reporting

The Rise of Analytical Apps — Are We Seeing the Last Days of Dashboards and Reports?

dinosaur_comic.png

66,038,000 years ago, a massive asteroid smashed into the earth in what is now Mexico's Yucatan Peninsula. After this massive collision, it took only 33,000 years before the dinosaurs were entirely extinct — a blink of an eye in terms of the history of the earth.

This asteroid is considered to be the "final blow" after a series of ecosystem changes (other asteroids, volcanos, etc.) created a fragile environment for the poor dinosaurs. The climate changed, the dinosaurs died out, and the mammals took over.

Incumbent solutions for delivering data —dashboard and reporting tools— are facing their own "fragile environment." The big asteroid may not have hit yet, but it is only a matter of time. Here's why.

Exhibit A:

A thoughtful answer from an experienced Tableau user to the question “Why do people still use Tableau?”

We need to consider why (and when) people might stop using Tableau. My opinion is that Tableau has failed to realise two important things about their software and that if another company can solve this problem then Tableau could really lose out:

1. Companies need to create applications, not just reports

Yes, Tableau is interactive but you cannot use Tableau to make applications that write back to a database. It has maps, yes.. But you cannot use Tableau as the basis for an app like you might with MapBox (which has multiple SDKs for different platforms) or Leaflet.js for instance. Tableau is not designed for this, so if you need apps and not reports then it is not for you. You need a developer (or dev team) instead. This is a big gap in the product that other companies are also failing to see.

2. Tableau’s software does not directly generate revenue for (the majority) of their users

For a company to run several copies of Tableau desktop costs several thousand pounds. This is without the additional costs of Tableau Server or end-user licenses that you will need if you want your customers to use your hosted visualisations and dashboards. Any business that chooses to use Tableau to deliver interactive reports to its customers would need to consider passing some of that cost (or all of it) onto its end users. But when we’re talking about interactive reports, not applications, it is hard to justify data reporting as a stand-alone or additional cost.

That’s a real user wondering whether the paradigm of visual analytics tools for analysts, dashboards for executives, and reports delivered to customers and stakeholders is going to hold up for much longer.

Exhibit B:

Analytics vendors and market analysts are using language that leans more toward delivering "apps." 

Alteryx

Alteryx

PwC analytical app marketplace

PwC analytical app marketplace

Infor

Infor

Gartner's IT Glossary

Gartner's IT Glossary

IBM Cognos

IBM Cognos

Is “app” more than a rebranding of a decade of data visualization tools? We think so. Here’s why we see analytical apps are on the way to taking over the BI world:

1. Apps have a purpose. A report or dashboard may carry a title, but it is less common that they have a clear and specific purpose. A well-conceived analytical app knows the problem it is trying to solve and what data is necessary to solve it. In this way they are similar to the apps on your phone — they solve a problem the same way a mapping app shows you how to get to the Chuck E. Cheese and a weather app lets you know if you need to bring an umbrella.

2. Apps make data exploration easy. I’ve spent a decade railing against poorly designed dashboards that put the burden on users to find where to start, how to traverse the data, and what actions to take. Good analytics apps willingly carry that burden. Whether we call it “data storytelling,” narrative flow, or quality user experience design, the app should deliver a useful path through the data to make smart decisions.

3. Apps are collaborative. Most business decisions are made as a group. If that weren’t the case, you’d have a lot fewer meetings on your calendar. Why should data-driven decisions be any different? Historically, reports and dashboards treat data delivery as a broadcast medium — a one-way flow of information to a broad audience. But that’s just the start: the recipients need to explore, understand, and find and share insights. They should bring their own context to a discussion, then decisions should be made. Our belief is that data analysis should be more social than solitary. It is at the heart of the “discussions" feature built into our data storytelling platform, Juicebox.

4. Apps lead to action. "What would you do if you knew that information?” That’s the question we ask again and again in working with companies that want to make data useful. Understanding the connection between data and action creates a higher expectation of your data. Analytical apps connect the dots from data to exploration to insight to action.

5. Apps are personalized and role-specific. The attitude of "one size fits all" is typically applied when creating a dashboard or report, and then it is up to individuals to find their own meaning. Analytical apps strive to deliver the right information for each person. How? By utilizing permissions for a user to only see certain data, automatically saving views of the data, and presenting content relevant to the user’s role.

The mammals took over because conditions changed, and the outdated species — with its size and sharp teeth — couldn’t adapt. Expectations are changing the analytics world. Consumers of data want an experience like they enjoy on their mobile devices. They don’t have the attention to pour over a bulky, unfocused spreadsheet, and they expect the ability collaborate with their remote peers. The climate has changed, and so too must our approach to delivering data.

