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

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

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

So, here are the results.

Survey Results

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

Question 1

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

• A one man band
• The Dirty Dozen
• The University of Rhode Island
• Microsoft

Question 2

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

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

Question 3

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

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

Question 4

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

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

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

( You can experiment with it yourself here. )

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

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

Question 5

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

( You can experiment with it yourself here. )

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

Question 6

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

• Staying the same
• Improving

Question 7

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

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

( You can experiment with it yourself here. )

Question 8

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

( You can experiment with it yourself here. )

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

Question 9

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

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

( You can experiment with it yourself here. )

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

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

( You can experiment with it yourself here. )

Question 10

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

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

( You can experiment with it yourself here. )

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

( You can experiment with it yourself here. )

Question 11

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

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

And here's the geographic representation from Many Eyes:

( You can experiment with it yourself here. )

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

Additional Insights

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

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

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

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

You made it to the end!

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

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.

Have you noticed that sometimes it's hard to get your point across? Do you find you're trying to do the right thing with your information, but the organization just won't cooperate?

We think this is happening too often in the companies the Juice Community lives and works in. And we want to do something to change it.

We want to better understand if we're helping you be more effective in your workplace as an information evangelist. To make this possible, we'd like to ask (yea, even beg) you to complete a short 10 question survey about how information presentation is making progress in your company and if you feel alone or supported by the info-viz pundits out there.

But you might ask "what's in it for me?" Well, to begin with, we're going to demonstrate how to summarize qualitative survey information. You'll get some great examples of how to apply non-traditional charting styles to problems within your organization.

However, we can't do it alone; we need you to complete the survey. And if we don't get enough respondents, the results won't lend themselves to what we have planned.

So what are you waiting for? Fill out the survey here and help us help you help us. And what does Gilligan's Island have to do with information presentation? Well, you'll just have to take the survey to find out!

Update: The survey is now officially closed. Thanks to all who responded. We'll have the results out in a few days.

Seems like everyone's trying to understand the future with respect to their customers. We see companies like Google, LinkedIn, and Facebook using predictive analytics to predict user user behavior. Even Stephen Few is giving predictive analytics air time, in classic Fewian cut-right-to-the-core style. So, when we recently had the opportunity to get some predictive analytics insights from one of the industry's thought leaders, we just couldn't pass it up.

Eric Siegel, Ph.D., is an expert in predictive analytics and data mining, and is the Conference Chair at the Predictive Analytics World 2010 conference. This is the premier predictive analytics conference and is the "business-focused event for predictive analytics professionals, managers and commercial practitioners." We asked Eric some questions about the trends he's seeing in this field and wanted to share them with our community.

(Also, don't miss out on the Predictive Analytics World discount code at the end.)

Juice: BI visualization has certainly started to become more mainstream in the past few years. Where is predictive analytics on this maturation/adoption curve?

Eric: Predictive analytics has crossed the chasm and hit mainstream in many sectors, such as credit scoring for financial institutions, response modeling for large direct mail houses, fraud detection, and others. And it is mature in that most large and many mid-tier businesses have employed it in one way or another, if only in a first-stage fashion. All industry verticals are replete with success stories.

Juice: Would you say predictive analytics is used more for understanding or for action?

Eric: I've always had the impression that predictive analytics is employed with action more the central objective than understanding, although understanding is usually also enjoyed, at least as a "side effect." A predictive model's scores drive operational decisions for each customer - that's the action for which it's designed. But by taking a gander into the rules or patterns embedded in the predictive model, strategic insights are often also gained.

On the other hand, the results of the Predictive Analytics Survey put the two benefits as a near tie. This may be because, while fewer projects put insights ahead of action, those with action first also typically include insights as well (the pertinent survey question was a check-all-that-apply).

Juice: What are some of the best examples you've seen of predictive analytics applications that are designed for the "non-analysts"?

Eric: Well, there are two sorts of "action" that can be driven by predictive analytics: decision automation and decision support. In almost all cases of the latter, where staff "in the field" are provided additional information in order to make more informed their decisions - such as customer service agents providing cross-sell offers based in part on system recommendations, or consumer banking branch managers greeting their clients most at risk of churn - it is a non-analyst who "consumes" the predictive scores output by the analytics system.

Juice: How important is real-time to predictive analytics results and resulting actions?

Eric: This depends entirely on the application: what actions or decisions are being driven by predictive scores? So, no knowledge of analytics is required in order to answer this question. The good news is, when the predictive scores output by a predictive model are required in real-time - such as for selecting the optimal ad to serve to a user based on her profile and behavior - predictive models themselves operate quite quickly. They may involve sophisticated math, but they almost never have any iterative/repetitive "loops" in their programming, so they can turn a customer's data into that customer's predictive score very very quickly. It is the derivation of the predictive model in the first place, the application of predictive modeling over historical customer data, that may take hours or days, depending on the analytical method and analyst's process employed; once you have the model, you are ready to fly.

