Tag Archives: graphics

Infographic on Commuter Volumes Across Different CIties

Screen Shot 2014-09-17 at 3.14.05 PMI saw this article on the Future of Daily Travel, part of the Good Cities Project.  The slightly-larger-than thumbnail sized infographic seemed intriguing.  So I bookmarked the page so I could come back to it later when I had the chance to really dig into it.

So many things going for it!  2.5D isometrics, used as graphic, colors, use of different types of transportation, use of XY space.  I couldn’t wait to get to it.

What a disappointment when I finally had a chance to look into it!

Any graphic has to be relatively easy to interpret.  The best allow you to “start easy” and get some initial “aha!”s, and then dig deeper, like some fine piece of art.  It’s OK to challenge and stretch your audience, but the payoff has to be there.  The opposite of this is to have the audience think, “is that it?”.  Or to make them feel dumb (I discuss this in a separate blog post).

It took me a while to realize that the icons (sprites for boats, cars, London double-decker buses, trains, etc.) had no meaning.  When you have something that take up that much room and color, it has to mean something.  It turns out that the LENGTH of the transportation does matter… BUT the location in XY space does not matter.  Or if it does, I can’t figure out the meaning.

Does the location of the person with the “Cost per Commuter” matter?  I dunno.  The # is nice, but there’s no way to compare it across the cities.  Maybe there is… I can’t tell.

Also, I got lost in the 2 colored lines per city.  Sure, most of the transport requires two line in the real world… is that what we’re trying to show?

There’s a lot of info here, and it still draws you in.  But I found it difficult to filter out what was informative and what was just cute.

What’s the “informative to cute” ratio in your graphics?


Now I See It! (The link between visualization and creativity)

We are visual people.  We recall past events by the mental picture we see (or create).  Words are powerful and can be precise, of course, but visual images can often leave very strong and lasting impressions in our minds.

It’s even in the way we describe our understanding of things.  When we say, “I see it!” to describe an idea, this is more than just saying, “I understand what you are saying.”.  We may actually visualize some embodiment of the idea, like someone using the product, walking through a business process, or people faces lighting up as they get their problems resolved.

To be clear, I am not talking about using images or visuals in our presentations or descriptions.  I am quite familiar with business diagrams, process maps, various charts and graphs and other (good and useful) visual tools we have developed over the decades.  These can be helpful, sometimes very helpful (or sometimes not).  Instead, I am talking about the mental work of visualizing something.

So what is the link between visualization and creativity?  In a recent TechCrunch post from Mark Suster, he claims that “all business success relies on creativity”.  He then goes on to describe how he uses visualization to drive creativity.  It’s a long post with one small NSFW element.  It’s also very personal, from a “what has worked for me” approach from Suster.  Other than that, I think it’s a good article.

It’s good because we don’t think much about creativity.  We label something as “creative” and use that terminology post facto, or in preparation of something we do.  But “being creative in something we are doing now” is something that’s relatively difficult if you are out of practice.  So before you draw, write, sort data, open a powerpoint template, even start an outline, maybe we should take a moment and visualize.  Think of what you are asked to do: re-design a business process, look for trends in data, create the world’s most perfect powerpoint page, prepare a weekly report, snuff out competition.  See if visualizing (as described in the TechCrunch post) helps.  It’s free, and it doesn’t have to take a lot of time.  And it gets easier and more productive over time.

2010: Year in Review (by Search Terms)

Welcome to the Business Analytics for a Complex World!  Or in some cases, welcome back!  Many of you are here because you know me, or I have pointed you to a specific post.  Some are clicking through from LinkedIn, facebook, or other hard links.  But many of you are finding this blog through search engines.  Thanks to some nice tools provided by wordpress and statcounter, I know which terms were used to find this blog in 2010.

This is a word cloud of the most popular words used to find this blog.  This first picture is “mostly unfiltered”.  The size of each word reflects the frequency (the number of times) used in a search engine request that someone used to click through to this blog.  (The colors mean nothing in this graphic.)  The only words not included are common English words, like “a”, “the”, “is”, etc.

What stands out?  The word, “business” is quite popular — but that’s to be expected.  There are smatterings of other words that seem to show up: “bubble”, “analytics”, “chart”, etc.

I find such word clouds to be interesting; however, in most cases, it helps to show more than one word cloud, even if it’s based on the same content.  That’s because, I believe, it’s important to help your audience not feel like an idiot.  Let me explain.  Any new graphic, even if it’s “obvious” takes some time to get used to.  With one example, sometimes it’s hard to get used to the new graph.

