Juice Analytics

[Insert witty opening here].

You see? In principle, when writing a blog post, I know it draws you (the reader) in to continue reading by starting with a story or something smart or a joke. Don’t overwhelm people right from the get-go. Start with metaphor or phrase that relates to the article.

That introduction relates to what I’m really interested in talking about: principles. We’re launching an exciting new resource today, and it has to do with principles, design principles that is. These resource will remind you to do things like use gradients appropriately or provide instruction. Their goal is to direct your design towards information presentation that focuses on the human element.

Engineers start with technology. MBAs start with funding. Designers start with people. The trick is to get interdisciplinary teams to raise their collective I.Q. by working in the overlap of those three areas. That’s where innovation flourishes.Moggridge

At Juice, we start with people and great consideration for that overlap. Therefore, we’re not only about what information to show but also how to show it. And behind those two basic ideas is an awful lot of thinking > developing > learning > and iteration. Through that process we’ve gathered a (rather long) list of principles that inform our decisions, and we hope it can help you with yours too. Rather than trying to be sure your application supports all principles, approach it more like a stack of flash cards and pull out the relevant ones. With experience, you’ll realize you’re doing these things naturally and understanding the drivers of design thinking is invaluable to introduce objectivity into application design.

There are two parts to this:

1. View the list and explore the content on our Design Principles page.
2. Engage in discussion on our Quora Design Principles Board.

This list will likely grow and shrink over time through the refinement process. The descriptions of each principle definitely will. Our goal is not to be exhaustive, but helpful.

There is a slight catch. So far, we’ve only fully a few of the many principles, which means we have a long way to go. We’re going to embrace process on this one with what might appear to be a (very intentional) turtle’s pace. Still, we’ve made the titles as concretely informative as we could before filling out all their content. Feel free to to run (err walk) right along side us or check in every now and then to evaluate your projects against the list. If you find these helpful or would like to share your experience or opinion on any of them we invite you to engage in the discussion, vote up and down the principles you find more or less useful. Share your insights why. Let us know which one you’d like to see next. Keep us honest, and the visualization community successful. Happy designing.

Do you have your child’s drawing on the wall of your cube, office or maybe at home on the fridge? Can you remember visualizing the world that simply?  When was the last time you looked at anything quite that way? What if you did?

Well, we did just that. And, our effort resulted in a video to share with people about what we do here at Juice.  We hope you like it.

(P.S. Thank your kid for the artwork covering that stain on your wall — and for the great analogy.)

Our founder and CEO, Zach Gemignani, went on the road recently to speak to a group of Voice of the Customer (VOC) professionals and customer intelligence experts at the Allegiance Engage Summit in Park City, Utah.  (Thanks for taking one for the team, Zach.)

Zach’s overall objective was to demonstrate how attendees could gain clear, actionable insights from consumer data.  I’m told that Zach delivered his message about as well as he crafted the data visualizations he used to build it.  In fact, it is rumored that Zach was so engaging that he was compared to none other than Guy Kawasaki, who was also speaking at the Summit.  (Fellow Juicers made mention of head room issues following that comment.)

Allegiance Radio will be airing a podcast of an interview with Zach from the Engage Summit on June 7 at 3:30 p.m. EDT on www.blogtalkradio.com,  Join via VoIP, chat or via phone at (619) 996-1642.  www.blogtalkradio/allegiance/2011/06/07/allegiance-summit-an-interview-with-juice-analytics

You can go to their website anytime after that to review the interview in its entirety.  Following the podcast, we will post a copy of the interview here on our website, as well.

Following is a summary of key content from Zach’s presentation along with resources that may inspire you to get to know your consumer data better to gain insights to move your business forward.  If you have questions or comments, feel free to send them our way.

Know Your Audience

Consider and understand the context of your audience.  ”Actionable” has as much to do with the recipient as the information.  Is it something they have the power or the influence on which to act?

Know Your Tools

Whatever your tool is, it’s worth your while to get good at it.  This saves you time and frustration.

Choose the Right Data

The gourmet values data quality – the right metrics, the right context, presented effectively.  The gourmand, on the other hand, is more interested in quantity. A gourmand believes that more is better, in part because they aren’t sure what they’ll do with the data in the first place.  (See the entire “Data Gourmet” blog at www.juiceanalytics.com/writing/being-a-data-gourmet/

Focusing on just the right data is a concept perhaps best summarized by Amanda Cox. “Data isn’t like your kids.  You don’t have to pretend to love them equally.” – Amanda Cox, New York Times

Choosing the Right Chart

So, how do you choose the right chart?  This is the challenge. Work by taking the most important attributes of your data (based on the question you want to answer) — mapping to the visual elements that most effectively convey that information.

