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I recently bought an iPhone 4S. I was anxious to try out Siri, the (supposedly) game-changing voice recognition software.

Siri’s great at some things (fetching the weather), reasonably good at others (setting up reminders), and relatively hopeless at many other things (its fallback to anything it doesn’t know how to do is to suggest a web search). I speak fluidly in an unremarkable Midwest American accent, but I still have to take care to enunciate.

There’s a large group of people for whom Siri is almost completely useless, however: many deaf people, and many people with speech impediments. It’s also far less reliable for English speakers with certain accents.

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Jared Loughner
I was stunned when I opened the Wall Street Journal today to find a chart tracking the psychological state of Jared Loughner.

As a reminder: Loughner is accused of killing 6 people in Tucson in January and injuring 13 others, including a member of Congress. He’s schizophrenic, and court battles have raged over whether or not he should be forcibly medicated.

If his mental illness is controlled, he could be competent to stand trial and face the death penalty.

Here’s the chart that accompanied the original article:

Chart showing the moods of Jared Loughner, taken from court documents, since prison officials resumed medicating him

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Sometimes, I see a chart that has too much crammed into it, to the point where it becomes unreadable. I’m reminded of the old military slang, “ten pounds of [stuff] in a five pound bag”, referring to an ugly or unmanageable situation.

Here’s an example of what I mean. This chart appeared in the Wall Street Journal a few weeks ago, in an article about Google’s acquisition of Motorola:

Wall Street Journal chart showing "How the Google-Motorola deal realigns the smartphone universe"

Is it a solar system model? A railroad map?

Here are all the types of data this graphic is trying to show at once:

  • Market capitalization
  • Specific partnerships in the smartphone industry
  • General levels of corporate competition with Google
  • Market share of smartphones for April-June 2011
  • Market share of mobile OS for April-June 2011
  • Market share of set-top boxes for January-December 2010

That’s six separate things, which are being conveyed through bubble sizes, colors, connecting lines, icons, dollar figures, and percentages. Some are in different units, or for different time periods. Much of the data aren’t relevant for certain players (for instance, Cisco doesn’t make smartphones).

The first step in sorting out this mess is to put the data into a table. In some cases, a table may be all you need.

Company Market Cap (in Billions) Relation to Google Partnerships Smartphone Market Share Mobile OS Market Share Set-top Box Market Share
Google $177.81 N/A Motorola, HTC, LG, Samsung 0.0% 43.4% 0.0%
Motorola $11.34 Ally Google 2.4% 0.0% 13.3%
HTC $24.48 Ally/Competitor Google 2.6% 0.0% 0.0%
LG $8.70 Ally/Competitor Google 5.7% 0.0% 0.0%
Samsung $106.98 Ally/Competitor Google 16.3% 0.0% 0.0%
Microsoft $211.93 Competitor Nokia 0.0% 1.6% 0.0%
Nokia $22.62 Competitor Microsoft 22.8% 23.1% 0.0%
Apple $355.03 Competitor None 4.6% 18.2% 0.0%
RIM $13.44 Competitor None 3.0% 11.7% 0.0%
HP $64.40 Competitor None 0.0% 0.2% 0.0%
Cisco $87.52 Competitor None 0.0% 0.0% 7.8%
Others N/A N/A N/A 42.6% 1.8% 78.9%

The first thing that becomes apparent is that there is some extraneous information. The chart is ostensibly about the “smartphone universe”, but HP and Cisco have virtually no smartphone or mobile OS market share. Also, what do “set-top boxes” have to do with smartphones, especially since almost 80% of the market is handled by “Others”?

The chart can be pared down like this:

Company Market Cap (in Billions) Relation to Google Partnerships Smartphone Market Share Mobile OS Market Share
Google $177.81 N/A Motorola, HTC, LG, Samsung 0.0% 43.4%
Motorola $11.34 Ally Google 2.4% 0.0%
HTC $24.48 Ally/Competitor Google 2.6% 0.0%
LG $8.70 Ally/Competitor Google 5.7% 0.0%
Samsung $106.98 Ally/Competitor Google 16.3% 0.0%
Microsoft $211.93 Competitor Nokia 0.0% 1.6%
Nokia $22.62 Competitor Microsoft 22.8% 23.1%
Apple $355.03 Competitor None 4.6% 18.2%
RIM $13.44 Competitor None 3.0% 11.7%
Others N/A N/A N/A 42.6% 2.0%

The article is about the Google-Motorola alliance. So we can add some subtotals to the table:

Company Relation to Google Market Cap (in Billions) Smartphone Market Share Mobile OS Market Share
Google N/A $177.81 0.0% 43.4%
Motorola Ally $11.34 2.4% 0.0%
HTC Ally/Competitor $24.48 2.6% 0.0%
LG Ally/Competitor $8.70 5.7% 0.0%
Samsung Ally/Competitor $106.98 16.3% 0.0%
Google Alliance Subtotal: $329.31 27.0% 43.4%
Microsoft Competitor $211.93 0.0% 1.6%
Nokia Competitor $22.62 22.8% 23.1%
Microsoft Alliance Subtotal: $234.55 22.8% 24.7%
Apple Competitor $355.03 4.6% 18.2%
RIM Competitor $13.44 3.0% 11.7%
Others N/A N/A 47.2% 20.2%

Grouping the data provides some ideas for graphs. We’re looking at three different metrics (market capitalization, smartphone market share, and mobile OS market share). A panel chart would allow us to look at the three different metrics side by side, without trying to squeeze them all into a single complex visualization.

