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!

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.

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 Juice, we work with web analytics APIs large and small, from Google, comScore and Omniture. The Google Analytics API is our favorite. It powers the world's best, most widely deployed analytics site. And it powers Juice products like Concentrate (innovative search analytics) and Vasco de Gapi (a tool for exploring the Google Analytics API).

We were approached by the Google Analytics API team to find ways to explore new ways of looking at data with the API, and we were excited by the possibilities. We've been working on our own visualization framework, JuiceKit, that integrates the power of the Flare Visualization Library with Adobe Flex.

The result is Analytics Visualizations, two visualizations powered by the Google Analytics API that are free to use. You just need a Google account with access to Google Analytics data to explore your own data.

Referrer Flow

Curious about what sites are linking to you and what content is benefitting the most? Referrer Flow answers those question and shows how results change over time. Here is a brief video introduction:

Referrer Flow is a stream of daily treemaps showing pageviews and bounce rates for various groupings of your website's pages. You can group by combinations of page title, referrer and url. Clicking on the treemap will filter all the data by the page, referrer or url that you clicked on. Click again to clear your filter.

Keyword Tree

A list of top keywords isn't enough to really understand how people are searching and finding your site. Keyword Tree visually displays the most frequently used search keywords and how they are used together. Here's a video overview:

You'll see a frequently used search term at the center and the words and phrases that are most often used in combination with that word. Pick a different starting word by typing into the box in the upper right or selecting from the top word across the bottom of the screen. The words are sized by their frequency of use and colored by bounce rate (or % new visitors or average time on site). Roll over a word to see details about that combination of connected words.

Depth and Discovery

In designing these visualizations we focused on the question: how can we let users uncover the unexpected? That means designing targeted visualizations focused on limited well-defined issues. The Referrer Flow monomaniacally focuses on a single question "What pages are people viewing on your site and where are they coming from?" The Keyword Tree is laser-focused on word ordering and what that means for keyword performance.

The Google Analytics reporting tool is a great general-purpose reporting solution. It gives the advanced users everything they need to answer specific questions. However, its generality means it has limited ability to focus on two issues; depth and discovery.

The Google Analytics API is Google's solution to this problem. It's an opportunity both for businesses like ours that can create new ways of analyzing data, and for large sites that can use the API for integration, custom analytics, and more.

Thanks to Nick Mihailovski at Google for his gracious support, help and encouragement and Avinash Kaushik for inspiring this idea.