Resources include www.chartchooser.com, www.extremepresentation.com/design/charts and www.juiceanalytics.com/writing/chart-selection-art-and-science/

Tell a Story

You have choices about how data is presented.  Make your choices deliberately.  Consider your audience, their needs and the information.   Then tell a story that clearly resonates with them and compels them — inspires them —  into action.

This summer I had an opportunity to lunch with Fernanda Viegas and Martin Wattenberg, a couple of the brains behind ManyEyes–the brilliant data visualization tool that remains an IBM toy. When I met them, the pair had recently started Flowing Media, a start-up focused on visualization solutions for media data. It was a short-lived venture–Google came calling and Martinanda decided to take their talents to a newly-minted Google “Big Picture” data visualization group.

Before the move, Flowing Media released an open-source desktop visualization tool for event-based data. TimeFlow was created along with Sarah Cohen, a professor and journalist, as a tool for reporters to analyze historical data.

The motivation behind TimeFlow comes from Sarah’s realization that visual analytical tools for reporters are rare. There are good visual presentation tools out there, but those that allow journalists to mull over hundreds and thousands of data points, slicing and dicing the information as they go along are harder to come by. Given this mandate, we set out to rethink timelines, striving to always show as much textual detail about the data as possible (a goal dear to reporters that, interestingly, goes against the visualization impulse to always aggregate).

Here’s what I like most: Flowing Media took a common analysis problem and built a focused solution to solve that specific problem. Most analytical solutions attempt to be all things to all people–and fail in the process. With about 1,000 downloads, I doubt TimeFlow has found its way to all the people who could benefit. In my non-exhaustive tour of the tool, I found that it does a bunch of things well:

• Easy start-up. For a non-technical person, TimeFlow may seem a bit intimidating. It is hosted on Github and downloads as a .jar file. However, I had it up and running seconds on my Mac.
• Uploading data. TimeFlow makes uploading a simple, flat file easy by letting you paste into a text box or selecting an existing CSV file.
• Smart options for data views. It provides a variety of relevant ways to present this timeline based data, including a timeline visualization, calendar, list, table, and bar chart.

• Data summary. An unexpected little feature is a summary of your data file (below). This is the type of useful view that only true data-lovers would think to include.

• In-line data editing. I was pleased to see that you can edit your data as you go. If you see something in a chart that doesn’t make sense, simply right-click to change any of the fields on the fly.

Now that Fernanda and Martin have moved on to Google, we’ll be curious to see what project they take on. It is not hard to imagine an extension of this TimeFlow visualization tool applied to Google news search results.

We’re back from beautiful Seattle having immersed ourselves in the data visualization party known as the 2010 Tableau Customer Conference. It was a wonderfully planned and executed conference during which we met lots of great Tableau users, exchanged data visualization tips, and learned a bunch about what Tableau is up to in version 6.0 (the most highly anticipated enhancement is no doubt the 100x performance improvement of the data engine.) The folks at Tableau are definitely ramping up for some great things and it was a privilege to be part of it.

As most of you know our core business is about building custom information applications to make information accessible to everyone, not just analysts. But we do so love the work they’re doing over at Tableau and keep a close eye on them. As a result, when they extended an offer for us to speak, we were thrilled.

Following our sessions, I was all excited about the reaction we had gotten from our attendees when one of my coworkers pointed out that I had made a terrible mistake: I neglected to give proper credit to Stephen Few. Part of the content that we covered was about how to effectively position elements in an information display to make it easy for the brain to understand what it’s seeing. To do this we discussed “6 Principles of Visual Context”:

• Principle of Proximity – Things that are visually close to each other are related
• Principle of Similarity – Things that look like each other (size, color, shape) are related
• Principle of Enclosure – Things that are enclosed by a shape are related
• Principle of Closure – We see incomplete shapes as complete
• Principle of Continuity – Things that are aligned are related
• Principle of Connection – Things that are visually connected are related

A great set of guidelines that explain so much about why some things make visual sense and others don’t.

However, in the heat of the moment, I neglected to point out that these principles are based on some very nice work Stephen performed a while back. We’re big fans of his and want to make sure we give credit where credit’s due. So, if you you’re not familiar with these principles, or haven’t reviewed them recently, please check them out. Very powerful stuff.

As far as the conference goes, if you’re a Tableau user, you should plan on attending next year. At about 700 attendees, it was nearly twice as big as the 2009 conference, and if the passion and excitement of Christian Chabbot is any indication, next year will be even bigger and even better.

