. they know all the relevant parameters).
• Users’ workflow will follow a linear sequence of events. Going back to the beginning is a failure of the process or user.

More effective analytical applications like Kayak and Google Analytics make different assumptions:

• Users have a general question, but do not necessarily know details about what they're looking for.
• Users need to see results before they can ask better, more detailed questions. These feedback loops provide critical learning.
• Users need to get to data as quickly and easily as possible. A screen without data is delayed progress.
• Different views of the data can provide different insights about results.
• Users want the application to keep up with their trains of thought. Speed and responsiveness matter. Here’s a framework from Jakob Nielsen’s blog about response time:

0.1 second is about the limit for having the user feel that the system is reacting instantaneously, meaning that no special feedback is necessary except to display the result.

1.0 second is about the limit for the user’s flow of thought to stay uninterrupted, even though the user will notice the delay. Normally, no special feedback is necessary during delays of more than 0.1 but less than 1.0 second, but the user does lose the feeling of operating directly on the data.

10 seconds is about the limit for keeping the user’s attention focused on the dialogue. For longer delays, users will want to perform other tasks while waiting for the computer to finish, so they should be given feedback indicating when the computer expects to be done. Feedback during the delay is especially important if the response time is likely to be highly variable, since users will then not know what to expect.

In my experience, making the right assumptions about user behavior makes all the difference between an application people enjoy and depend on and an application people dread using.

Organizations have a personality, and it bleeds into everything from executive reporting to product offerings. A recent Fortune article entitled Microsoft without Gates offers this wonderful tidbit about Steve Ballmer, CEO of Microsoft:

Even though he never was a serious computer programmer, by all accounts Ballmer is just as good at math as Gates is. He lives and breathes data. “Steve has a computer in his head,” says Bob Muglia, a 20-year company man who heads the Server and Tools division. Ballmer expects his subordinates to be adept in math as well. He distributes 11-by-17 sheets filled with numbers detailing the progress of various operations. The numerals are so small that executives use transparent magnifier rulers to see them. But there are never any columns showing percentage changes. Ballmer believes people ought to do that in their heads. It saves space on the paper for more numbers.

Wow. If it is as bad as the author describes, Ballmer has designed the anti-dashboard.

The Presentation Zen blog offers another great example of organization culture as displayed in business artifacts:

Gates here explaining the Live strategy. A lot of images and a lot of text...Good graphic design guides the viewer and has a clear hierarchy or order so that she knows where to look first, second, and so on. What is the communication priority of this visual? It must be the circle of clip art, but that does not help me much.

Does it get more "Zen" than this? "Visual-Zen Master," Steve Jobs, allows the screen to fade completely empty at appropriate, short moments while he tells his story.

Straight from the parallel universe where clever and horrible go together like peanut butter and chocolate comes the following press release:

We are excited to announce the launch of our new community website for Sears and Kmart customers. The service you originally registered with, My SHC Community is now called sk-YOU. The new name represents "Sears and Kmart, building a better relationship with you" and that is of course, part of our vision and mission. It is a growing and personalized online community currently comprised of 40,000 consumers who want to be heard. You can share ideas, opinions and thoughts on a wide variety of topics from travel to kitchen appliances and cell phone service. It enables you to provide feedback and guidance on the offers and shopping experiences that are most important to you.

I can see how this sounded wildly clever in a meeting.

Mash Sears, Kmart, and "you" all together and look what you get. It shows our commitment to the customer and it sounds like "sku".

Bzzzt, horrible. People don't care about stock keeping units—and they certainly don't want to be associated with one. They don't care about clever. Unless you're a financier, there's no reason to associate Sears with Kmart. Branding should help the you understand and remember a product. It's not about how you perceive the customer or about how you perceive an internal initiative. The dash and all caps YOU makes it harder for the customer to remember. But I ramble.

At Juice, our naming bible is available in PDF form from Igor International.

http://www.igorinternational.com/process/naming-guide-product-company-names.php

The central wisdom of this guide—and it's packed full of gems, naming taxonomies by industry, checklists, taglines, case studies—is that names fall into the following categories.

Descriptive names (names that describe what the product or company does) BMW, IBM, AdWords

• Good for a product, easy to remember
• Rough sledding for a company name, as there will be dozens of companies in the field with similar names (unless you have 100 years of meticulous branding like BMW and IBM)

Invented names with latin roots

• Aquilent, Taligent, Acela, Agilent
• "Safe" choices, hard to remember, a blank slate. Generally too clever by half. Hey, did you think it was clever to name a company as a cross between "agile" and "intelligent"? Nobody cares!

Invented names that are fun to say

• Snapple, Oreo, Kodak
• Fun to say, opens the door for lots of positive associations with strong branding

Experiential names (names that describe the experience of the company or product)

• Navigator, Safari, TrailBlazer, Fidelity
• Intuitive but common, doesn't differentiate, a workmanlike approach for a product

Evocative names (names that evoke feelings about the experience you will have with the company—those feelings may even be initially negative)

• Caterpillar, Apple, Amazon, AirPort, Target, Yahoo, Virgin
• Connects emotionally with people because they have lots of previous experience with the word. "Scary" choices that are hard to get a committee to agree to

We often are are asked why we're named "Juice"—Igor is the answer. When we go places, people say "Heeey, Juice guys!"—if you're a client, be aware you're not the first one to use that line. We benefit from every dollar Nantucket Nectars spends on their "Juice Guys" ads and we love it. Every dollar Tropicana spends helps you remember our name. Even OJ Simpson is on our branding team.

If you're naming an internal product, steer toward descriptive names or evocative names. If you're creating a reporting portal, don't be afraid to call it "Report Portal". Or call it "Butterfly" or "Moonbeam." Brighten people's lives by delivering fun, or ease their lives by not making them remember some obscure acronym. Most of all, remember to be a servant of your customers and that clever is not equal to smart.

A couple months ago, we put together a Greasemonkey tool that sucked data out of Google Analytics, and after mining it for trend information, integrated it back into the GA interface. This week's tool combines and extends Google Analytics with data from an outside source.

Here is a quick alpha of our Greasemonkey integration of external data reporting into Google Analytics for Kampyle, a "feedback analytics service." Click on the images to zoom in.

Clicking on the 'Kampylize' tab queries the Kampyle site in real-time to populate the standard GA data table.

Our friends at Kampyle run a service that allows website owners to put a feedback button on individual pages of their website. All information submitted by the user is uploaded to a central Kampyle database that compiles the user feedback with web page url and standard internet statistics such as the name of the browser. Website owners can access a server-end service that consists of a reporting site complete with summary data tables, graphs, and charts.

Since both sites are web-based reporting suites segmented in a similar fashion (individual website, date, web browser, etc.), they integrate together naturally. There is a lot of value in placing related data side by side, allowing users to get a more holistic picture of web site performance. If you have other ideas of data sources that would fit neatly with Google Analytics, let us know and we'll consider building the integration.

If you're interested in technical details, continue to Open Juice to see how this is all accomplished...