is a 30-year-old human being, lives in Chapel Hill, NC, works as Vice President of newfangled.com, reads, writes, draws, and thinks about the future.
• Ask me anything Christopher Butler
The visual concept of Clavilux 2000 is quite simple. For every note played on the keyboard a new visual element appears in form of a stripe, which follows in its dimensions, position and colour the way the particular key was stroke: The length and vertical position show the velocity, the stripe’s width reflects the length of each note. By mapping the color wheel on the circle of fifths, the colours finally give the viewer and listener an impression of the harmonic relations. Notes belonging to one specific tonality always get colors from one specific area of the color wheel. Therefore each key gets it’s own color scheme and “wrong” notes stand out in contrasting colors. The more different tonalities a piece has, the more colorful the visualization will be.
Matt Jones from Berg writes about the idea of ‘humanizing’ data and shows the above example- they have been riffing on the idea of Chernoff faces as a means to differentiating school performance based on a variety of factors. Read the entire post and watch the quick demo he provides. It’s a really wonderful idea!
dataMorphose is an interactive installation which projects data into real space and visualizes it three-dimensionally. Information is represented by spanned and moving sails directly in the room. Thus abstract and virtual data becomes real and tangible. As the user takes new positions and perspectives, he can experience a completely novel and sensual perception of data.
Three spatial displays visualize statistical data, web activities and the current time. The coding and procurement of data is visualized by the tension of the canvas, the pace of movement, the position of the canvas and the change of their shape.
Another day, another set of data… I’ve beeninvestigating what I call “peripheral” data sets in order to get a different perspective on how previously unseen or unmeasured activity affects the overall operation of our company. In my last post, I looked at how our busyness could be represented by the volume of communication over our internal project management system from one month to the next. In looking at that picture, I realized that the volume of activity is much more drastically affected by maintenance work for our clients than by new projects. I classify “maintenance” as any work done for an existing client- it’s a pretty broad spectrum, but since our new project process is so regimented, the split in categories is pretty realistic as far as our company’s day to day experience is concerned. When I noticed that October of 2008 had the highest volume of communication, I wondered what our maintenance sales were that month and how they related to new project sales. Sales data is the easiest information for me to dig up, but I wasn’t interested in the particular sales totals as much as the relationship between the numbers.
This brings me to the graph you see above. As I said, I wasn’t so much interested in how much we sold from one month to the next as I was about the breakdown of sales- how much of it was new business and how much of it was maintenance. So, I determined the percentage of each month’s sales total for the past few years that came from new projects and maintenance. For example, the graph above shows that in October, 2008, 37% of the month’s sales total came from new project sales while 63% came from maintenance. No wonder we had so many posts to our project management system that month! As you can tell from glancing at the graph, this is a relatively infrequent occurrence; more often than not, the new project sales account for the majority of the total. When I first plotted the data, I didn’t add the percentage values because I was more interested in the general relationship, as well as any trends that might be perceivable from visualizing the data. Again, glancing at the graph seems sufficient to conclude that there are no obvious patterns, nor an obvious trend in any direction (i.e. maintenance percentages trending upward or downward).
Averages and Average Averages Then I wondered about averages. The data set covers three years, but it isn’t three full years. Additionally, the current year has a couple of extreme cases (January, in which maintenance accounted for only 19% of the sales total, and September, in which new projects accounted for only 2%), so I decided to look only at 2008’s average.
In the chart above, I plotted out a spectrum displaying only the percentages of total sales accounted for by maintenance sales— the lowest, 24%, came in July of 2008, while the highest, 71%, came in August. Two concurrent months bookending the spectrum seems to clearly show that there isn’t a seasonal correlation. But back to averages, the average maintenance sales percentage for 2008 was 41%. What’s interesting about this is that 8 months out of 12 were less than or equal to the average, leaving only 4 months in 2008 that exceeded it. If I isolate 2007, the average maintenance percentage for the 7 months plotted is 42%. If I isolate 2010, the average maintenance percentage for the 10 months plotted so far is 38%. These numbers are pretty close together. In fact, only 11 months out of the plotted 29 had maintenance percentages that exceeded 41%, which is a “score” of 40%. Maybe there is some significance to 40%…?
Ultimately, I’d love to see the percentage of maintenance account for more consistently higher amount. I think doing more work for fewer clients is to our and our clients’ advantage- it fits in with my motto of what we want to do: Serve fewer clients at a higher level. I believe we’ll get there.
One last thing: The graph above doesn’t show the number of new projects sold on a month to month basis. In 2007, the average was 4.1. In 2008, the average was 4. This year, the average so far is 2.6. To me, that’s the kind of decrease I want to see. It means that we’re selling fewer projects on a monthly basis this year, but at higher costs each (fewer at a higher level). So, all in all, one more piece of the puzzle…
In my last post in what is becoming series on measurement, I started off with my hypothesis that our company is like an ecosystem, “comprised of many areas of unseen activity” in addition to the sort of seen activity you’d expect (sales, individual projects, relationships, etc.). So, in trying to verify my hypothesis, I’ve been gathering data representing all kinds of unseen and unmeasured activity to see how it relates to the big picture as I’ve understood it so far. I started with looking at our blogging activity over the past three years and noticed that the months where we posted less loosely corresponded to what we tend to think of anecdotally as “busy” times for our company. That made me wonder- how else could I measure “busyness”? Looking at sales data wouldn’t quite do it, because those numbers would correspond to the beginning of a project, so the trendline of sales may not match up exactly with that of volume of work over time. However, looking at the volume of communication using our internal project management system might help me discern at trendline for “busyness.”
Unfortunately, there was no simple way to do this…
Google has just released World Bank public data, which allows you to sift through various metrics and create embeddable graphs. This graph compares the number of internet users as percentage of population in the United States vs. Malaysia (where I lived from 2005-2006). It’s interesting that the rate of growth is very similar between the two countries, only delayed by a handful of years in Malaysia.
In conjunction with the relaunch of their website, CNN asked me to examine their web statistics and create a visual record of the site’s last 13 years. We were both interested in telling a larger story about the growth of the Internet and the public’s changing media habits through the lens of such an influential and heavily trafficked site.
The process started by determining what metrics might hold an interesting narrative, and which ones were available over the entire lifespan of the site. CNN was able to provide me with daily page views, the top 20 days for each year and the most popular pages on those days. I was also provided with monthly category views and lists of the nations visiting the site.
The spike chart of average weekly page views forms the centerpiece of the chart. The busiest 10 weeks are called out, and the events associated with the week are highlighted below the x-axis… along with other events of cultural significance or large week-over-week gains. I also tracked the absolute and relative growth of their site categories over time, and highlighted several unique metrics at the top of the chart, including the busiest and slowest days of the year, and the number of countries that visit the site (192 at last count). Finally, to put everything in context, I found milestones in the history of the Internet for each year which I placed along the bottom of the chart to create context for the narrative.
Ultimately, I think the most fascinating story here is the change in our news habits after September 11, 2001. After this day, a new and higher baseline for visits to the site is established, and the inference is that this event really established CNN.com and the greater Internet as a reliable, timely and indispensable source for news.
And lately I have enjoyed a lot Aaron Koblin’s projects, from The Sheep Market to Bicycle for 2,000, where a question is posed and the data to explore that question is generated from scratch by a collective of participants that are never aware of the global picture.
Building new roads and new lanes often just isn’t possible any longer, but building intelligence into the roads and the cars—with roadside sensors, radio frequency tags, and global positioning systems—certainly is.
Also (and this is a digression), check out that snazzy menu of all of IBM’s “A Smarter Planet” topics on the landing page. RAD:
