We were thrilled when we first found out that the Federal IT Dashboard had incorporated our JuiceKit treemap. A year later, the dashboard has been relaunched:

U.S. CIO Vivek Kundra relaunched a IT Dashboard today, and, well, the thing almost makes navigating federal tech spending data fun. Kundra told Politico's Morning Tech that the inspiration for the redesign are online tools people might use to navigate their stock portfolios. The new dashboard offers up more data on spending on more than 7,000 federal IT projects

The first time around, it was awesome to see transparency and visualization brought to the federal government. This time, some of the excitement has worn off and we're going to use it as a case study for "opportunities" to design a better dashboard. There are five areas where it can be significantly improved:

• Message
• Flow
• Charts
• Context
• Design fundamentals

(Not coincidentally, these are the types of areas we cover in our Viva Visualization Tour. Next up, Boston August 25th.)

Part 1: Message

The information designer is responsible for presenting the data in a way that the message is delivered in a clear and understandable way. If the data is left to speak for itself, users can be left confused or frustrated. And in all likelihood they won't to see the full value of the data. That's particularly tough for this Federal IT Dashboard where a huge amount of effort has been put into gathering consistent data across agencies.

The goal of this dashboard is clearly stated on the landing page:

“The purpose of the Dashboard is to provide information on the effectiveness of government IT programs and to support decisions regarding the investment and management of resources.”

They want to answer a couple fundamental questions: Where is money being spent on IT projects? How effective are those projects being managed? Unfortunately the data isn't presented in a way that novice users can quickly answer those questions. Instead the dashboard raises more questions than it answers. For example:

A giant chunk of overall spending goes to the Department of Defense. But how big are these numbers? How are they changing?

Pie charts show that something is mostly green--but not entirely. What does this represent? How should I feel about mostly-green performance?

The three ratings lines are converging around 7.5. What are these numbers and what is driving the trends?

We took the liberty of sketching up a revised dashboard that would more effectively talk to the message of IT program cost and performance. Our dashboard has two primary views, spending and performance.

IT Spending. Where is the money going? Here we have highlighted the top spending agencies and those that are seeing the greatest increases in spending. The line chart on the right shows the trend in spending for any selected Agency. In this way, a user can click on items that they are interested in and immediately see what has happened over time. Labeling also matters. We included titles that would be easy for the first-time user to understand.

Performance. How effectively are the projects being delivered? In this view, we have included something we call a "Spike Chart"; it is a specialized version of a Parallel Coordinates Chart. The Spike Chart allows you to track the performance of the same entities (i.e. Agencies) across multiple performance criteria. The chart will quickly reveal which Agencies bubble to the top (or the bottom) and how they perform across different evaluation criteria.

Within each view, we would let people see the data either by Agency or Investment. In both cases, the resulting visuals are focused on showing which entities are spending/performing the best/worst--and how are the values changing.

There is a ton more functionality embedded in the Kundra's IT Dashboard, but we'd argue for hiding that away until the user has understood the most important and/or interesting information. Then they can drill down into the specifics of a project or organization.

If you're interested in learning more about how to design better dashboards, check out our white paper Designing Dashboards People Love to Use.

A lot of the applications that Juice creates are designed to make information more accessible to people who wouldn't consider themselves to be data experts. They realize the value in the data that they have, and in many cases they have some sort of analytics solution in place, but they know they're not getting as much value from their data as they should.

One of the hurdles we frequently come up against is that people who aren't actively participating in the visualization discussion don't know what's possible. All they've ever seen, in many cases, are the confusing dashboards, charts, and graphs that are all too prevalent from the vendors in our space. You know the ones: a thick layer of technology slathered with some gloss and wiggle, between two slices of "do it yourself".

In many cases, we find ourselves closing this gap by referring to some of the best examples of work out there. As we were thinking about this, the idea to provide a simple walk through of these examples came into being. The result: a 30 day calendar chocked full of some of the best samples of skills enhancing examples we could find.

Each day is a bite sized chunk and takes only a few minutes to watch, read, do, or play. Some of the days are comprised of Juice content, but most days are from other sources that we've found useful.

You can download it to use yourself, or to share with your friends who need to expand their info-viz horizons. Either way, we think it'll get your creative juices flowing.

Listen up, all you Juice fans who live in Atlanta. We've finally nailed down all the details for the get together we announced a few weeks ago. We're calling it the Viva Visualization Tour.