If you’re still churning out reports, we can help you do better. Or if you’ve constructed a one-page dashboard, we can show you a different approach. Drop us a line at info@juiceanalytics.com or send us a message using the form below.

Building Customer Loyalty through Reporting

Over the past few months we’ve heard numerous stories, like the SEO reporting example highlighted here, where organizations are losing customers or renewals are in jeopardy because of poor reporting.   It hasn’t been specific to one industry either.  We’ve heard this story in healthcare services, software maintenance renewals, ad agencies, etc.

What we’ve noticed as a common thread across these cases is that reporting is viewed as a compliance activity or requirement, not an opportunity to connect with customers.  Reporting or the sharing of insights, is rarely thought of as a means of educating customers, sharing expertise or part of the overall customer experience.   

While there are many opportunities to build customer loyalty using data, i.e. using predictive analytics, to personalize offerings, we’ve created a short, no registration, e-book, Building Customer Loyalty Through Great Reporting, to articulate how a valuable Information Experience TM can enhance the overall customer experience you deliver and round out all your touch points.

The e-book is an easy late night Kindle read or lunch time scan.  Please check it out and let us know what you think about the relationship between reporting and your customer experience.  You can download it by clicking here.  

For a demo of our product, Juicebox, schedule an appointment.

   


Customer Reporting is Different

The biggest difference between customer reporting and internal reporting is understanding responsibility. In customer reporting the burden of understanding is on the author not the reader. When doing internal reporting its reasonable to expect staff, peers and colleagues to invest time, know the business and react to what's been shared.  Unfortunately, the same expectations can’t be placed on customers. If the report isn’t useful they won’t use it nor do they have to.

To ensure customers value your reporting investments and actively use them, report authors should:

  • Be Captivating -  Grab their attention.  There’s only a few moments to engage the reader and a couple of minutes to have them use what you’ve given them.   This is less about styling and more about having intention and clarity.   Squeezing every number they need onto one screen isn’t the answer.  Juice’s Design Principles offer some structure on how to get started on the right track.

  • Guard against misinterpretation - It isn’t good enough to make the information available; use visual cues and functionality to draw the reader to the things that matter most. A few mechanisms that can help are alerts, positioning on the page, and intentional use of color and emphasis.  Also, reveal information as the user expresses interest. In other words, don’t bombard the user with all the information at once. We frequently use levels of increasing detail from (a) key metric to (b) context around the metric to (c)  full breakout detail for the metric. (Data Fluency page 121)

  • Provide a To Do List -   Is it clear for the reader what they should do next? This is the hardest part. Deliver specific metrics where actions are obvious.  For example, customer satisfaction is an often used metric, but the next steps are never clear.  Also, ensure they have the same metric definitions and understand so that actions are clearer.  Then there’s always the obvious.  Tell them.  You’re the expert and want to be a trusted advisor.  Maybe include a tooltip on hover or some dynamic text, e.g. "If X goes below 5% be sure to…” Getting the To Do List right is the hardest, but when it works you’ve achieved report utopia. 

While not as critical, here are a few other important items for customer reporting.   These can certainly throw a monkey wrench into your delivery and customer expectations if not handled correctly.

  • Web-enabled - Be prepared to deal with the cross-browser issue.  Will you restrict which browsers you support or will you ensure to test all reports and modifications across browsers (and screen sizes too).

  • Secure access - This is not only about SSL vs. TLS and data security, but getting users to change passwords, timeouts when idle, as well as displaying only the relevant data to them.   If they already have a company login, is  single sign-on an option?

  • User Management - An ability to monitor how customers are accessing and using their reports  as well as a means of adding and removing users.  Consider giving your sales and support teams access to this information.

  • Licenses - While the organization may have a corporate dashboard or reporting platform, that doesn't mean you have web licenses for all customers.   Have a good sense as to how you plan to share information and what are the licensing implications.   Quite a few folks get surprised by how their software provider handles customer access licensing.

  • Education - A means of getting new or novice users up to speed.  This can also be important for support or sales teams that have to answer customer questions related to reporting that is shared.


This is just the beginning.  If you’d like to learn more or possibly discuss this in more details don’t hesitate to reach out at info@juiceanalytics.com.

Got Data Viz, Y'all?

The Juice Analytics team will be in Nashville July 9 - 11, 2012 and while we can't carry a tune, we'll have with us an interactive demo of our hosted reporting platform to share. Of course, we'll also share the latest around dashboard and interface design, as well as data visualization, in general, along with other stuff that interests you. Reach out if you'd like to meet up while we're in town.

(P.S. If other cities have pent up demand for Juice, reach out and we'll consider a visit.)

Cheers!

Data Empowers Conversations and Rainbows.