Find and Download Great-Looking Excel and PowerPoint Charts

Chart Chooser is an online tool that answers two questions we commonly get:

1. What type of chart should I use to show my data?
2. How can I make good looking Excel or PowerPoint charts?

Chart Chooser is easy:

1. Check the boxes on the left that best describe your objective
2. Select the chart that you want to use
3. Choose from Excel or PowerPoint downloads to get a formatted chart template

A few notes about Chart Chooser:

• Thanks to Andrew Abela of Extreme Presentations for inspiring Chart Chooser with his “Choosing a Good Chart” post and for working with us to put this tool together.
• We’ve tried to make the charts both Tufte-compliant (i.e. minimal chart-junk) and visually attractive (thanks to Google for the color scheme).
• Feel free to suggest other types of charts that you’d like to see in the Chart Chooser. Send an example to chartchooser@juiceanalytics.com.
• If you’d like a customized version of Chart Chooser for your organization, write us at chartchooser@juiceanalytics.com or call me at 202.251.7750.

Monte Carlo. It’s a car. It’s a song. It’s a casino. It’s a city in Monaco (near France, somewhere). It’s also a method of statistical simulation that is used to better understand the probabilistic distribution of known set of data. Great. So what?

I recently attended the Atlanta session of the FogBugz World Tour to hear from Joel Spolsky about the latest version of his bug tracking software, FogBugz 6.0. Joel Spolsky has been writing about software development, management, business, and the Internet at joelonsoftware.com since 2000. Three books have sprung from content on his site: User Interface Design for Programmers, Joel on Software, and Smart and Gets Things Done. They are good reads for software developers and business folks alike; smart, funny, and focused on the human face of software development.

Before starting his company, Joel was a Program Manager at Microsoft on the Excel team to provide programmability to Excel.

The new release of FogBugz has quite a few nice features to make bug tracking much easier, but I was most intrigued by the new capability called Evidence-Based Scheduling, or EBS for short. This new capability uses a Monte Carlo simulation approach to determine probabilities associated with the expected “ship dates” of the software release project.

The core premise behind this capability is that unlike in the financial realm, in the software world, future results can accurately be based on past performance. FogBugz remembers the original estimate and the actuals for each task for each developer, and for all the projects they’ve been assigned to. Here’s where it gets interesting. Based on this information, FogBugz runs a series of Monte Carlo scenarios where it randomly generates different plausible results for each member of the development team. It then assembles the results to create a distribution for probability of completion for each developer—and more importantly, for the entire project. The result of this analysis is a “Ship Date” probability curve that shows potential ship dates on the X-axis and probabilities of achieving those dates on the Y-axis. Ship Date depends not only on estimates for remaining components, but also on the probability that the individual developers will be able to individually meet their commitments. A steeper curve means the developer estimates are more confident and a flatter curve means estimates are less confident.

The classic software development process involves balancing three things: time, money, and features. Most projects of reasonable duration are not going to be able to effectively add staff mid-stream—at least not to accelerate delivery timeframes. If you assume the primary factor in determining “money” is tied to people, time and features are your only real variables. FogBugz 6.0 lets you experiment with changing these two factors. This is a useful tool. Consider a scenario where the business people come to the development lead because they need a project to be complete by a specific date. This tool gives the project lead enough information to understand the probability of making a specific date. Additionally, it lets you test out what will happen if you remove a particular feature. With this new information, the development lead has the visibility to provide back additional guidance to the project sponsor about the probability of making the requested date, as well as the effect that changing release date and scope have on the probability of on-time delivery.

So how does it know? The ship dates are based on each developer’s history of estimation accuracy supported by developer time sheets. Yes, that’s right. I said time sheets—the bane of all developers. But stick with me, it’s not as bad as you might think. Each developer (or responsible team lead) can turn on a timer that automatically tracks time spent on each of their cases.

Fogbugz plots a linear fit through the data points for each developer and then uses the calculated R² value to determine how consistent the developer’s estimates have historically been. Based on this calculation, probability distributions for remaining tasks for each team member are determined, which leads the ship date probabilities. It’s then easy to see the long pole in the project (Milton Ritchie in this example). This doesn’t necessarily mean the most work, but only reflects the probability of on-time completion—and correspondingly, the most risk to the project.

Anyone who has followed Joel’s writings knows that he is keen on the idea of improving the process of software development. And, we all know that estimating an accurate time to completion, or “ship date,” for any software project is a pain in the rear even after all these years of “practice.” The unique approach that Fog Creek takes is not to predict the specific completion time, but rather to predict the probability of completing the project across a range of dates.