What you'll have when you leave:

• visualization best practices training around information layout and workflow, information visualization, chart selection, and styling
• networking within the Atlanta visualization community
• a full breakfast buffet like only Magianno's can put on
• opportunity to pick the Juice collective for tidbits of vizo-knowledge
• a few pieces of custom designed Juice schwag

What Juice gets out of this:

• socializing with the people in our virtual and physical community who love making visual sense of data
• opportunity to talk about the stuff we're excited about with people who are actually willing to listen (besides our moms)
• a big ol' food bill
• the joy of seeing some of the most respected members of the Atlanta visualization community all in one room

So, if this sounds like great fun to you (and who wouldn't think so), register. We do have limited space, so don't wait.

And, for those of you who aren't in the Atlanta area, we're also planning an August meeting in D.C. and a September meeting in Boston, so keep your eyes open for those (as well as other potential cities).

Ah... Summer in Atlanta. Sunshine, green trees, Peachtree Road Race... and pollution. I love living in Atlanta, I really do, but one thing that most non-Atlantans don't know is that we have a real problem with poor air quality. As someone who really enjoys biking, hiking, and running, I pay a lot of attention to the Georgia Department of Natural Resources Air Quality Index. So far this year we've had a handful of "orange" days, but no "red" or "purple" ones - which sounds anticlimactic, but is a real improvement over a few years ago (we haven't registered a "code purple" day since 2002).

Anyway, as I was checking the air quality forecast this week, it occurred to me that the green/yellow/orange/red/purple/maroon categorization is based on an index. This started some thinking about measures, averages and indexes.

If you've never thought much about indexes, they are calculated by dividing the measured value by a base or expected value and then (usually) multiplying by 100. The result is that the target value is "100".

The great thing about indexes is... they are super easy for casual users to interpret. This is the case because they remove the dependency on the user to understand and keep absolute values in their head. In the case of the Air Quality Index, it's based on the national air quality standard for the pollutant measured. Since air quality in Georgia is primarily composed of 7 measures, it can get pretty confusing if you want to know what's going on. To demonstrate, here's a table of the national air quality standards that the Georgia Department of Natural Resources monitors:

Get the point? As an environmental layperson, it's much easier for me to interpret current measurements if they are expressed with respect to the national index (100) as opposed to ppb, ug/m^3, etc. Most people don't have a clue (nor do they care) about what a mg/m^3 or a ppb is, but they do care about their respiratory health. So, providing a common, simplified measure makes complex data oh so much more accessible to the populous. This is the power of indexes over absolute measures and average values. In Atlanta the use of indexes has unified nearly every Atlantan's practical understanding of air quality.

What it means for you

So, when it comes to creating information applications and dashboards, if you're presenting complex values, consider using indexes to reduce the barriers to entry for non-domain-expert users. Here are a few tips to keep in mind:

• Just as with any new metric, it needs to follow certain guidelines for good metrics.
• Get buy-in with your user group so they don't feel like you're pushing yet another meaningless value down their throat.
• Don't fall into the temptation to swap indexes with historical average values. Averages represent historical measures; indexes represent performance compared to a group.

Oh yeah, lest I forget, there's always a tradeoff. As with most things, when you simplify, you lose some resolution. The draw back is that for those who want to delve deeper into the meaning of the measure, they now have to do some researching (just as I did) to understand how the actual metrics are measured and how the index is calculated. Take this into consideration in your information design and make the absolute measures readily available through alternate views, mouse overs, or similar.

Grocery shopping at a new store is a drag, no matter how thoughtful the supermarket layout or how clear the signage or how wide the aisles. I have a mental model of my local supermarket that makes my trip efficient and helps me avoid that frustrating "double back" to search for the peanut butter.

This thought made me wonder about the importance of familiarity in dashboards. We spend a lot of time at Juice designing intuitive, simple-to-use dashboards. We want to create a logic and cohesiveness that ensures the right things are placed in the right proximity and order; sales leads should connect to prospects in the same way as the peanut butter and jelly is shelved near the bread.

If you are starting from scratch, this internal logic and consistency is paramount. But how about a dashboard that is already familiar to the target audience? Does it makes sense to redesign a dashboard for usability if it is already heavily used and understood?

For many dashboards, the purpose is simply to convey a few key nuggets of information. Without a series of interactions or tasks, the user's only need is to locate and absorb data. In these cases, the measure of success is whether the user can find what they are looking for quickly.

I can appreciate the value of familiarity over usability. When the new Microsoft Office "menu ribbon" was introduced, it was described as a convenience to new users because it displays the most relevant features for any given context. For power-users it broke the experience; all the effort I'd put into memorizing static menus was lost.

For all our concerns about poorly-designed dashboards, it may be familiarity that explains why it can makes sense to keep the status quo.