A few weeks ago, I received an e-mail from the folks at Widgenie asking for a blog review of their newly launched service. Widgenie targets non-technical people looking for an easy way to create and publish charts or data visualizations on the web. As I began looking it over, I wondered whether this problem had been solved elsewhere. It has. I’ve summarized my experiences with five services that can help you go from data table to web chart with ease.

I evaluated the services across three criteria:

1. Data upload: simple process, options for file types, control after loading data, ease of updating data.
2. Chart options: different chart types, control of labels, axes, ordering data, and formatting.
3. Chart design: effective data presentation, absence of chart junk.

A few things I didn’t worry about, but might be worth considering:

• Managing charts that you have created
• Animation or interactivity of charts (usually this is more distracting than useful)
• Easy registration process
• Performance across all the chart types

I gave each service a score of 1 (poor) to 3 (great) for each criteria. Top performers are highlighted in green.

Unlike some of the other solutions, Google Spreadsheet with charts/gadgets isn’t primarily about creating data visualizations. Nevertheless, in a few simple steps, you can upload data, create a good looking chart, and publish for the web.

One key advantage to Google Spreadsheets is that you can easily change and manipulate the data online. The chart output is clean and practical. The chart options are basic, but when you select gadgets rather than charts, you have a lot more visualization options. I was disappointed that it didn’t offer many options for formatting, which is frustrating in an application that is beginning to feel like Excel.

Widgenie is exclusively targeted at the problem of embedding web charts. Overall, I found Widgenie easy to use. The process for loading data and creating “widgets” was straightforward and obvious, if a bit linear (e.g. once you create a widget, you can’t go back to change the chart type). Widgenie offers an above-average number of publishing options, including embedding your charts in iGoogle, Blogger, and Facebook.

Unfortunately, Widgenie offers a lot more sizzle than steak. Its bold marketing statements include:

“the all-powerful data visualizer”

“Take your data and transform it into visual information that can be shared with anyone, anywhere. Your wish is our command!”

“We combine all the power of an enterprise-level business intelligence platform and provide it in a convenient Web 2.0 widget.”

A few of the issues I ran into:

• Relentless use of “animated” 3D charts—where animated means it moves as it is drawn on the screen, not that it shows changes over time in movie format (now that could be powerful).
• The tag cloud visualization, the lone advanced widget option, colors the text rather than sizing it. That isn’t the way tag clouds typically work.
• Finally, as you can see from the image below, I wasn’t able to present my data in order of months (though I loaded it that way). When the data is labeled as text, it automatically gets sorted alphabetically. Changing it to date/time made things even worse.

Editor's note: We can't even embed the widgenie widget in the page because a coding problem with the widget caused all the links on this page to turn red. This problem only occurs with certain browsers.

Swivel isn’t directly aimed at the embedding charts market, but still does a competent job of delivering this capability when you select the “post to blog” option. Swivel offers a beautiful data upload process, but failed in its attempted to auto-define the chart type. Like Widgenie, Swivel struggled with ordering my data the way I wanted it, and somehow lost the order of my original data load.

The charts are generally clean and easy to read, but they’ve made a strange commitment to vertical grid background which doesn’t work for a column chart.

Many Eyes is brought to us by the big-time data visualization thinkers at IBM. They know the right way to present data, and it shows.

Unlike the other services, the only way to get data into Many Eyes is by pasting into a text box. When it comes to visualizing the data, however, Many Eyes offers a myriad of options. Using it to create a simple column chart feels like taking a Ferrari to the grocery store. It is worth checking out the word tree, treemap, network diagram, and proper tag cloud.

Like Swivel, Many Eyes was designed as an online community for the visualization of public data sets, yet it pulls of web chart publishing with ease. The one negative is the "click to interact" feature on the chart.

Zoho Sheet is an online spreadsheet like Google Spreadsheets. I was ready to fall for this one. Like Google Spreadsheet, I simply dropped in my data and selected the create chart button. You’d swear you were in Excel as it walks through the steps to selecting and customizing a chart. But then this came out…

Fortunately, a Zoho'r explained in the comments how to change from this default view.

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.