Juice: How does scenario analysis fit into predictive analytics? What are some of the best practices around scenario analysis?

Eric: Predictive analytics generally works at a lower level than standard scenario analysis. It is doing such an analysis at the individual customer level, predicting the probability the customer will exhibit a certain behavior, such as a response, purchase, or defection. So, when considering a prospective predictive analytics initiative, its potential benefits could be put into a scenario analysis. For example, if predictive analytics is to be used to target a retention campaign, its target benefit of decreasing churn by, say, 10% more than current retention efforts could be plugged into a scenario analysis in order to calculate project ROI and gain further traction for the project.

For more information about predictive analytics, see the Predictive Analytics Guide

More information about the upcoming Predictive Analytics World Conference, Feb 16-17 in San Francisco.

(And finally, here's the 15% off discount code for the upcoming conference: JUICE010.)

There are many exclusive conversations going on in the world. Making sense of these conversations can be intriguing however may not be the most productive and satisfying process when you have a specific goal or specific information you would like to retrieve. Many interfaces often speak this 'I'm-a-computer-do-it-my-way" language, without introducing a visual language and workflow that maintains a holistic and ergonomic view of people's goals, strengths, and weaknesses. And the way you build interfaces that engage and speak people rather than speaking computer, isn't putting make-up and jingle bells on yesterday's interface through wiz-bang graphics or merely adding features. Interfaces should maintain clear intentions with a non-exclusive language that stays true to their audience.   

Put these methods into practice:

• know how to dialogue with people, as people and not computer users (Donald Norman also has recently been advocating this. Word usage is important)
• stay abreast of capital (money-making!) design decisions that speak people
• embrace the cross-pollination of ideas. Since people are everywhere, take advantage of learning from new fields you don't frequent. 

In a design nut shell, this is about creating interfaces people love to use. When you see something you love using, seek to understand the fundamental reasons why that is so you can implement these in the future. Often you'll find its the culmination of many design decisions creating a consistent language people understand and love. 

Let's put this into practice by looking at a how potentially foreign information space complimented a workflow for people that is more natural and less exclusive. Hopefully, as we dissect a few notable design decisions, you'll be more comfortable with identifying and repurposing some fundamental principles. Adobe Lightroom 3 Beta is a professional photo editing program I downloaded recently. I noticed the Adobe team touted an improved "Import Dialogue box." Since, generally, all import dialogues seemed to be created equal, I was interested in how they handled this process. 

Old Import Dialogue interface:

Click on the images below to take a look at the redesign and my annotations on it, and then I'll describe how certain annotations support fundamentals of improved information design that can be appropriately applied on future interfaces.

Lightroom 3 Beta Import Dialogue - minimal, basic view: Lightroom 3 Beta Import Dialogue - maximized, advanced view:

Without being exhaustive, let's look at some culminating design decisions associated with general design principles. To clarify, right now we are training an informed design language that will aid us in creating future interfaces with less fluff and more decisions truthful to the content and workflow.

Content promotes context.  The content medium for information / data in this application is photography, and this part of the photography workflow is specific to importing, therefore, a structure is laid intuitively that matches this context.  Concept supported by: flow of the header elements, dimming background, dark / desaturated palette that compliments the overall goal of focusing on altering the pixels of your photography.

Attention balance.  Build a meaningful hierarchal language that emphasizes the content where decisions are made. Hierarchy of text styles or graphics match hierarchy of function or ramifications. Concept supported by: header text specifying the decision is brightest, purposeful icons, inverted preset tab, vignettes and blurring, highlighted mouseovers.

Intention balance. Some interfaces may only need to support casual or advanced use, but this process specific to importing photos now supports both, making it the most beneficial upgrade feature. Interfaces should support peoples' intentions and maintain context while seamlessly transitioning between them.  Concept supported by: expand / contract dialogue arrow, minimal information preview of selected photos, minimized and advanced views.

How is this interface now less exclusive? As people dialogue with this portion of the software, they have fewer hoops to jump through to accomplish the same goals and the new process preserves the context of the content and workflow. Naturally, there are many design fundamentals to build a language around. It can take some work making sense of everyone's tidbits, top-ten lists, quotable quotes, and pattern libraries, but with a little intentional thought we can get more proficient, personally and collectively, at a common language that moves design forward in a methodical, tangible nature.

Start small. Identify design decisions out there grouped in these three fundamentals to get you started and post examples for the Juice community love if you feel so led. It will sharpen you toward purposeful reasoning on the drawing board and during concept presentation time.