So, project estimating will likely never be a simple turn and churn process. But with this release of FogBugz, Fog Creek Software has shown great outside-the-box-thinking that could significantly improve how we deliver software projects.

“The representation of numbers, as physically measured on the surface of the graphic itself, should be directly proportional to the quantities represented.”

Edward Tufte calls violation of this principle the “Lie Factor”. The implementation of in-cell data bars in Microsoft Excel 2007 is a big offender.

Almost a year ago, I was surprised to discover that the Microsoft Excel 2007 development team didn’t understand what zero means. Their implementation of in-cell data bars showed a bar in a cell, even if the cell had a zero or very low value.

That was in the Excel 2007 Beta. Things haven’t improved in the current version of Excel 2007. The default setting for data bars in Excel 2007 is to scale to bars so that the smallest bar is based on the smallest value in the selected range and the largest bar is based on the largest value. It still appears that the smallest bar will be no smaller than five or ten percent of the width of the cell. Here’s a sample:

So, if you select a range that has values between 600 and 700, the 600 would have a little bitty bar and the 700 would have a full-width bar. Based on the bars, it would look like the 700 is ten to twenty times larger than 600. Outside of Redmond, this is generally regarded as untrue.

What’s more, if you create two sets of data bars side by side, each group of data bars scales itself independently even though they look the same. Take a look at this screenshot:

Notice the top seven cells have data bars that have one set of scaling and the bottom data bars have a different scaling. However, they look identical, and users should generally expect these bars to have the same scale.

Here are the rules:

1. Defaults matter! It doesn’t matter that you can do data bars correctly in Excel. The default should be to do it right and it should be hard to do it wrong.
2. The “right way” to make data bars is to make the length of the data bar directly proportional to the value in the cell. If one cell has a value twice another it should have a bar that is twice as long.
3. Remove the default gradient shading. The gradient makes it hard to tell where the bar ends, obscuring what you’re trying to show.
4. Continuous cells with data bars should all use the same scale. Use different colors to indicate ranges that have different scales.

Excel 2007 supports at least twenty-five different combinations of ways of specifying the length of the data bar.

Exactly one of those ways is correct. Base the shortest bar on the number 0. Base the longest bar on the highest value. Turn off the gradient. If you want to see bars based off percentile or some custom formula, then be explicit. Create a new column, create your formula, create bars on that column.

Please, guys, this isn’t rocket science. This is plain common sense. You would not ship Microsoft Word with a glaring bug in the way text renders. You would not ship Excel with a broken statistical function that people use everyday. Delivering deceitful-by-design infographics betrays your central role in democratizing the analysis of data. Until you fix this, in-cell ASCII art still remains the best way to explore data visually.

A disclosure: We do not currently use Excel 2007 at Juice Analytics. This is not due to a high-minded sense of moral outrage but is merely a reflection of our clients’ environments.

A client recently asked us if we could help him find the five locations that have the most customers nearby. I dug into the annals of our blog to find two tools that together (“wonder twin powers activate… shape of mini-GIS solution”) could solve this problem:

1. Juice geo-coding tool lets you get the precise latitude and longitude for a list of addresses and plot these locations in Google Earth.

2.ZIP code finder lets you enter a US address and returns zip codes within a certain number of miles of that address.

Led by our resident Excel guru David, we combined these features into an Excel tool that lets you answer common location-based questions such as:

• How many libraries are within 10 miles of Worchester, MA?
• Which cities have the most libraries within 10 miles? 20 miles?
• Could I see the library locations on a map, please?

Here’s how it works:

1. Drop your location information into the “Geocode data” tab. We are using the Yahoo geocoding service.

2. Go to the results tab and fill in the table with your selected location addresses and distances. Press “Calculate.”

Download the Excel file here: Juice Distance Tool

We had a couple other features in mind, but thought it would be better to get some reader reaction before we loaded it up.

This tool is released under the MIT license.

Google Analytics has been rebuilt and the result redefines the frontiers of doing analytics on the web. Avinash Kaushik has the definitive early review.

I had the privilege of attending the launch and playing with the early release. Here are a few things I noticed.

• Speak my language: Google has put a lot of effort into replacing specialized terms with everyday ones. This makes the application usable by a broad base of people and is one way to fight GUI Jock-itis.
• Speed kills: The interface is easily reconfigurable and fast. I’ve long argued that interface speed is a substitute for configuration options. I’m curious to play with the tool and get a better sense if this is true.
• Flex rules: Much of the componentry for viewing data in Google Analytics is built in Adobe Flex. This is similar to Google Finance, and not at all like GMail or Google Reader, which use the GWT. We believe this has profound implications for analytical tools on the web and will dig into this in later posts.