So, I generated a second graphic:

The second picture is like the first, but I weighted the terms that were used multiple times.  For example, the exact search term “how much oil is leaking” was by far the most popular (several hundred!), so I increased the size of some of those terms.  This does several things to the picture: it starts to show that there are a few “tiers” or “levels” of popular terms.  This helps us to focus on the most popular terms, such as “leaking”, “bubble”, “analytics”, and so on.

Remember that your audience goes through a few phases when they see something like this.  At first, they have to figure out what it is.  Then they have to figure out what it might mean.  You may be very familiar with the underlying topic and “what the graph” means, but please be courteous to your audience… provide a simple guide or step-through.

You give complex diagrams… a bad name

Death by PowerPoint

It’s not often that a Powerpoint page makes it to the front page of a national newspaper.  The story goes like this.  Leaders were discussing the complexity of American Military strategy in Afghanistan.  Someone prepared a PowerPoint slide after lots of work.  The slide was shown to Gen. Stanley A. McChrystal, who leads the US and NATO forces.  There was a awkward pause, broken by his observation that “when we understand that slide, we’ll have won the war.”  The audience lets out a roar of laughter.  The presenters and diagram-preparers are embarrassed.

Perhaps you’ve seen this page.  It’s become its own meme.  It was ridiculed by many, including Jon Stewart and probably many of your Facebook friends.  It confirms what many of us have seen with our own eyes: there’s a fine line between trying to communicate a memorable story and drop-off-the-cliff absurdity, especially when you are dealing with a complex story.

As a stand-alone, this is a horrible picture.  Many pictures that try to tell a complex story do not do well by sitting by themselves.  Your audience may be familiar with the topics and may even agree with what you are trying to communicate.  But in general, our audience needs a guided tour through something like this.

There’s a certain fluency required in understanding a complex causal loop diagram.  What makes it worse is that without a guided tour, your audience is misled into thinking that since they can read parts of the diagram, they can also read the “whole diagram”.  This is faulty logic.  You don’t want to make the audience seem dumb.  Your audience may be very smart, but when confronted with a diagram like this, they are likely to ask some practical questions.  Where do I start?  What does this mean?  If there are no grips or footings to stand on, your audience will fall, or at least feel like they are slipping.  So they ridicule the diagram, say nothing, or wait until someone says something funny.

So what do you do if you have a complex story to tell that’s best represented by a causal loop diagram?

You should first establish some “basic rules” of how something like this is read.  There are many ways to do this depending on what you are discussing, how many diagrams are in play, your relationship to your audience and a host of other factors.  Sometimes a one-page introduction with a description of what the following diagram shows, along with one loop or a few links does the trick.

You can show things in chunks.  Do you notice the colors?  There are subsections or subsystems.  You can start with an overall subsystem diagram that shows the stakeholders and links… maybe this has only 5-10 actors and only 10-15 links.  All we’re doing is establishing that there are many players and different relationships… not enough detail to be useful, but enough to engage and prepare the discussion.  Then build out details, chunks at a time.

Never forget to explain WHAT you are trying to do with the diagram.  Also notice that it’s “what YOU are trying to do”, and not “what the DIAGRAM is trying to do”.  The diagram does nothing.  Except confuse and amuse.  Human beings (like you or the audience) use, show, debate, decide, tell stories, and understand.

Ecologist Eric Berlow presents a good approach to stuff like this at a TED conference.  He starts with the whole, then gets rid of stuff.  The key is that he gets rid of stuff to fit a certain sub-story.  I call this “collapsing” the diagram… not a great use of the terminology, I admit, but useful in helping the audience feel a bit of relief from the task of dealing with everything.

Bubble Charts — What does the size mean?

A few weeks back, there was a short article in the WSJ about 3M‘s recent acquisitions.  A very prominent bubble chart accompanied the article (in both print and on-line).

I generally like bubble charts,  It’s an easy way to show several dimensions.  Done well, it’s an efficient way to packing lots of useful info into a small space.  We tend to associate the size of the bubble with some magnitude.  In addition, you can use color and place the bubbles on a x-y graph.  With “size”, “x”, “y” and “color”, you get 4 dimensions on a chart.  Not bad… if you can keep it from getting out of hand.

For the article in question, using a bubble chart makes sense.  The punchline is something like, “wow, look at the size of the acquisitions!”  The tagline accompanying the graph is “Three Deals in Two Weeks.”