Rainbow in Cooperstown - June 3, 2012
Rainbow in Cooperstown - June 3, 2012

A cool afternoon rain was the only thing damper than the spirits of the 12-year-olds who shuffled off the field. With the score still lit up on the wooden scoreboard, the coaches yelled to the boys as they struggled to lift their heads so they might catch a glimpse of a rainbow as it rose from the fence in front of them.

The players of both the winning and losing teams stood there on the wet, steamy grass, frozen in place, in awe of the sight of a rainbow that mystically appeared as if painted on the sky just for them to see. For them, it was an atta boy, pat on the back, a perfect way to wrap up a hard fought double header in which the score had not quite represented the effort that the losing team had given, where the stats failed to tell the tale that brought these two teams together on the hallowed Cooperstown soil.

That’s the thing about numbers. When left to their own devices, they can feel as cold as digits on a lonely scoreboard. They say nothing of the teams who trained for months, played together game after game, relinquished their Saturdays and played nearly perfect seasons just to get to the tournament.

Numbers alone tell us nothing of context. When we have something particularly meaningful to say, images help us share it best. Dashboards and data visualizations bring to life presentations in which we can engage in two-way conversations with our audience making the story around our data more memorable, impactful and effective than any spreadsheet or table of numbers we can put in front of them.

What will your audience remember? The numbers, the final score? Share visually, and they will remember the rainbow and the sunshine that most certainly will follow.

Special thanks to Peter Bielan, my significant other, for inspiring this blog by sharing this photo that he shot during his son's baseball team pilgrimage to Cooperstown, NY this week.  

Being a Data Gourmet

I grew up in a bilingual household where we spoke French and English. Many of us who've been exposed to other languages realize that there are some words that just don't translate well into English. One of the words that got used often in our family was the French word gourmand.  Its closest translation in English is gluttony, but how often does anybody ever say that word?  Probably the simplest way to think of it is the antithesis of gourmet, or even better, someone who prefers quantity over quality.

While there can sometimes be a negative connotation with the phrase, "Il est gourmand," ("He is gourmand"), it can also be just a recognition of someone's preferences.

To this day, even though my French has gotten pretty bad, I still occasionally refer to people as gourmet or gourmand.  It could be when I'm sitting in a restaurant, standing behind them in line at Costco or even hearing about their current data initiative.

What is a data gourmet?

gourmet
gourmet

Data is to an Information Connoisseur as Food is to a Gourmet Chef

Just like a food gourmet, a data gourmet is someone interested in something distinctive, visually appealing and inspired by results or action taken. It isn't about hordes of numbers or metrics. It's about getting the right metrics in place, putting them in the right context and letting them stand out.

Think of the chef who prepares the meal like the one in the picture. He or she not only wants to stimulate your taste buds, but also hopes that their use of color, plating and white space will appeal to you and your visual senses, as well.

What is your data gourmand?

gourmand_550px
gourmand_550px

Prioritize Data Quality Over Data Quantity

So, as I alluded to earlier, not everyone is a gourmet. Many people value quantity over quality. As it relates to data, someone who is a gourmand is probably unsure of what they really want to do with all the data they are requesting. They figure it best to get as much as they can while they can, especially if they aren't sure what they will do with it.

Unfortunately, they probably have never been exposed to a really useful dashboard or visualization. Ultimately, what they think will satiate them and potentially their users is as much data as possible. However, the volume of data would net a number of metrics, charts and gauges, etc. that would be more than they could ever consume.

Working with a Data Gourmand

When you find yourself in a situation where you are working with a data gourmand (and you will - it's just a matter of time), don't look down your well-trained visualization palate at them.  Instead, gently guide them along a path of visual-epicurean transformation.

Most likely, they're going to want to load up their dashboard plate with every bit of data junk they can find.  Start by getting them to see their dashboard as a blank palette to meet specific goals vs. an empty pallet to load up everything they don't need.

As they select different metrics, invest the extra time to train them to carefully select just the right information that provides the balance their data diet needs for a healthy body.  As they make their selections, help them to see that it's okay to have favorite metrics.  As Amanda Cox of the New York Times says, "Data isn't like your kids.  You don't have to pretend to love them equally."

Finally, if you need some help, refresh your skills with the Juice white paper, "A Guide to Creating Dashboards People Love to Use".

Once you've finished, ask yourself these questions.  Does everything in front of your gourmand now have a reason to be there? Did they pause in appreciation or comment that they can't wait to use it?  If so, you may be well on your way to executive data-chef status.

Have a data gourmand/gourmet story of your own?  We'd love to hear about it in the comments below.

Chart Selection, Art and Science

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:

Step 1, All 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.

Step 2, Option 1
Step 2, Option 2

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.

Step 3, Option 1
Step 3, Option 2
Step 3, Option 3

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?

Step 4, Option 1
Step 4, Option 2
Step 4, Option 3

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.