Chart showing market cap, smartphone market share, and mobile OS market share for major players and alliances

From this panel chart, these stories emerge:

  • There are three big players in terms of combined market capitalization. Does RIM have the resources to compete?
  • Google and its allies lead the pack in smartphone market share, but Microsoft’s deal with Nokia makes them a close second. There are many other small players not shown.
  • Google is far ahead in terms of mobile OS market share. Microsoft, Apple, and RIM are fighting to gain ground.

The one thing the chart doesn’t make clear is that the Microsoft/Nokia strength in mobile OS market share is due to Nokia’s Symbian OS, which it is retiring in favor of Windows Phone 7.

The most important thing when designing a visualization is to keep it simple. Bar charts are great for showing one metric; scatterplots can look at the relationship between two metrics. You need to be very careful when trying to show three or more things at once; often, you’ll wind up with a confused mess. When in doubt, show less.

 

In screenwriting and playwriting, the golden rule is “Show, don’t tell.” Dramatize what’s interesting; don’t just talk about it.

For instance, you could have a character walk up to his friends in a coffee shop, say “I just saw a bank get robbed,” and then describe how the robbery went down. But it’s much more exciting to go back in time and show the bank robbery in progress.

The same thing holds true in the computer world. It’s much more meaningful to show your problem to the help desk than to try to explain it to them. Anyone who has gone back and forth over email with the help desk (or tried to describe a problem over the phone) knows what I mean.

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I was traveling in Morocco a few years ago, where you have to haggle for everything. My wife is a tough-as-nails negotiator who was lowballing a pottery merchant. At one point, the man laughed and told us, “You’re trying to buy a camel for the price of a goat!”

We laughed, too, but the expression stuck in our mind. After that, we found ourselves wondering what camels and goats actually cost.

That’s why I got a kick out of the website for Heifer International. The charity works to address world hunger by giving communities livestock and tree seedlings. Here’s the ad for it starring food nerd Alton Brown:

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If there’s a theme to this blog, it’s that numbers have value. Numbers help you do things better. If you measure something (your car’s MPGs, the calories you consume, the dollars you spend on lattes), you can influence it.

And then there are numbers that just exist because, hey, people love to count.

If you missed it, New York Yankees player Derek Jeter notched his 3,000th hit on Saturday in a game against the Tampa Bay Rays. He’s not the first guy to do it; he’s actually the 28th. But it’s still a nice, big, round number that no one on the New York Yankees had ever reached, and it’s an accomplishment that happens infrequently enough that it sets sports fans’ hearts a-flutter.

It’s a number with enough resonance that it deserves to be emblazoned on a t-shirt with no explanation of its meaning.

Image of commerative t-shirt from shop.mlb.com with "3,000" on it.

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A few months ago, I wrote about the MPG gauge in my Honda FIT and Fuelly.com. In the 4+ months since that post, I have made an even greater effort to use the MPG gauge and the Fuelly site to improve my gas economy. (Side bar – Fuelly has since updated their main site to allow additional dashboard customizations, new metrics, and display capabilities; as well as updated their mobile app).

So what are the results of using those BI tools? Well, by utilizing the site, and MPG gauge, my average MPG’s since the post have been 37.9; over 4 mpg higher. Which equates to a savings of about 40 miles per tank, or one gallon of gas (as of this writing, that’s about $3.60). Now – because the car is pretty efficient anyway, $3.60 isn’t a huge deal, but I do fill up about 2x per week (long commutes). So it’s about $7.20 – $8 per week. Which is about a 10% saving in fuel costs.

I’ve also started tracking gas stations I purchase gas from – and while that data set is still small and this inference is probably premature – but Sunoco gas yields the worst MPG’s. While my combined average for Hess/Shell/Mobil (Shell being the clear leader) is about 38, Sunoco is about 34. More to come on that.

What it really boils down to is 3 things:

1. Used correctly, BI helps reduce costs (even in everyday life) – 10% in my case or about $400 annually

2. Charts and graphs alone aren’t enough.  Understanding which metrics to review (real time MPG, supporting data – which gas stations, driving style) is much more important.

3. And probably most important (and definitely the most difficult), is knowing how to act on the insights BI provides (buying gas at the right place, driving efficiently). It’s all well and good to collect, report and look at numbers/charts/pivots, but until you turn them into actions, they don’t provide much value (not changing anything is sometimes the right action too!)

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I’m a member of meetup.com, the slightly square social networking site that promises to connect you–in meatspace!–with people who share your interests. Every so often, Meetup sends me emails inviting me to new groups, and many of them are hysterically off-key. Tuesday, I was invited to join “Asian girls/ladies working/living in the U.S.

For reference purposes, this is me:

Photo of me in my natural habitat, a Dunkin Donuts

I always complain that personal data is scattered willy-nilly across the Internet. So why is Meetup clueless about my gender and ethnicity?

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I’ve seen radar charts advertised as a feature in BI software packages.  But I’ve never figured out how or when to use them. I had always filed them under, “interesting looking, but probably useless.”

I certainly never thought of them as a way to rate and compare rice cookers. But at a home goods retail store in the Akihabara district of Tokyo, that’s exactly how they’re used.

Radar Charts for Rice Cookers. Sorry for the glare, I was taking a picture of a price tag in a retail store... generally not looked upon kindly in any culture.

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I’m used to the government botching things. But the Environmental Protection Agency unveiled their new fuel economy labels last week and, to my surprise, they’re really, really good.

For the near future, most people are going to shop for a plain-old gasoline powered car. So here’s an example of the new label from the EPA’s website, fueleconomy.gov:

New EPA fuel economy label, showing mpg, annual fuel cost, savings, and environmental ratings

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