From Edward Tufte‘s class and personal experience, I have learned that a good graphic “tells a story” instead of only showing numbers.  Graphics should be constructed to make intuitive sense relatively quickly, and if possible, draw the audience in for more exploration.

Which is why I was disappointed in the three green bubbles for this article.  First, the only thing the graphic tells me is that there are three acquisitions that are being graphed.  Are there more acquisitions before this week or perhaps others that may be a candidate?  Also, it might be nice to have some sort of anchor.  For example, if we had a larger circle represent 3M’s annual revenue, or the value of all acquisitions in the five years prior to these three, a competitor… something to compare to.  If the punchline is indeed, “look at the size” or “look how many in such a short time”, maybe we could have placed them on some timeline.  Finally, a close look at the numbers are misleading.  If Arizant is a $810MM acquisition, why is the $943MM Cogent acquisition a smaller bubble?

Reading through the article (and a subsequent email exchange with WSJ) confirms that the Cogent bubble size is based on $430MM, the amount that represents the actual cost, taking into Cogent’s cash reserves.  I am told that the explanation was cut out in the graphic.  Maybe we could have had concentric or internal tangential circles, the larger one showing $810MM, and the smaller one showing $430MM.

Tainted Eggs and Sticky Accelerator Pedals

We’ve had hundreds of millions of eggs recalled in the last several weeks.  That’s more eggs than there are people in the United States.  According to CNN.com, there were 1,953 cases of Salmonella enteritidis reported in a 3-month period.  Salmonella hits you hard. It can leave you sick for a week with cramps, chills, headaches, vomiting, diarrhea.  And that’s if you are healthy.  The elderly, young or folks with weaker immunity can suffer much worse.

1,953 reported cases.  Even after the recall, there could be more cases since the symptoms can hit several days after consumption of tainted eggs.  That’s a lot of sick people.

Or is it?

Another recent recall involved Toyota vehicles and the problem of accelerator pedals.  Cars accelerated out of control.  People died.  There were multiple stories carried by the media in quick succession.  Police were interviewed.  Congressional hearings were held.  A company’s reputation was at stake.

Take a look at the graph published by the Wall Street Journal that shows the “daily number of complaints about vehicle speed and accelerator pedal control” and the dates of some key events.  I am not sure what the “normal average daily complaint rate” should be, but before the warning from Toyota in late September 2009, it seem like there were fewer than 10 complaints per day.  There’s a small spike after the September warning.  The complaints seem to show a temporary peak about 6 weeks after this. In late November, Toyota announces a recall, accompanied by another spike in the days following.  Finally, in late Jan and early February 2010, there are calls to investigate the possibility of faulty electronics.  Around the time regulators officially expand the probe, the complaints spike, reaching a height of over 150 on a single day.

It’s difficult for Toyota to claim that either the drivers were becoming less careful or that the complaints were unjustified.  We have seen such PR blunders before from companies.  When a company makes such a mistake, no amount of science, facts, statistics or promises can fix the PR damage.

Back to the tainted eggs.  According the the CDC, from May to Jul, we would expect about 700 cases of Salmonella instead of 1,953.  Clearly, there is a spike associated with the eggs.  And it’s also likely that not all cases relating to the eggs have been reported.

What do you think?

Can recalls “cause” complaints?  Should companies (and organizations) revise the way recalls are done?  How should we use such statistics in setting the communications or policies regarding recalls?

Be on the Lookout for Sparklines!

What are sparklines?

Although it’s not clear who was the first to use them, Edward Tufte gets the credit for introducing the term “sparklines” to describe these bite-sized graphics.  I suspect that we’ll all see more of them in the future, now that Microsoft Excel 2010 has these built into the graphing features.

You have probably seen these before.  I see them when I check out my stocks at yahoo finance.  Tiny charts under the “Intraday” column tell me if the price is up or down from previous close as well as how they have changed throughout the day.  Since it’s a time chart where the entire x-axis is the trading day, I also get a sense of how much of the trading day is left.  It’s got a lot of data packed into a small space.

The three key elements of a sparkline are:

  • data intensity
    (lots of data instead of having a few data points, even if the data is not labeled)
  • graphical simplicity
    (no extraneous “chartjunk“, text or labels)
  • sized to fit in with exiting text
    (there is no need for the eye to travel far from the text or other information)

That’s it!  It’s wonderfully simple and refreshing.  There’s a lot you give up, of course, since we may want to know the values associated with the axes, or if we see them in small multiples, we will want to know if the scales are comparable across the multiples.  But we gain so much in the simplicity.