A few final observations:

  • 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.

Five Features of Effective Filters

I’ve developed a bit of a penchant (obsession?) for decomposing the pieces of analytical applications and framing the good and the bad characteristics. So far I’ve taken on treemaps, real-time dashboards, alerts, composite measures, success metrics.

Next up the poor, neglected, and taken-for-granted filter. For such a common and essential component, it seems rare that designers take a moment to consider how to make the best possible filtering mechanism. Here are the five elements I consider critical to a good filter selector along with examples from exemplary interface designs.

  1. Selections
  2. Impact
  3. Context
  4. Persistence
  5. Short-cuts

Selections

Good filters make it obvious to users what has been selected. That might seem like an obvious necessity but consider what happens when you filter in an Excel list. The filter section, even if it is a single item, is immediately hidden from view.

We Feel Fine

Jonathan Harris’ frequently referenced We Feel Fine visualization offers one of my favorite filtering examples. Notice how the selected items are highlighted and the non-selected items are de-emphasized. The bar at the top clearly shows what has been selected, even after the filter selector is "put away."

Impact

The best filtering mechanisms also give instant feedback about the impact of your filters. This can be as simple as a subtle indicator that the filters are being applied. Even better, as demonstrated in the The New York Times’ Rent or Buy site, the graph animates in real-time as filters are applied. This creates a very tangible connection that helps the user understand the impact of the filtering choices.

NY Times Rent or Buy

Context

Filters should provide information around the items being selected. What does it look like? How many are there? Take the simple font selector in Office applications: Isn’t it a no brainer that the names of the options are shown in the actual typeface? Here are a couple other fine examples of context:

Click Shirt

Click shirt is Bret Victor’s brilliant t-shirt design interface. In it, he offers an elegant filter implementation where all the selections show images of what you are about to select.

Elastic Lists

Elastic lists is one of the most innovative approaches to filtering. The height of individual blocks in the selectable stack shows the frequency of the items, an embedded sparkline shows the trend, and brightness indicates "weight of the metadata value compared to the overall distribution" (a bit too ambitious/confusing, in my view).

Persistence

Given the importance of filters to most information applications, it is surprising how often the interface makes them hard to find. As I mentioned in an earlier post, the failure of many analytical and reporting applications is that "they assume users know precisely what they need before they’ve begun the analysis." Filtering shouldn’t be a one shot deal; the functionality should always be accessible.

Kayak, a travel site, integrated the selection filters into the results so users can easily change their trip criteria without having to start a new search.

Kayak

Short-cuts

Finally, filters should make it easy to apply common selections (All, None) or complex sets (My Saved Filters, Northwest Region).

Moodstream by Getty Images recognizes that users aren’t always going to want to configure a bunch of filters individually. The presets wheel solves this problem by offering a series of pre-defined "filter sets."

Moodstream

Finally, I’d be remiss if I didn’t mention the sophisticated and powerful filtering functionality delivered in Tableau. In addition to providing filtering by selecting graphs (i.e. in context filtering), the application allows for multiple selector types, wild-carding, conditional filters, top/bottom filters, and on and on. If you want a comprehensive catalog of potential ways to offer filtering, watch the Filter Data video here.

Why Analytical Applications Fail

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.

Travelocity Results

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:

Kayak Results

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:

Omniture start page

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:”

Omniture form

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:

Google Analtyics dashboard

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.

Microsoft’s Executive Dashboard… Magnifying Glass Required

Organizations have a personality, and it bleeds into everything from executive reporting to product offerings. A recent Fortune article entitled Microsoft without Gates offers this wonderful tidbit about Steve Ballmer, CEO of Microsoft:

Even though he never was a serious computer programmer, by all accounts Ballmer is just as good at math as Gates is. He lives and breathes data. “Steve has a computer in his head,” says Bob Muglia, a 20-year company man who heads the Server and Tools division. Ballmer expects his subordinates to be adept in math as well. He distributes 11-by-17 sheets filled with numbers detailing the progress of various operations. The numerals are so small that executives use transparent magnifier rulers to see them. But there are never any columns showing percentage changes. Ballmer believes people ought to do that in their heads. It saves space on the paper for more numbers.

Wow. If it is as bad as the author describes, Ballmer has designed the anti-dashboard.

The Presentation Zen blog offers another great example of organization culture as displayed in business artifacts:

Gates here explaining the Live strategy. A lot of images and a lot of text...Good graphic design guides the viewer and has a clear hierarchy or order so that she knows where to look first, second, and so on. What is the communication priority of this visual? It must be the circle of clip art, but that does not help me much.

Does it get more "Zen" than this? "Visual-Zen Master," Steve Jobs, allows the screen to fade completely empty at appropriate, short moments while he tells his story.